Tag: technology

Solving a real problem on quantum computers

This is the final of a series of four articles based on my Jupyter Notebooks exploring quantum computing as a tool for generating random number distributions.

Generating random numbers from a variety of specific probability distributions is interesting, and so is implementing digital computer operations on a quantum computer so that multiple operations are performed simultaneously. However neither of these is enough to justify the hype around quantum computers. Let’s now take a look at an example of something where quantum computers can significantly outperform digital computers. It’s known as Grover’s algorithm, and allows a quantum computer to take a function that might be performed on digital computers, and wraps it in some quantum goodness that quickly solves for it. “Solving for it” in this context means creating a probability distribution that is skewed in a controlled way so that the right answer comes up most often when measuring the qubits at the end.

Also, we will solve for a function of the type where it has one (or just a couple) of solutions, and where the approach to solve for it on a digital computer would be to “brute force” the answer by trying every possible solution to check if it works. On a quantum computer, there are some tricks to try the function fewer times, or even just once, and yet still figure out the solution. This demonstrates quantum advantage for using a quantum computer to solve problems of this type.

Functions of the type we’re interested in, that have one specific solution, exist all over the place. So, this is a potentially highly useful application for quantum computers. For example, a function that checks a possible set of numbers to a particular Sudoku puzzle to verify if it is a correct solution, a function that checks a password to see if it matches an encrypted password entry for a user, or a function that confirms the colour of a particular pixel in an image is correct for a given 3D scene with a particular set of objects and lighting. Many different problems can be rewritten in terms of a function that checks if a given answer is correct.

However, before seeing how to do this on a quantum computer, we need to introduce a couple of new operations.

Z operation

The Z operation works on all pairs of rows in the state vector associated with outcomes where there are different values of only a particular qubit, and flips the sign on the second row of each pair. We can call it the “flip” operation. Let’s have a quick look at an example.

In a two qubit scenario, if we start with an H(0) operator and an H(1) operator, as we did in the first notebook, we have the same value on each row of the state vector. If we then do a Z(0) followed by a Z(1), you can see the signs flip but the numbers otherwise stay the same.

QubitsInitial state vectorH(0)H(1)Z(0)Z(1)
|00>1.01/√21/21/21/2
|01>0.01/√21/2-1/2-1/2
|10>0.00.01/21/2-1/2
|11>0.00.01/2-1/21/2

We saw negative probabilities in the article where we introduced the RY operation, and here they are again. They are the key to how Grover’s algorithm works.

Note that we could have created a Z operation out of the operations that we already have, using a neat trick. The Z operation produces the same result as using the H, X, and H operations in sequence. If you remember, H takes a pair of rows with values a and b, and turns them into (a+b)/√2 and (a–b)/√2. X then swaps these, so performing H again results in the pair of rows becoming 2a/(√2 x √2) and -2b/(√2 x √2) – which is just a and –b. However, Z is a common enough thing to want to do that it is useful to have it as a standalone operation rather than do H, X and H each time.

CCZ operation

Similarly to CCX, the CCZ operation is “doubly constrained”. In this case, it is a “doubly constrained flip” operation. Constrained to just those rows where the two specified qubits are |1>, it flips the sign of the second row of all pairs where the third qubit is the only one changing. Since the second row of these pairs is also the row where the third qubit is |1>, another way to think about this operation is flipping the sign of all rows where the three specified qubits are |1>.

Here’s an example of CCZ in practice:

QubitsInitial state vectorH(0)H(1)H(2)CCZ(0, 1, 2)
|000> (|0>)1.01/√21/21/√81/√8
|001> (|1>)0.01/√21/21/√81/√8
|010> (|2>)0.00.01/21/√81/√8
|011> (|3>)0.00.01/21/√81/√8
|100> (|4>)0.00.00.01/√81/√8
|101> (|5>)0.00.00.01/√81/√8
|110> (|6>)0.00.00.01/√81/√8
|111> (|7>)0.00.00.01/√8-1/√8

Implementing a verifier

The other thing that Grover’s algorithm needs is a function that verifies whether a value is a valid solution to some problem. All it needs to do is take a potential solution, and tell us “yes” or “no”.

We can do this by considering some of the qubits in the outcome to represent a proposed solution, and one other qubit to represent “yes” if it is |1> or “no” if it is |0>. In the example implemented here, qubits 0 and 1 will represent potential solutions, and qubit 2 will represent the result of validating it.

On a digital computer, we would think about this as bits. We would implement some logical operations that take two bits representing potential solutions and return another bit with the validation result. As we saw in the last notebook, we can implement the deterministic operations of a digital computer on a quantum computer by using X, CX, CCX, etc. operations.

Let’s say we want our verifier function to take state vectors where one of |000>, |001>, |010>, or |011> rows has the value 1.0 (100%), and only if it’s the “right” one, will the state vector be changed so that the corresponding row where qubit 2 is |1> becomes 1.0. For example, if |011> is the right solution, this would be implemented simply with the function CCX(0, 1, 2) which would swap the 1.0 value from |011> over to |111>.

Firstly, let’s use X operations to encode the value 3 into the state vector, by putting the value 1.0 in the |011> (|3>) row. (You can grab the complete Python script from here, or just type in the code below.) 

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer
from qiskit.visualization import plot_histogram
from qiskit.quantum_info import Statevector
import matplotlib.pyplot as plt
backend = BasicAer.get_backend('qasm_simulator')

q = QuantumRegister(3)    # We want 3 qubits
algo1 = QuantumCircuit(q) # Construct an algorithm on a quantum computer

# Start in the |3> row
algo1.x(0)
algo1.x(1)

v1 = Statevector(algo1)
print(np.real_if_close(v1.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
0.0 \\
1.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

Now if we perform CCX(0, 1, 2), the values in |011> (|3>) and |111> (|7>) will be swapped, moving the 1.0 value to the final row, where qubit 2 has a value of |1> . Since we know that CCX is constrained to work only on these rows, we know that only where the |011> potential solution is given the 1.0 value will the state vector be changed to have 1.0 on a row where qubit 2 is |1>. The other three potential solutions will result in no change.

# Apply CX operation, constrained to rows where qubit 0 and 1 are |1>, 
# swapping qubit 2's rows
algo1.ccx(0, 1, 2) 
v2 = Statevector(algo1)
print(np.real_if_close(v2.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0 \\
1.0
\end{bmatrix}$$

To create different verifier functions, we can use X operations and specify either qubit 0 or qubit 1. For example, to create a function that will answer “yes” for the potential solution |01> and “no” the other three potential solutions, we simply do X(1) before doing CCX(0, 1, 2). We will also do X(1) again after the CCX to “undo” the first X, and ensure the state vector has 1.0 in the |101> (|5>) row:

QubitsInitial state vectorX(1)CCX(0, 1, 2)X(1)
|000> (|0>)0.00.00.00.0
|001> (|1>)1.00.00.00.0
|010> (|2>)0.00.00.00.0
|011> (|3>)0.01.00.00.0
|100> (|4>)0.00.00.00.0
|101> (|5>)0.00.00.01.0
|110> (|6>)0.00.00.00.0
|111> (|7>)0.00.01.00.0

Implementing this in Qiskit:

# Verifies that a proposed solution is correct only when it is |10>
def add_verify(algo):
    algo.x(1)
    algo.ccx(0, 1, 2)
    algo.x(1)

algo2 = QuantumCircuit(q) # Construct an algorithm on a quantum computer

# Ensure the state vector has 100% in the |001> row
algo2.x(0) 

add_verify(algo2) # Add the verify function to the algorithm
v3 = Statevector(algo2)
print(np.real_if_close(v3.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0 \\
1.0 \\
0.0 \\
0.0
\end{bmatrix}$$

However, we need to modify the verifier function a little before we use it in Grover’s algorithm. We are going to apply the H operation on the result qubit (qubit 2) before running the function, and then again afterwards.

This is a little trick that turns an X operation into a Z operation. So, the CCX operation effectively becomes like a CCZ operation. And yes, the verifier function could have just been written with a CCZ instead of a CCX and we could skip the H operations, but digital computer operations don’t use Z type operations, and this way the algorithm is more general.

# Flips the sign of the row corresponding to the outcome that 
# the verify function would indicate is correct
def add_verify_with_h(algo):
    algo.h(2)
    add_verify(algo)
    algo.h(2)

This version of the function will now flip the sign of the state vector row with the answer, so if the state vector was fully populated with positive values, the solution will be revealed as the one that’s negative. Unfortunately, we can’t stop here with the job done, because in practice we can’t read the state vector out of the quantum computer. All we can do is take measurements of the qubits, and while we can have a negative value in a row of a state vector, we won’t see a negative probability appear in measurements.

Grover’s algorithm is about amplifying the negative row so it will have a higher probability in the measurements.

Grover’s algorithm

Normally the verification function will be quite complicated, and difficult to figure out from just looking at it. Our verification function is simple, but that’s fine for learning how Grover’s algorithm works.

The basic strategy for using Grover’s algorithm is to:

  1. Prepare the state vector so it has the same value on every row, i.e. no row has a zero value.
  2. Apply the verification function, which will flip the sign of the row corresponding to the right answer.
  3. Amplify the negative rows compared to the non-negative rows.

Then we measure the qubits, and the most likely result should be the right answer. For larger numbers of qubits, the steps 2 and 3 will typically be repeated to make the right answer clearer, but we shouldn’t need to do that for our example.

We’ve already defined the verification function, but here’s the state preparation function:

# Creates a uniform probability distribution across the state vector
def add_prepare(algo):
    algo.h(0)
    algo.h(1)
    algo.h(2)

It is just the approach to creating a uniform probability distribution that we saw in the first notebook.

We can see how the verification function just flips the sign of the answer row |101> when given a state vector with 1/√8 values in all of its rows:

algo3 = QuantumCircuit(q) # Construct an algorithm on a quantum computer
add_prepare(algo3)        # Add the operations to prepare the state vector
add_verify_with_h(algo3)  # Add the sign-flipping version of verify 
v4 = Statevector(algo3)
print(np.real_if_close(v4.data))

$$\begin{bmatrix}
\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}}\\
-\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}} \\
\frac{1}{\sqrt{8}}
\end{bmatrix}$$

Step 3 – the amplification function – requires a bit of explanation.

The idea now is to make everything a bit more negative, and because one row is already negative, that row becomes much more negative than the other rows. As the probability that the outcome of a measurement being a given row is equal to the square of the value of that row, it doesn’t matter than the values are negative. The row that is more negative than the other rows will end up becoming a more likely outcome.

The workhorse of the procedure is the H operation. As we discussed in the first notebook, it is like a “half” operation, where it works on all pairs of rows that differ only by a specific qubit, and turns the first of these into the sum of the pairs divided by the root of a half, and the second into the difference of the pairs divided by the root of a half.

There are two observations worth noting here. Firstly, it puts the sums of the pairs into the first row, i.e. the row where the specific qubit has a |0> outcome. Secondly, it is its own inverse, i.e. that it you perform two identical H operations in sequence, the second operation undoes the first one.

Using these two observations, the amplification function applies the H operation for each of the qubits in turn, resulting in all rows being summed into the first row of the state vector, i.e. corresponding to the |000> outcome, although this sum will be divided by the root of 8, which is the result of dividing by √2 in the calculations three times. However, this row will be a large, positive value compared to the others.

Then the amplification function flips the sign on the |000> row, making it a large, negative value. Lastly, the H operation is applied for each qubit in turn, reversing the earlier H operations, but spreading the amount “taken” from the |000> row evenly across all of the rows.

Let’s see it in action. Firstly, let’s apply H for each qubit. We can do this by reusing the prepare function:

add_prepare(algo3)
v5 = Statevector(algo3)
print(np.real_if_close(v5.data))

$$\begin{bmatrix}
3/4 \\
1/4 \\
-1/4 \\
1/4 \\
1/4 \\
-1/4 \\
1/4 \\
-1/4
\end{bmatrix}$$

After the verify function was performed, all rows were 1/√8, except for the solution row which was negative. The sum of all rows is 6/√8 and this value divided by √8 is 3/4, which is what we’ve ended up with in row |000> after the first part of the amplification procedure.

Next we flip the sign on that row so it becomes negative. We have an operation – CCZ – that flips the sign on the |111> row, but not one for the |000> row. Still, we can do this by first using the X operation for each qubit, to reverse the order of the state vector. On a digital computer, to reverse a vector like this, you’d need to perform an operation for each row in the first half of the vector, swapping it with its counterpart row in the second half of the vector. Quantum computers are much more efficient at this.

X(0) swaps groups of rows separated by one row, X(1) swaps groups of rows separated by two rows, and X(2) swaps groups of rows separated by four rows. Once we’ve performed each of these, the state vector has been reversed:

# Reverses the rows of the state vector
def add_reverse(algo):
    algo.x(0)
    algo.x(1)
    algo.x(2)

Using this reverse routine, we can following it by using CCZ to flip the sign on row |111>, then reverse the state vector again to put the state vector back in the original order.

add_reverse(algo3) # Add the operations to reverse the state vector
algo3.ccz(0, 1, 2) # Apply the CCZ operation to flip the sign on row |111>
add_reverse(algo3) # Add the operations to reverse the state vector again
v6 = Statevector(algo3)
print(np.real_if_close(v6.data))

$$\begin{bmatrix}
-3/4 \\
1/4 \\
-1/4 \\
1/4 \\
1/4 \\
-1/4 \\
1/4 \\
-1/4
\end{bmatrix}$$

Note that in flipping the sign on the 3/4 value in row |000>, we have effectively deducted an amount equal to 6/4 (or 3/2) from this row. This reduction will now be spread back across all the rows by using the H operation for each qubit again.

add_prepare(algo3)
v7 = Statevector(algo3)
print(np.real_if_close(v7.data))

$$\begin{bmatrix}
-\frac{1}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}} \\
-\frac{5}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}} \\
-\frac{1}{\sqrt{32}}
\end{bmatrix}$$

The reduction by 3/2 has been divided by √8 again, so the difference between these values and the ones after the verify function is just 3/√32. All of the rows that were 1/√8 are now -1/√32, and the single row that was -1/√8 is now -5/√32. If you’re following along with Python yourself, the output probably doesn’t show it, and just shows -0.17678 (or similar) for all rows except one that shows -0.88388 (or similar).

Now that we’ve worked through the operation of the amplify function, we can define it as a Python function:

# Amplifies the row with a negative value to become more negative
def add_amplify(algo):
    add_prepare(algo)
    add_reverse(algo)
    algo.ccz(0, 1, 2)
    add_reverse(algo)
    add_prepare(algo)

Let’s now see the whole thing in action, and what gets measured at the end. (You can grab a simplified version of the Python script from here that does only this bit, or just type in the code below.)

c = ClassicalRegister(2)     # The solution at the end has only 2 bits
algo4 = QuantumCircuit(q, c) # Construct an algorithm on a quantum computer

add_prepare(algo4)           # Step 1 of Grover's: prepare the state vector
add_verify_with_h(algo4)     # Step 2 of Grover's: flip the solution row 
add_amplify(algo4)           # Step 3 of Grover's: amplify negative rows

algo4.measure(q[0:2], c)     # Measure the two qubits 0 and 1, get some bits
result = execute(algo4, backend, shots=1000).result() # Run this all 1,000 x
plot_histogram(result.get_counts(algo4))              # Show a histogram 
plt.show()

You can go back and set up the verify function differently, and you’ll see that the algorithm will still reveal the correct solution in the measurements.

In this way, the quantum computer hasn’t needed to brute force the answer by trying the verification function over and over again until it finds the answer. The fact that the verification function can be used to make a row in the state vector negative was enough to allow this negative value to be amplified, and set up a probability distribution that makes the answer pop out more often in the measurements.

As I mentioned at the start, it may be that the

add_verify_with_h(algo4)
add_amplify(algo4)

steps need to be repeated as the number of qubits increases. However, it won’t need to be done as frequently as once per qubit, so it will continue to be more efficient than the brute force approach that a digital computer has to use.

In conclusion

We added another two operations to our set, and have seen how to use them on a quantum computer to quickly figure out the solution to the digital computing function that verifies solutions to a problem. Here is the complete set of operations over these four articles:

OperationShort-hand descriptionSpecified byDetailed description
H“half”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, replace the first row value with a new value that is the sum of the original values divided by √2, and the second row value with the difference between the original values divided by √2.
CX“constrained swap”2 qubitsFor all pairs of rows where the first qubit specified is in the |1> state in the outcome, and where otherwise the rows differ only by the value of the second qubit specified, swap the rows in the pair.
RY“relative swap”1 angle and 1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, swap a fraction “f” of the value from the first row to the second, and bring the opposite fraction (i.e. 1-f) from the second row but with the sign flipped, where “f” is specified as the angle 2 x arcsin(√f). If “f” is 1.0, the angle will be 𝜋.
X“swap”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, swap the values in the pair.
CCX“doubly constrained swap”3 qubitsFor all pairs of rows where both the first and second qubit specified are in the |1⟩ state in the outcome, and where otherwise the rows differ only by the value of the third qubit specified, swap the rows in the pair
Z“flip”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, flip the sign on the second row of each pair.
CCZ“doubly constrained flip”3 qubitsFor all pairs of rows where both the first and second qubit specified are in the |1> state in the outcome, and where otherwise the rows differ only by the value of the third qubit specified, flip the sign on the second row of each pair.

That’s all for now. Hope you’ve enjoyed working along with me in seeing how quantum computers can perform computation by changing the probabilities of the different possible outcomes of their qubits, and how often this approach allows quantum computers to solve a problem more efficiently than a digital computer.

Digital operations on quantum computers

This is the third in a series of four articles based on my Jupyter Notebooks exploring quantum computing as a tool for generating random number distributions.

Generating random numbers from a variety of specific probability distributions shows us how the quantum state vector reflects the desired probability distribution, and the previous article showed how a variety of such distributions could be achieved. However, quantum computers can simulate a digital computer also. Even though bits are certain and qubits are uncertain, computing on a digital computer can be thought of like working with a special kind of probability distribution: one where there is a row on the state vector with a 100% probability, and all the rest are zero. This reflects how digital computers are deterministic.

Let’s look at how we might perform digital computing operations on a quantum computer, sticking with high-school level maths. First, we need to introduce some new operations.

X operation

We have already seen the CX, or “constrained swap”, operation. There is a simpler one called the X operation which does a swap within all pairs of rows in the state vector where the only difference is in a specific qubit. So, where the CX operation required specifying two qubits to determine the rows it affects, the X operation requires specifying just one qubit. Where you might think of CX as a “constrained swap”, you can think of X as just a “swap”.

To clarify the X operation, here is an example of how it might be used:

QubitsInitial state vectorX(0)X(1)
|00>1.00.00.0
|01>0.01.00.0
|10>0.00.00.0
|11>0.00.01.0

The first X swaps the first two rows, as these differ only in qubit 0 (the rightmost qubit), and while it also swaps the second two rows, these were the same, so we don’t see a difference there. The second X swaps rows |01> and |11>, as these differ only in qubit 1 (the leftmost qubit), and while it also swaps the remaining two rows, again these were the same value, so we don’t see any difference after the operation.

CCX operation

Now that we know about X and CX, you might be wondering if there are more constraints that can be added to X. Yes, a common operation is a “doubly constrained” version of X, sometimes known as a Toffoli operation.

The CCX operation is constrained to operate only on pairs of rows where two specified qubits are |1>, and it swaps pairs of rows where only a third qubit changes, i.e. a “doubly constrained swap” operation. Here’s what some CCX operations look like on a state vector consisting of three qubits:

QubitsInitial state vectorX(1)CCX(0, 1, 2)X(0)CCX(0, 1, 2)
|000> (|0>)1.00.00.00.00.0
|001> (|1>)0.00.00.00.00.0
|010> (|2>)0.01.01.00.00.0
|011> (|3>)0.00.00.01.00.0
|100> (|4>)0.00.00.00.00.0
|101> (|5>)0.00.00.00.00.0
|110> (|6>)0.00.00.00.00.0
|111> (|7>)0.00.00.00.01.0

Since our examples use Qiskit, qubits are numbered from the right. Qubit 0 is the rightmost one, then qubit 1 is in the middle, and qubit 2 is the leftmost one. In the above table, as it is starting to get long, next to the qubits identifier for the row, I’ve also written the row number in brackets. The qubits identifier is a binary number, and corresponds to a decimal number, which is the row number, e.g. “011” is the binary number for 3, so I’ve written this as |011> (|3>).

In this example, the CCX(0, 1, 2) operation swaps rows where qubits 0 (rightmost) and 1 (middle) are |1>, i.e. those rows ending in |11>: rows |3> and |7>. The first time this operation is performed, both of those rows are 0.0, so it looks like nothing happens, but the second time, we see the effect of the swap performed.

Incrementing a 3-bit number

A very common operation on a digital computer is incrementing a number, or in other words, adding one to it. Incrementing 3 results in 4, incrementing 6 results in 7, and so on.

Each row of the state vector represents a different number, i.e. the decimal number corresponding to the binary number for that arrangement of qubits. For a state vector that represents 3 qubits, row |100> is row |4>, while row |110> is row |6>. Incrementing a number can be thought of as taking a state vector with a specific number encoded in it – the row with 100% probability – and turning it into a state vector with a new number encoded in it, specifically the original number plus one. For example, if we start with a state vector with row |4> with 100% probability, incrementing this would result in a new state vector with row |5> having 100% probability.

To implement this sort of algorithm, where a row has 100% probability, and we make another row 100% probability, we simply need to use variants of the X operation. The X, CX and CCX operations only swap rows around, so will always leave the state vector having a single row with 100% probability. In this case, they can simulate the deterministic operations of a digital computer.

To increment a number encoded on the state vector using variants of the X operation, it is quite straightforward, but we need to think about it in binary notation. If we add one to a number ending in |0>, it becomes |1>. While if we add one to a number ending in |1>, it will become |0> and carry a one to the next place. To achieve this, we can use X to swap from a row |0> to a |1> row, or visa versa, and a CX to manage the carrying of the one. Similarly, we can use a CCX to manage the carrying of the one to the final place.

Implementing in Qiskit

Let’s create this increment operation as a Python function. (You can grab the complete Python script from here, or just type in the code below.)

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit.quantum_info import Statevector

# Add the operations to an algorithm that increments the number 
# encoded on a 3 qubit state vector
def add_increment(algo):
    algo.ccx(0, 1, 2) # Carry the one to qubit 2, when qubits 0 and 1 are |11>
    algo.cx(0, 1)     # Carry the one to qubit 1, when qubit 0 is |1>
    algo.x(0)         # Add one to qubit 0

Now we can test it out. 

q = QuantumRegister(3)    # We want 3 qubits
algo1 = QuantumCircuit(q) # Construct an algorithm on a quantum computer

# Start in the |2> row
algo1.x(1)
v1 = Statevector(algo1)
print(np.real_if_close(v1.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
1.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

# Increment the number encoded in the state vector
add_increment(algo1)
v2 = Statevector(algo1)
print(np.real_if_close(v2.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
0.0 \\
1.0 \\
0.0 \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

# Increment the number once more
add_increment(algo1)
v3 = Statevector(algo1)
print(np.real_if_close(v3.data))

$$\begin{bmatrix}
0.0 \\
0.0 \\
0.0 \\
0.0 \\
1.0 \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

We are successfully incrementing the number encoded in the state vector each time.

Doing multiple increments simultaneously

What if multiple numbers were encoded in the state vector? Actually, the same algorithm will continue to work.

Let’s start by encoding two numbers, so rather than one row having a 100% probability, the state vector will have two rows each with 1/√2 . Remember that we square this to get the probability, which will be 1/2 or 50%.

In Qiskit, we will encode both |0> and |3>, and apply the increment operation.

algo2 = QuantumCircuit(q) # Construct an algorithm on a quantum computer

# Start with |0> and |3> rows having equal probability
algo2.h(2)
v4 = Statevector(algo2)
print(np.real_if_close(v4.data))

$$\begin{bmatrix}
\frac{1}{\sqrt{2}} \\
0.0 \\
0.0 \\
0.0 \\
\frac{1}{\sqrt{2}} \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

# Increment the numbers encoded in the state vector
add_increment(algo2)

v5 = Statevector(algo2)
print(np.real_if_close(v5.data))

$$\begin{bmatrix}
0.0 \\
\frac{1}{\sqrt{2}} \\
0.0 \\
0.0 \\
0.0 \\
\frac{1}{\sqrt{2}} \\
0.0 \\
0.0
\end{bmatrix}$$

Now it has rows |1> and |4> with equal probability. Two increments have been performed simultaneously, without changing the increment operation at all. (In fact, the increment operation can also be thought of as a rotation operation, where the values are rotated through all of the rows of state vector, a row at a time.)

It is this sort of capability that highlights the power of quantum computers to rapidly speed up some types of computation.

In conclusion

We have added another two more operations to our set, and seen how to use them on a quantum computer to perform a traditional digital computer functions (incrementing a number). We’ve also seen how quantum computers can enhance digital functions, like performing multiple increments at once. Here is the set of operations we’ve talked about so far:

OperationShort-hand descriptionSpecified byDetailed description
H“half”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, replace the first row value with a new value that is the sum of the original values divided by √2, and the second row value with the difference between the original values divided by √2.
CX“constrained swap”2 qubitsFor all pairs of rows where the first qubit specified is in the |1> state in the outcome, and where otherwise the rows differ only by the value of the second qubit specified, swap the rows in the pair.
RY“relative swap”1 angle and 1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, swap a fraction “f” of the value from the first row to the second, and bring the opposite fraction (i.e. 1-f) from the second row but with the sign flipped, where “f” is specified as the angle 2 x arcsin(√f). If “f” is 1.0, the angle will be 𝜋.
X“swap”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, swap the values in the pair.
CCX“doubly constrained swap”3 qubitsFor all pairs of rows where both the first and second qubit specified are in the |1⟩ state in the outcome, and where otherwise the rows differ only by the value of the third qubit specific, swap the rows in the pair

The next article will look at a well-known algorithm that performs a task that is complex on a digital computer but is very efficient on a quantum computer.

Further adventures in quantum randomness

This is the second in a series of four articles based on my Jupyter Notebooks exploring quantum computing as a tool for generating random number distributions.

The first article showed how a quantum computer could be programmed to generate a uniform random distribution of two bits using operations on qubits. It was a pretty trivial algorithm, and compared with the complexity of generating pseudo-random numbers on a digital computer, showed the advantage of using quantum computers for this application. However, given that I discussed how quantum computers can manipulate probabilities, it’s natural to consider how other, non-uniform, random number distributions might be calculated using a quantum computer. As with the first article, I’m sticking with high-school level maths.

Bell state

A special type of quantum state is known as the Bell state. There are actually four Bell states, but for simplicity, we’ll just pick one. To put a two qubit quantum computer into a Bell state, we will manipulate it to have the state vector

$$\begin{bmatrix}
\frac{1}{\sqrt{2}} \\
0.0 \\
0.0 \\
\frac{1}{\sqrt{2}}
\end{bmatrix}$$

which means that a measurement will get either the |00> or |11> outcomes with equal probability, but the |01> and |10> outcomes won’t appear at all. Another way to think of this is flipping two coins, and having them always end up heads-heads or tails-tails, but never getting a heads-tails result.

To get this state vector, it’s not enough to use the H operation, but we need something called the CX operation.

CX operation

The CX operation can be thought of as a “constrained swap” operation which affects pairs of rows in the state vector specified by the states of two qubits (rather than specified by just one qubit, like we saw with the H operation). It will cause the values of those pairs of rows to swap, constrained to those pairs of possible outcomes where the first qubit specified is in the |1> state and that otherwise differ only by the value of the second qubit.

For example, if we start with the usual initial state vector for two qubits:

QubitsInitial state vector
|00>1.0
|01>0.0
|10>0.0
|11>0.0

where the |00> outcome has a 100% probability, and now apply the CX operation against the right-most qubit then the left-most qubit, or CX(0,1) to use the Qiskit numbering for qubits, the state vector wouldn’t change at all, since the pair of rows where the right-most qubit is |1> are both the same, i.e. 0.0, so swapping doesn’t change anything.

However, if we firstly use the H operator on rows associated with the right-most qubit, or an H(0) operation, and then perform the same CX(0,1) operation, we get a more interesting result:

QubitsInitial state vectorWorking out H(0)Result of H(0)Working out CX(0,1)Result of CX(0,1)
|00>1.0=(1.0+0.0)/√21/√2unchanged1/√2
|01>0.0=(1.0-0.0)/√21/√2=0.00.0
|10>0.0=(0.0+0.0)/√20.0unchanged0.0
|11>0.0=(0.0-0.0)/√20.0=1/√21/√2

Swapping the rows made a change this time, and we have ended up with the Bell state that we were talking about above.

Implementing this on Qiskit

Now, let’s create a histogram of the results we get from performing this on a (simulated) quantum computer, and check that it does what we expect. We’ll use the same approach with Qiskit as we did last time. (You can grab the complete Python script from here, or just type in the code below.)

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt
backend = BasicAer.get_backend('qasm_simulator')

q = QuantumRegister(2)   # We want to use 2 qubits
algo = QuantumCircuit(q) # Readies us to construct an algorithm to run on the quantum computer

algo.h(0)          # Apply H operation on pairs of rows related to qubit 0
algo.cx(0,1)       # Apply CX operation, constrained where qubit 0 is |1>
algo.measure_all() # Measure the qubits and get some bits

result = execute(algo, backend, shots=1000).result()
plot_histogram(result.get_counts(algo))
plt.show()

Yes, this is the random distribution we were hoping to get. It is just “00” and “11” with no “01” or “10” results.

RY operation

We’ve achieved a non-uniform distribution, but it’s not a very interesting one. It’s a 50-50 outcome, and we could have achieved that with 1 qubit. We didn’t really need 2 qubits. To create more interesting distributions, we will need another operation. Let’s take a look at the RY operation.

RY adjusts the pairs of state vector rows applying to a specified qubit, and adjusts them by a specified “angle”. If the angle is pi (𝜋), which is an amount in radians equivalent to 180 degrees, the adjustment results in a swap of values and flipping the sign of the first value (we’ll come back to this). But the swap is modified relative to the angle, so we can think of it like a “relative swap” operation.

Let’s have a look at at how it would work on the standard initial state vector, with the specific qubit being the right-most one (or, qubit 0), and for some different angles:

QubitsInitial state vectorR(0.0, 0)R(𝜋, 0)R(𝜋, 0) againR(𝜋/2, 0)
|00>1.01.00.0-1.0-1/√2
|01>0.00.01.00.0-1/√2
|10>0.00.00.00.00.0
|11>0.00.00.00.00.0

The first time the RY operation is used, it is given a specified angle of 0.0, and it does absolutely nothing to the state vector. This is correct – with an angle of 0.0, RY will not change anything.

Next, we can see that when the RY(𝜋, 0) operation happens, it swaps the values where the right-most qubit (qubit 0) differ, i.e. the first and second row, and the third and fourth row. In addition, it flips the sign on the first of each pair of rows. The first time RY happens, it simply moves the 100% outcome from |00> to |01>. The second time RY happens, it moves this outcome back to |00> and flips the sign to negative.

What does -100% mean? How can this be a probability? Well, each row of the state vector is a probability amplitude rather than a probability. If a probability amplitude is a real number, i.e. no imaginary component, you can turn it into its corresponding probability by just squaring it. -1.0 x -1.0 is 1.0, so -100% as a probability amplitude is equivalent to a 100% probability. Note that this isn’t just some oddity, but actually part of what makes quantum computers powerful.

The final application of the RY operation in the table is with a specified angle that is 𝜋/2 which corresponds to 90 degrees. It’s mid-way between 0.0 and 𝜋, and produces a result that is also mid-way between the previous results. Where the 0.0 angle didn’t move any of the probability amplitude values between the pairs, and the 𝜋 angle moved all of the probability amplitude values to the alternate row in each pair, the 𝜋/2 angle is halfway between those angles and it moved half the probability amplitude, in the same way the H operator did in the previous notebook.

In fact, we can pick an angle to give to the RY operation that will move a desired fraction of the probability amplitude value between the rows. To swap a fraction “f” of the value from the first row to the second, and bring the opposite fraction (i.e. 1-f) from the second row but with the sign flipped, you use the angle calculated by 2 x arcsin(√f). For our final application of RY above, it had the fraction f=1/2, and it turns out that 2 x arcsin(√(1/2)) = 𝜋/2 which is the angle used in the operation.

We can now use this knowledge to create a range of specific probability distributions for our random bits. The set of operations we have talked about so far – H, CX and RY – should allow us to create any probability distribution. For example, if we want to create a probability distribution where it is equally likely that any of the first three outcomes (|00>, |01>, and |10>) happen and yet the last outcome (|11>) shouldn’t happen, the state vector we’d want to create is:

$$\begin{bmatrix}
\sqrt{\frac{1}{3}} \\
\sqrt{\frac{1}{3}} \\
\sqrt{\frac{1}{3}} \\
0.0
\end{bmatrix}$$

A way to get this is to recognise that if we look at the state vector as two pairs of rows, the first pair of outcomes are twice as likely in total as the second pair of outcomes. We can use the RY operation to swap (the square root) of a third of the overall probability to the second pair. We can then use a sequence of H, RY, CX and RY operations to spread the probabilities within each pair. This looks like:

QubitsInitial state vectorRY(2 x arcsin(√(1/3)), 1)H(0)RY(𝜋/4, 0)CX(1, 0)RY(-𝜋/4, 0)
|00>1.0√(2/3)√(1/3)0.31250.3125√(1/3)
|01>0.00.0√(1/3)0.75430.7543√(1/3)
|10>0.0√(1/3)√(1/6)0.22090.5334√(1/3)
|11>0.00.0√(1/6)0.53340.22090.0

You can see here that after the H(0) operation, the first two rows have the values we want, but the final two rows had the desired values before the H(0). The operations following the H(0) have the effect of undoing the H(0) operation on the final two rows but leaving the first two rows alone. Note that the final two RY operations are opposite signs to each other, so they should cancel each other out, but a CX(1,0) operation has been done in the middle. This CX operation, in swapping the final two rows, has the effect of making it as if the first of the final two RY operations was also a negative angle for those rows, so instead of cancelling out (like happened on the first two rows), the two RY operations on those rows add together as if it was an RY operation of -𝜋/2. As we saw above, an RY operation with the angle 𝜋/2 is similar to an H operation, and with the negative angle, the RY operation acts to reverse the H.

Don’t worry if you didn’t fully follow that. This sort of procedure is called “amplitude embedding” or “state preparation”, and there are various algorithms to do this, many of which get quite esoteric. The above procedure was inspired by a paper by Mottonen, Vartiainen, Bergholm, and Salomaa. The main thing to note is that quantum computers allow arbitrary non-uniform distributions to be constructed.

Implementing this on Qiskit

Let’s test the above procedure and see if it does what we expect. (You can grab the complete Python script from here, or just type in the code below.)

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt
backend = BasicAer.get_backend('qasm_simulator')

q = QuantumRegister(2)   # We want to use 2 qubits

angle1 = 2 * np.arcsin(np.sqrt(1.0/3.0))
angle2 = np.pi / 4

algo = QuantumCircuit(q) # Readies us to construct an algorithm to run on the quantum computer

algo.ry(angle1, 1)       # Apply RY operation to swap 1/3 of qubit 1's value 
algo.h(0)                # Apply H operation on pairs of rows related to qubit 0
algo.ry(angle2, 0)       # Apply RY operation to perform a half of H on qubit 0
algo.cx(1,0)             # Apply CX operation, constrained to where qubit 1=|1>
algo.ry(-angle2, 0)      # Apply RY operation to undoing half of H on qubit 0

algo.measure_all()       # Measure the qubits and get some bits

result = execute(algo, backend, shots=1000).result()
plot_histogram(result.get_counts(algo))              
plt.show()

This is exactly what we were hoping to see. It is “00”, “01” and “10” split three ways, and with no “11” results.

In conclusion

We have added two more operations to our set, and seen how to use them on a quantum computer to create a variety of random distributions, such as the Bell state:

OperationShort-hand descriptionSpecified byDetailed description
H“half”1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, replace the first row value with a new value that is the sum of the original values divided by √2, and the second row value with the difference between the original values divided by √2.
CX“constrained swap”2 qubitsFor all pairs of rows where the first qubit specified is in the |1> state in the outcome, and where otherwise the rows differ only by the value of the second qubit specified, swap the rows in the pair.
RY“relative swap”1 angle and 1 qubitFor all pairs of rows that differ only by the value of a specific qubit in the outcome, swap a fraction “f” of the value from the first row to the second, and bring the opposite fraction (i.e. 1-f) from the second row but with the sign flipped, where “f” is specified as the angle 2 x arcsin(√f). If “f” is 1.0, the angle will be 𝜋.

The next article will look at how to implement digital computing functions through operations on the state vector.

A Quantum Computer is a random number generator

This is the first in a series of four articles based on my Jupyter Notebooks exploring quantum computing as a tool for generating random number distributions.

Many of the introductory quantum computing articles and courses out there are not quite right. They either quickly head deep into details that require a University-level physics or mathematics background, or sit at a high level based on analogies that are out of step with how quantum computers actually work. I want to try something different, and introduce some useful quantum computing algorithms using high-school level maths. I will avoid much (but maybe not all) of the jargon, and show how the algorithms can be implemented on the commonly available Qiskit platform.

In my earlier article on quantum computing, I introduced an analogy to describe quantum computers, which are based on qubits rather than bits. The analogy was of a coin-flipping robot arm that is flipping a coin that lands on a table. A qubit is like the coin when it is in the air, and a bit is like the coin when it has ended up on the table. When it’s in the air, the coin is in a kind of probabilistic state where it may end up heads or tails, but once it’s on the table, it’s in a certain state where it is definitely one of either heads or tails. Quantum computers work in the realm of probabilities, and can manipulate the coin while it’s still spinning in the air. The spinning coin in the air is the qubit. But at some point it will land on the table and be measured as either heads or tails. At that point it becomes a bit.

To write about quantum states, a notation is used where the name of the state is put between a vertical bar and an angle bracket. Just like a single bit can be in the “0” state or the “1” state, for a single qubit, we might say it can be in the |0> state or the |1> state. Our hypothetical robot-arm is well-calibrated, so it consistently flips a coin that lands with the same side facing up, and the resulting coin is like having a qubit in one of these states. The coin is in a probabilistic state, but the probability of it having a particular result is 100%. Similarly, if a qubit is in the |0> state, when it is measured, you will get a “0” result 100% of the time.

However, a quantum computer can manipulate the probabilities of the qubit, so even if the qubit started in the |0> state, after manipulation it enters a new state where if the qubit is measured, it will get “0” outcome 50% of the time (and “1” outcome the other 50% of the time, of course). This is done using a Hadamard operation, usually just written as H. We will use this operation to create truly random numbers.

Creating truly random numbers

Mostly when you have a computer give you a random number, such as using the RAND function in Microsoft Excel or when you’re playing a computer game and the enemy does something unexpected, the computer is actually producing a pseudo-random number. If you could create a perfect snapshot of everything in your computer, then get it to do something “random”, and return to that snapshot again, it will do exactly the same random thing each time. So, it’s not actually random, but it looks random unless you peer too closely.

For most applications, that’s fine. But if you are doing cryptography, having truly random numbers is important. You want to generate a secret key that no-one else can guess. Ideally, even if someone could take a snapshot of your computer, they still couldn’t predict what secret key is generated. There are special hardware random number generators that can create truly random numbers (Cloudflare uses lava lamps!), and quantum computers create truly random numbers too.

Let’s say we are going to generate a 2 bit random number. We’ll use 2 qubits, and the starting state of the qubits will be |00>, which means the outcome of measuring them both as “0” is 100%. We’ll use a quantum computer to manipulate the qubits so that all four possible outcomes |00>, |01>, |10>, or |11> are equally likely. Then when the qubits are measured, we will have some truly random bits.

We can write the four possibilities as a vector, with each row consisting of the probability of that outcome. Quantum computers perform their calculations using complex numbers rather than real numbers, and this is because complex numbers are needed to accurately describe how things work at the quantum level. We can simplify things, and just use real numbers, but we will need to calculate probabilities by squaring the values in each row of the vector.

We call this vector the quantum state vector (or just state vector), and it starts with being

$$\begin{bmatrix}
1.0 \\
0.0 \\
0.0 \\
0.0
\end{bmatrix}$$

Each row of the state vector corresponds to a different outcome, with the outcomes for two qubits being |00>, |01>, |10>, and |11> as we go down the vector. So this state vector represents a 100% probability of getting the |00> outcome.

We want each outcome to have a 25% probability, so we want to change the state vector to be:

$$\begin{bmatrix}
\frac{1}{2} \\
\frac{1}{2} \\
\frac{1}{2} \\
\frac{1}{2}
\end{bmatrix}$$

Of course, when you square 1/2, you get 1/4, or 25%.

The H operation is a standard operation on quantum computers, and works on all pairs of rows of the quantum state vector where that outcome differs only by the value of a specific qubit, e.g. where one outcome has the |0> for that qubit and the other row has |1>. For each pair, it turns the first value into a new value that is the sum of the original values divided by \(\sqrt{2}\), and the second value into the difference between the original values divided by \(\sqrt{2}\). While it is a division by \(\sqrt{2}\) rather than a division by 2, you can think of H like a “half” operation, where it calculates half the sum and half the difference and is scaled by a normalisation constant so that when the values are squared, the probabilities add up to 1.0. Written out mathematically, if the first row value is \(a\) and the second row value is \(b\), the first row value becomes \(\frac{a+b}{\sqrt{2}}\) and the second row value becomes \(\frac{a-b}{\sqrt{2}}\).

To get the desired final state vector from the initial state vector, we can apply H first to the pair of rows associated with a difference in the right-most qubit, then apply H to the pair of rows associated with a difference in the left-most qubit. Here’s how it would go:

QubitsInitial state vectorWorking out first HResult of first HWorking out second HResult of second H
|00>1.0=\(\frac{1.0+0.0}{\sqrt{2}}\)\(\frac{1}{\sqrt{2}}\)=\(\frac{\frac{1}{\sqrt{2}}+0.0}{\sqrt{2}}\)\(\frac{1}{2}\)
|01>0.0=\(\frac{1.0-0.0}{\sqrt{2}}\)\(\frac{1}{\sqrt{2}}\)=\(\frac{\frac{1}{\sqrt{2}}-0.0}{\sqrt{2}}\)\(\frac{1}{2}\)
|10>0.0=\(\frac{0.0+0.0}{\sqrt{2}}\)0.0=\(\frac{\frac{1}{\sqrt{2}}+0.0}{\sqrt{2}}\)\(\frac{1}{2}\)
|11>0.0=\(\frac{0.0-0.0}{\sqrt{2}}\)0.0=\(\frac{\frac{1}{\sqrt{2}}-0.0}{\sqrt{2}}\)\(\frac{1}{2}\)

Now that we’ve covered the process, let’s look at how this would be written programmatically using the Qiskit library from IBM.

Implementing this on Qiskit

We’re going to set up a (simulated) quantum computer with 2 qubits. (You can grab the complete Python script from here, or just type in the code below.)

import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute, BasicAer
from qiskit.quantum_info import Statevector
from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt
backend = BasicAer.get_backend('qasm_simulator')

q = QuantumRegister(2)   # We want to use 2 qubits
algo = QuantumCircuit(q) # Readies us to construct an algorithm to run on the quantum computer

By convention, all qubits begin in the lowest-energy state, so without doing anything, the qubits of our quantum computer should be set to |00>. We can check the state vector and see.

v1 = Statevector(algo)
print(np.real_if_close(v1.data))

Which will print “[1. 0. 0. 0.]” and shows the |00> row is 1.0 and the other possible outcomes are 0.0.

Qiskit numbers the right-most qubit as qubit 0, and the one to the left of it as qubit 1, with the next as qubit 2, and so on. You may have come across this as being called little-endian. Let’s start by using the H operator on pairs of rows associated with the |0> and |1> outcomes on qubit 0 (the right-most qubit).

algo.h(0)  # Apply H operation on pairs of rows related to qubit 0
v2 = Statevector(algo)
print(np.real_if_close(v2.data))

Which will print “[0.70710678 0.70710678 0. 0. ]”, and given 0.70710678 is \(\frac{1}{\sqrt{2}}\), it is what we were expecting. Now to do the H operation on the pairs of rows associated with the other qubit (qubit 1).

algo.h(1)  # Apply H operation on pairs of rows related to qubit 1
v3 = Statevector(algo)
print(np.real_if_close(v3.data))

Which will print “[0.5 0.5 0.5 0.5]”. The application of the H operations has set up the state vector so that the quantum computer should give us different randomly generated 2 bit values with uniform distribution. Let’s add a measurement to the end of our algorithm, and have the quantum computer do this 1,000 times and see what we get.

algo.measure_all()  # Measure the qubits and get some bits
result = execute(algo, backend, shots=1000).result()  # Run this all 1,000 times
plot_histogram(result.get_counts(algo))
plt.show()
chart with four columns of similar height, labelled with 00, 01, 10 and 11

This shows that of the 1,000 times this was performed (1,000 “shots”), the different 2-bit results occurred approximately the same number of times. It is what you would expect of a uniform distribution, noting that it is unlikely for every possibility to occur exactly the same number of times.

You can extend this process to as many random bits as you want, by having a qubit for each and applying the H operation in turn for each qubit. Quantum computers are still not very big, so you’ll run out of available qubits quickly. Or, you may want to just to re-run this process and get another two random bits each time.

We used a quantum computer simulator here, so it’s still a pseudo-random result. To use an actual quantum computer, you would need to set up an account on IBM Quantum, get an API key, and change the backend to point at an instance of a quantum computer from their cloud. This is easy enough to do, but is an unnecessarily complication for this article.

You can then access true random bits that can be fed into any software that needs it. With all that, you have seen how to create a simple quantum algorithm and make it do something useful that is not easily done on a digital computer.

Please let me know… Were you able to follow this description of quantum computation? Do you feel confident that you could get this working on a real quantum computer? Would you prefer if there was more linear algebra, matrices and complex numbers in this article?

My main insight from SXSW Sydney

Last week, I attended the inaugural SXSW Sydney, and the first SXSW outside of Texas. It was different to the regular tech conferences that I’ve attended – it was much more diverse, with the games/film/music streams attracting a broader crowd. The sessions that I made it into were stimulating and sparked a range of ideas.

Of course, topics like AI (particularly Generative AI) and the Future of Work featured heavily in many presentations, and this led me to a realisation that I hadn’t had before, and I feel is likely to be the biggest impact from GenAI in the medium term. Rather than keep it to myself, I am sharing it here so that I can hear from others if it makes sense to them also.

Specifically, GenAI will bring about a huge disruption to the professional workforce and education system, not necessarily because humans will be replaced, but because humans who have been excluded from participation will now have fewer barriers to entry. Proficiency in the English language has been used as a justification for keeping certain people out of certain fields, and GenAI allows anyone from a non-English background to be as creative, smart, and persuasive as they are in their native tongues.

Our current GenAI systems are largely based on the Transformer machine learning architecture, which showed up early in online language translation tools like Google Translate. However, the GPT (T stands for Transformer) systems, particularly ChatGPT, have shown us that only a few words in broken English are able to be turned into paragraphs of words in perfect English, or even the reverse where paragraphs are summarised down to a few points in another language. University-level English spelling, grammar, and comprehension are no longer the exclusive domain of the English fluent.

There’s a fun TV series called Kim’s Convenience about a Korean couple who move to Canada to raise their family. The couple were teachers in Korea, but instead of doing that, they open a convenience store in Toronto. Presumably their lack of English or French language fluency would have been a limitation in getting teaching jobs. However, less than two months ago, OpenAI published their guide for teachers around ChatGPT, and it included the use case of “Reducing friction for non-English speakers”. In this guide, it was to help non-English students, but many of the suggestions could help non-English teachers also.

About 6% of the world’s population are native English speakers, and 75% do not speak English at all. And yet, about a third of the world’s GDP comes from countries where English fluency is required for success. If English is no longer a barrier to success in that market, it will be a significant disruption.

The spread of remote working technologies due to the pandemic has changed the ways of working for many jobs. Many white-collar jobs will likely still have an element of face-to-face contact, even if to come together for celebrations or training. However, where workers can be fully remote, the lack of English fluency as a barrier will enable many countries to export their talent without it leaving their shores.

Before the pandemic hit, over a quarter of University revenues in Australia came from international students. This gives international students some influence over University policies, and currently they face English language proficiency tests as part of their enrolment and visa processes. In the near future, GenAI looks set to be considered a generally-available tool in the workplace, like a calculator or laptop. If prospective students could make use of such a tool to address any gaps in their English language skills post-graduation, is it fair to prevent them from using it before graduation?

Traditionally, those people who had limited English in countries like Australia, UK or USA had been resigned to taking a jobs as an “unskilled” worker. There are already concerns that the number of people willing to do this type of work might not be enough to meet future industry demands. What might happen to wages if a good proportion of these people were able to move out of the unskilled workforce? How readily can the creative and information worker industries expand to take on new talent? What new barriers might be created by unions and professional organisations to help limit a flood of new workers into their industries?

GenAI has been making headlines that AI is taking many people’s creative jobs. After hearing from several panels at SXSW on AI, Long-term Forecasting, Work of the Future, and Education, my conclusion is that a plausible and perhaps more relevant headline would be that GenAI will allow many more people to take on creative jobs.

Making a VRM avatar from Ready Player Me

When I went looking to create an avatar, I discovered that there were a lot of options. There are 2D avatars that look like animated illustrations and 3D avatars that look like video game characters. There are full-body avatars, and half-body avatars (the top half, if you’re wondering). There are avatars tied to a particular app or service, and avatars that use an interoperable standard. There are many standards.

I decided that I wanted a full-body 3D avatar, since this seems to be the way things are headed. If I was using a Windows PC, I would be able to use something like Animaze and have my avatar track to my gestures and expressions. However, I am currently using a Mac and there are fewer options, especially in English. I was able to find the browser-based FaceVTuber service and the application 3tene, though. 3tene requires avatars in the VRM standard, so that made my decision for me.

The easiest way to create a VRM avatar seems to be to use VRoid Studio application, although the resulting avatars look like anime characters. I wanted to create a more realistic looking 3D avatar, and a service like ReadyPlayer.Me would be perfect, as it quickly creates an avatar based on a photo. The catch is that ReadyPlayer.Me does not yet export a VRM file version of their avatars. But there is a way to do it, if you’re willing to jump through some hoops.

This is a guide that I’ve put together based on trial and error, and heavily inspired by ReadyPlayer.Me’s instructions on exporting to a GLB file for Unity and Mada Craiz’s video on converting a ReadyPlayer.Me GLB file into a VRM file.

Firstly, you will need to have downloaded Blender and Unity / Unity Hub. For Unity, you will probably need to also set up an account. This guide was based on using Blender v3.2.1 and Unity 2020.3.39f1 Intel.

You will also need to download the UniVRM package for Unity. I used v0.103.2, which was the latest version at the time. Make sure you download the file named something like UniVRM-0.xxx.x_xxx.unitypackage. You don’t need the other files.

How to create a VRM file from a Ready Player Me avatar

  1. Create a folder that you’re going to store all the avatar assets in, let’s call it vrm_assets.
  2. Create an account on ReadyPlayer.Me, and build an avatar for yourself. It’s pretty fun.
  3. Click on “My Avatars”. You may need to click on Enter Hub to see this menu option.
  4. Click on the 3-dots icon on your avatar, and select “Download avatar .glb”, and store it in vrm_assets (or whatever you called that folder before).
    screenshot of page within Ready Player Me showing the menu to download a GLB file
  5. Open Blender, and start a New File of the General type.
  6. In the Scene Collection menu, right-click the Collection and choose Delete Hierarchy, to get rid of everything in the scene.
  7. Then select File > Import > glTF 2.0 (.glb/.gltf) menu option, pick the avatar GLB file that you downloaded from ReadyPlayer.Me and stored in vrm_assets, and click “Import glTF 2.0”.
  8. If you’re worried that all of the colours and textures are missing, you can get them to appear by pressing “Z” and selecting Material preview, but you can skip this step.
  9. Select the Texture Paint on the top menu bar to enter the Texture Paint workspace.
  10. Change the “Paint” mode to the “View” mode in the menu in the top left of the Texture Paint workspace screen.
    screenshot of Blender showing where the View menu is
  11. Then use the texture drop-down in the menu bar at the top to select each Image_0, Image_1, texture etc. in turn.
  12. For each texture, select the  Image > Save As menu option to save as individual images in your vrm_assets folder. Some of the textures could be JPG files while others are PNG files. Don’t worry about that. Just make sure you save all the images, but you can ignore “Viewer Node” or “Render Result”.
  13. Now select File > Export > FBX (.fbx) and before you save, change the “Path Mode” to “Copy” and click on the button next to it to “Embed Textures”. Then click the “Export FBX” button to save it into vrm_assets as well.
    Screenshot in Blender showing where to set Path Mode to Copy
  14. Close down Blender, and open up Unity Hub.
  15. Create a New Project, and select an Editor Version that begins 2020.3 and using the 3D Core template. Give the project a name that works for you, but I will use “VRM init”. Click “Create project”.
  16. Wait a little while for it to start up, then a blank project will appear. The first thing to do is bring in the UniVRM unitypackage file, so drag that from the file system into the Assets window. You will be shown an import window, with everything selected. Just click Import to bring it all in. After it’s done, UniGLTF, VRM and VRMShaders will be added to the Assets window.
    Screenshot of Blender showing the import of the unity package
  17. Create a new folder in the Assets window called Materials. Open the Materials folder, then drag all the texture files from vrm_assets over into it.
    Screenshot of Unity showing the textures in the Materials folder
  18. Go back out of the Materials folder to the top level of Assets, and drag the FBX file that you exported from Blender into the same Assets window. The model will appear there after a little while.
  19. If at any point you get an error message like “A Material is using the texture as a normal map”, just click “Fix now”.
  20. Click on the model, then in the Inspector window, click on Rig. Choose Animation Type to be “Humanoid”. Click Apply.
  21. Staying in the Inspector window, click on Materials. Choose Material Creation Mode to be “Standard (Legacy)”, choose Location to be “Use External Materials (Legacy)”, and leave the other options at their defaults (Naming as “By Base Texture Name” and Search as “Recursive-Up”). Click Apply.
  22. Drag the model from Assets into the Scene.
  23. If your model is meant to look like an anime figure, do this step, but otherwise (e.g. for more realistic avatars) skip it. Expand the newly created avatar in the Hierarchy window, and for each Material listed (which should be everything but Armature), click on it, then scroll down in the Inspector to the Shader. Click on the Shader drop-down (it may say something like “Standard”) and change it to VRM > MToon. Do this for all the materials in the model.
    Screenshot of Unity showing where to change the material Shader
  24. Alternatively, you can do other tweaks to the materials at this point. I find Unity makes the textures look a little grey, so this can be corrected by going into each Material as described in the previous step, opening up the Shader and changing the colour next to Albedo to use Hexadecimal FFFFFF (instead of CCCCCC). This is completely optional though.
  25. Click on the avatar in the Hierarchy window, and then in the VRM0 top-level menu of Unity, select Export to VRM 0.x resulting in the export window popping up.
    Screenshot of Unity showing the VRM export window
  26. Click on “Make T-Pose”. Scroll down a bit and enter a Title (ie. the name of your avatar), a version (e.g. 1.0) and the Author (i.e. your name). Then click Export. Choose a name like “avatar” and save the VRM file into your vrm_assets folder.
  27. Delete the avatar that you just exported from the Scene by right-clicking it in the Hierarchy and choosing Delete. This just keeps the Scene neat for later.
  28. Now, drag the newly-saved VRM file into the Assets window of your Unity project. It is time to configure the lip synch and facial expressions.
  29. Double-click on the BlendShapes asset (if you had saved the VRM file as avatar.vrm, this asset will be called avatar.BlendShapes) to show all the expressions that can be configured. Clicking on BlendShape will allow you to easily see and configure them in one place.
    Screenshot of Unity showing the configuration of Blend Shape
    Configuring the vowels will allow lip synch to work with your avatar, but you should configure all of it to ensure your avatar doesn’t look too wooden. Note that the vowels are in the Japanese order: A, I, U, E, O. Here are the settings that I used, but different avatars will need different values.
    • A:
      • Wolf3D_Head.viseme_aa 100
      • Wolf3D_Teeth.viseme_aa 100
    • I:
      • Wolf3D_Head.viseme_I 100
    • U:
      • Wolf3D_Head.viseme_U 100
    • E:
      • Wolf3D_Head.viseme_E 100
      • Wolf3D_Teeth.viseme_E 30
    • O:
      • Wolf3D_Head.viseme_O 100
      • Wolf3D_Teeth.viseme_O 100
      • Wolf3D_Teeth.mouthOpen 15
    • Blink:
      • Wolf3D_Head.eyesClosed 100
    • Joy:
      • Wolf3D_Head.mouthOpen 60
      • Wolf3D_Head.mouthSmile 48
      • Wolf3D_Head.browInnerUp 11
    • Angry:
      • Wolf3D_Head.mouthFrownLeft 65
      • Wolf3D_Head.mouthFrownRight 65
      • Wolf3D_Head.browDownLeft 20
      • Wolf3D_Head.browDownRight 20
    • Sorrow:
      • Wolf3D_Head.mouthOpen 60
      • Wolf3D_Head.mouthFrownLeft 50
      • Wolf3D_Head.mouthFrownRight 50
      • Wolf3D_Teeth.mouthOpen 30
    • Fun:
      • Wolf3D_Head.mouthSmile 50
    • LookUp:
      • EyeLeft.eyesLookUp 36
      • EyeRight.eyesLookUp 36
      • Wolf3D_Head.eyeLookUpLeft 75
      • Wolf3D_Head.eyeLookUpRight 75
    • LookDown:
      • EyeLeft.eyesLookDown 40
      • EyeRight.eyesLookDown 40
      • Wolf3D_Head.eyeLookDownLeft 20
      • Wolf3D_Head.eyeLookDownRight 20
    • LookLeft:
      • EyeLeft.eyeLookOutLeft 67
      • EyeRight.eyeLookInRight 41
    • LookRight:
      • EyeLeft.eyeLookInLeft 41
      • EyeRight.eyeLookOutRight 67
    • Blink_L:
      • Wolf3D_Head.eyeBlinkLeft 100
    • Blink_R:
      • Wolf3D_Head.eyeBlinkRight 100
  30. Now go back to the top level of the Assets window and scroll down to the avatar VRM model, then drag it into the Scene.
  31. Just as before, in the VRM0 top-level menu of Unity, select Export to VRM 0.x. You can leave the fields as they are, or update then. Click on Export. Save your VRM file into your vrm_assets folder with a new name to reflect it now has the expressions configured.
  32. Quit and save Unity, in case you want to come back and make further tweaks. You now have a VRM model.

Test out the VRM file in the avatar application of your choice! Good luck.

Turning up for work as an avatar

I don’t think we’re talking enough about avatars. I don’t mean the James Cameron film or the classic anime series. I’m referring to the computer 3D model that can represent you online, instead of a picture or video of the “real you”.

Due to the Covid-19 pandemic, we’ve had something like 5 years of technology uptake in an accelerated timeframe. Remote working has become much more common, with people regularly joining meetings with colleagues or stakeholders via services like Teams, Webex or Zoom rather than meeting up in person.

While pointing a camera at your face and also seeing an array of boxes containing other people’s faces has its merits, it can have a bunch of downsides. It turns out that many of these can be addressed by attending the meeting as an avatar rather via camera.

Interacting with others via avatars is the normal way of things when it comes to computer games. Many people are familiar with avatars from online social settings like Minecraft, Fortnite or Roblox. I’d think that for many kids today, they have spent more hours interacting online with others as an avatar than on camera.

So, it may be there is a generational shift coming as such people come up through our Universities and workplaces. But there are also fair reasons for moving to use avatars for meetings in any case. Here are five reasons why you should consider turning up for work online as an avatar.

1. It’s less stress

Being on camera can be a bit stressful, since your appearance is broadcast to all the other people in the same meeting, and other people can be a bit judgy. Why should your appearance be the concern of people that don’t need to share the same physical space as you?

If you attend a meeting as an avatar, you

  • Don’t have to shave, brush hair, put on makeup
  • Don’t have to worry about a pimple outbreak, or a bad haircut
  • Don’t have to get out of pyjamas, take off a beanie, or cover up a tattoo
  • Know there’s no chance of someone embarrassing wandering past in the background or a pet leaping up in front of you

2. You will appear more engaged

Well, if having the camera on is stressful, why not just turn it off? In some workplaces or schools, it is considered bad etiquette to turn off your camera in a group video call. It is not a great experience to be talking to a screen of black boxes and not seeing anything of your audience. Seeing a participant’s avatar watching back instead of a black box is a definite improvement.

However, sometimes it is a good idea to turn off the camera, such as when eating or having to visit the bathroom. The participant is still engaged in the meeting but for good reasons has turned off the camera. There is no need to do that with an avatar.

An avatar is also able to make eye contact through the meeting. Unfortunately, not everyone with a camera can do this, as the camera position might be to the side, above or below the screen that the participant is actually looking at. This tends to make the participant look distracted, as that would be how such behaviour would be interpreted in a face-to-face meeting. Avatars don’t have this issue.

3. Avatars are more fun

With Teams, Webex or Zoom, you can replace your background with a virtual background for a bit of fun. With an avatar, you can change everything about your look, and make these changes throughout the day.

You don’t even need to be human, or even a living creature. You might want to stick to an avatar that is at least humanoid and has a face, but there’s a huge creative space to work within.

In some online services, avatars are not limited to being displayed in a box (like your camera feed is), but can interact in a 3D space with other avatars. This also means that stereo audio can be used to help position the avatar in a physical space, making it easier to tell who is speaking by just where the sound is coming from, or distinguish a speaker when someone is talking over the top of them.

4. There may be less risk of health issues

Most group video meeting services show a live feed of your own camera during the call. It’s not exactly natural to spend hours of a day looking at yourself in a mirror, especially if the picture of you is (most likely) badly lit, from an odd or unflattering angle, and with a cheap camera lens. Then, if you couple this with seeing amazing pictures of others online, say on social media, it all appears to be a bit unhealthy.

While it’s not an official condition, there is some discussion about what is being called Zoom dysmorphia, where people struggle to cope due to anxiety about how they appear online. These people may go the plastic surgery route in order to deal with this.

Having a camera on all the time may also be generally unhealthy since it ties people to the desk for the duration of the call. Without this, for some meetings, people might instead take a call while walking the dog or taking a stroll around the block.

5. It works well for hybrid meetings

Hybrid is hard. It’s typically not a level playing field to have some meeting participants together in a room and some joining remotely. Having a camera at the front of a room capturing all of the in-person attendees means it is often difficult for the remote participants to see them.

The main alternative is that all the participants in the room have a device in front of them that allows them to join the meeting as a bunch of remote participants who happen to be in the same place. This usually results in a bunch of cameras pointing up people’s noses, as the cameras in a laptop or tablet are not at eye-level.

If the people in the room join as avatars, they can be showed nicely to the other participants, and the individuals’ cameras are often still adequate for animating their avatar to track with their face and body.

However

There are some down-sides to using avatars. It can make it more difficult for hard-of-hearing participants since they can’t rely on lip reading to follow a conversation. There will need to be avatar etiquette discussions so people aren’t made uncomfortable by certain types of avatar turning up to meetings. The technology is still evolving so it can look a bit unnerving if an avatar doesn’t show expected human emotions.

But directionally, avatars solve problems with our current group video meetings, and we can expect to see them become more mainstream over the coming years.

What is a qubit?

I am not a deep expert in quantum computing, but I know several who are. In order to chat to them, I have read quite a few introductory quantum computing articles or online courses. However, I find that these are either pitched at a level where it’s all about the hype, or at a level where you need to have a good background in either mathematics or physics to follow along. So, I have been trying to describe a quantum computer in a useful way to people without the technical background.

This is just such an attempt. If you’re still with me, I hope you find this useful. This is for people that don’t know the difference between Hamiltonians, Hermitians or Hilbert spaces, and aren’t planning to learn.

Let’s start with some definitions. A quantum computer is a type of computing machine that uses qubits to perform its calculations. But this raises the question of what is a qubit?

Digital, or classical, computers use bits to perform their calculations. They run software (applications, operating systems, etc.) that run on hardware (CPUs, disk drives, etc.) that are based on bits, which can be either 0 or 1. The hardware implementation of these bits might be based on magnetised dots on plastic tape, pulses of light, electric current on a wire, or many others.

Qubits are “quantum bits”, and also have a variety of hardware implementations such as photon polarisation, electron spin, or again many others. Any quantum mechanical system that can be in two distinct states might be used to implement a qubit. We can exploit the properties of quantum physics to allow a quantum computer to perform calculations on qubits that aren’t possible on bits.

Before we get to that, it is worth noting that quantum computers are known to be able to perform certain calculations in minutes that even a powerful classical computer could not complete in thousands of years. For these specialised calculations, the incredible speed-up in processing time is why quantum computers are so promising. As a result, quantum computers look to revolutionise many fields from materials engineering to cyber security.

Since a qubit can be made from a variety of two-state quantum systems, let’s consider an analogy where we implement a qubit on something we all have experience with: a coin. (I know this is not an exact analogy since a coin is a classical system not a quantum mechanical system, and it can’t actually implement entanglement or complex amplitudes, but it’s just an analogy so I’m not worried.)

If we consider a coin lying on a table, it can be either heads-up or heads-down (also known as tails). For the purposes of this analogy, let’s call these states 1 and 0. You will recognise that this is like a classical bit.

Maybe this coin has different types of metals on each side, so we could send some kind of electromagnetic pulse at it to cause it to flip over, and this way we could change it from 1 to 0, or visa versa. If there is another coin next to it, we might consider another kind of electromagnetic pulse that reflects off only one of those metals in a way that would flip the adjacent coin if the first coin’s 1 side was up. You might ultimately be able to build a digital computer of sorts on these bits. (You can build a working digital computer within the game of Minecraft, so anything’s possible.)

Let’s now expand our analogy and add a coin flipping robot arm. It is calibrated to send a coin up into the air and land it on the table, such that it always lands with the 0 side up. While the coins are in the air, these are our qubits. When they land on the table, they become bits.

Now we can flip coins into the air, and send electromagnetic pulses at them to change their state. However, unlike bits that can be only either 0 or 1, qubits have probabilities. A pulse at a coin can send it spinning quickly so that when it lands on the table it will be either 0 or 1 with a 50-50 chance. Another pulse might reflect off this spinning coin so that it hits the next coin and spins it only if the pulse happens to hit the 1 side of the first coin. Now when the coins land, they have a 50-50 chance of either being both 0 or both 1.

However, you won’t know this from measuring it just the one time. You will want to perform the coin flips and the same electromagnetic pulses a hundred times or more and measure the number of different results you get. If you do the experiment 200 times, and 100 of those times you get two 0s and the other 100 times you get two 1s, you can be pretty confident that this is what is going on. For more complicated arrangements of pulses, and greater numbers of coins, you might want to do the experiment 1000 times to have a clear idea of what is happening.

This is how quantum computing works. You perform manipulations on qubits (coins in the air), these set up different possible results with different probabilities, the qubits become bits (coins on the table) that can then be read and manipulated by a classical computer, and you repeat it all many times so you can determine things about those probabilities.

Wrist Computers

At some point in the last century, a strange thing happened: people took something that they’d been happy to carry around in their pockets for centuries and started to wear it on their wrist. Why?

I have just bought myself a smartwatch, and it’s got me thinking about this. A smart watch is typically what a 1980s calculator watch would be if someone invented it today. Because that’s basically what 99% of them are. Not calculator watches, of course, but stick with me for a bit. Just as in the 1980s, the most computing power an ordinary person could carry around in their pocket was a calculator, so people tried to put a tiny version of it on their wrist. These days, the most computing power an ordinary person can carry around in their pocket is a smartphone, so people are trying to put a tiny version of it on their wrists.

That said, you may not be too surprised to hear that the smartwatch I bought was part of the 1% that aren’t like that. It is a Withings Activité Pop, which is an analog watch that happens to also talk to my smartphone using Bluetooth. Withings isn’t the only maker of this sort of smart watch, e.g. you can also get a Martian watch which takes a similar approach to being “smart”. I expect other watch makers will put chips in their watches and it will become pretty normal soon.

I am really loving my Withings smartwatch. It automatically updates the time when daylight savings changes or when I travel into a different timezone. It has a pedometer inside it, and shows me my progress towards my daily step target on a dial on the face. It also has a bunch of other features, and sometimes gets new ones that appear for free, like tracking swimming strokes. But most of all, it looks good, is light on my wrist, and has a battery life of over 8 months. While these as expected features of a normal watch, they are rather novel in a smartwatch.

As a result, smartwatches haven’t really taken off yet in the way that, say, FitBit fitness trackers have. Is the smartwatch market destined for greatness or niche-ness?

Perhaps the history of the pocket watch has some relevant lessons, for which I will be drawing heavily on Wikipedia. The wearable watch was a 16th century innovation, beginning as a clock-on-a-pendant with only an hour hand. Some 17th century improvements brought the glass-covered face and the minute hand, and they became regularly carried in (waist coat) pockets at this time. It took until late in the 18th century for the pocket watch to move beyond a pure luxury item.

Pocket watches continued to be the dominant form of watch, at least for men, until the late 19th century, when the “wristlet” (we know it better as the wrist watch) came along. The British Army began issuing them to servicemen in 1917, where synchronising the creeping barrage tactic between infantry and artillery was important, and pocket watches were impractical. Reading the time at a glance was probably the first “killer app”, and by 1930, the ratio of wrist to pocket watches was 50 to 1. Within a couple of decades, the pocket watch had been completed disrupted.

While it was more convenient to read the time on a wrist watch than a pocket watch, it was also was also awkward to wear a heavy thing on a wrist, and in terms of fashion, the wrist watch was considered more of a women’s fashion item. In the end, World War I forced the issue, eliminating the fashion consideration, and the convenience factor overcame the weight problem.

Coming back to the present, UK mobile operator O2 published a report called “All About You” in 2012 that noted 46% of respondents had dispensed with a watch in favour of using their smartphone to check the time. It seems the greater utility of a smartphone has led people to forgo their watches, even if it means that time has gone back into the pocket.

So, there’s an argument that if the smartwatch provided similar utility to the smartphone, people would again shift from the pocket to the wrist. My Withings watch doesn’t in any way substitute for my smartphone, and is really a smartphone accessory. However, something like a LG Urbane Second Edition watch runs Android and has an LTE connection for calls and texting, and is more powerful than even a smartphone of a few years ago. Speech recognition can make up for the lack of keyboard entry, and a Bluetooth headset can enable private conversations.

However, economically a smartphone is actually a games platform, and games dominate the revenues from apps on smartphones. Making the smartwatch a viable games platform may be required for it to replace smartphones. Even in the 1980s, there were attempts to create games for the wrist, but they weren’t enormously successful compared to the game & (pocket) watch versions. Admittedly, there are games for modern smartwatches. However, they drain the battery and aren’t the same calibre as smartphone games.

If we measure the period of the smartphone since 2002, when Nokia introduced Series60 handsets, it has been with us for 13 years. The pocket watch, from invention to disruption, lasted 400 years, but declined due to the rise of the wrist watch in the last 50 of those years. If the smartwatch disrupted the smartphone at the same speed, it would need less than 2 years.

All I can say is: watch this space.

Lessons from NYT on innovation

The Kindle New York TimesWhatever the circumstances that led someone at The New York Times to leak their report on Innovation, I am thankful. Published (internally) in March, it is the fruits of a six month long deep-dive into the business of journalism within a company that has been a leader in that industry for over a century, and provides an intimate and honest study into how an incumbent can be disrupted. It is 97 pages long, and worth reading for anyone who is interested in innovation or the future of media.

The report was leaked in full in May, and I’ve been reading bits of it in my spare time. Just recently I completed it, and felt it was worth summarising some of the lessons that are highlighted by the people at the Times. As it is with such things, my summary is going to be subjective and – by nature – highly selective, so if this piques your interest, I encourage you to read the whole thing.

(My summary ended up being longer than I’d originally intended, so apologies in advance.)

Organisational Division

Because of the principle of editorial independence, the Times has clear boundaries between the journalists in the newsroom and those who operate “the business” part of the newspaper, which has been traditionally about selling advertising. This separation is even known as “church and state” within the organisation, and affects everything from who is allowed to meet with whom (even during brown-bag lunch style meetings) to the language used to communicate concepts. This has worked well in the past, allowing the journalism to be kept at the highest quality, without fear of being compromised by commercial considerations.

However, the part of the organisation that has been developing new software tools and reader applications is within “the business” (not being journalists), and has hence been disconnected from the newsroom. Hence new software is not developed to support the changing style of journalism, and where it is, it is done as one-off projects. Other media organisations are utilising developers more strategically, resulting in better tools for the journalists and a better experience for the readers.

Lesson: Technology capability needs to be at the heart of an innovation organisation, rather than kept at arms-length.

Changing Customers

For a very long time, the main customer of the Times has been advertisers. However, print media is facing a future where advertisers will not pay enough to keep the organisation running. Online advertising pays less than print advertising, and mobile advertising even less again. Coupled with declining circulation due to increased digital readership, the advertising business looks pretty sick. But there’s a new type of customer for the digital editions that is growing in importance: the reader.

While advertising revenues had the potential to severely compromise journalism, it’s not so clear that the same threat exists from reader revenues. In theory there is a good alignment: high quality journalism results in more readers. But if consideration of attracting readers is explicitly kept away from the newsroom as part of the “church and state” division, readers may end up being attracted by other media organisations. In fact, this is what is happening at the Times, with declines in most online reader metrics, and none increasing.

In the print world, it was enough to produce a high quality newspaper and it would attract readers. However, in the digital world this strategy is not currently working. Digital readers don’t select a publication and then read the stories in it, they discover individual articles from a variety of sources and then select whether to read them or not. The authors of articles need to take a bigger role in ensuring those articles are discovered.

Lesson: When customers radically change, the business needs to radically change too (many true-isms may be true no longer).

Experimentation

The rules for success in digital are different from those of traditional print journalism, although no-one really knows what they are yet. That said, the Times newsroom has an ingrained dislike of risk-taking. Again this made sense for a newsroom that didn’t want to print an incorrect story, and so everything had to be checked before it went public. However, this culture inhibits innovation if applied outside of the news itself.

Not only does it a culture of avoiding risks prevent them from experimenting and slow the ability to launch new things, but smart people within the organisation risk getting good at the wrong things. A great quote from the report: “When it takes 20 months to build one thing, your skill set becomes less about innovation and more about navigating bureaucracy.”

Also, the newsroom lacks a dedicated strategy and operations team, so doesn’t know how well readers are responding to experiments, or what is working well for competitors. Given that competitors are no longer only other daily newspapers, it’s not enough to just read the morning’s papers to get insight into the competition. BuzzFeed reformatted stories from the Times and managed to get greater reader numbers than the Times was able to for the same stories.

Lesson: If experimentation is being avoided due to risk, then business risks are not being managed effectively.

Acquiring Talent

It turns out that people experienced in traditional journalism don’t automatically have all the skills to meet the requirements of digital readers. However, the Times has a bias for hiring and promoting people in digital roles based on their achievements as journalists. While this likely worked in the past to create a high quality newspaper, it isn’t working in digital. In general, the New York Times appears to be a print newspaper first, and a digital business second. The daily tempo of article submission and review is oriented around a daily publication to be read in the mornings, rather than supporting the release of stories digitally when they are ready to be published. Performance metrics are still oriented around the number of front page stories published – a measure declining in importance as digital readers cease to discover articles via the home page.

The lack of appreciation for the digital world and digital people in general has resulted in the departure of a number of skilled employees, according to the report. Hiring digital talent is also difficult to justify to management given that demand has pushed salaries higher for skilled people even if those people are relatively young. What could be a virtuous circle, with talent attracting talent, is working in the opposite direction with what appears to be a cultural bias against the very talent that would help the Times.

Lesson: An organisation pays for the talent either by paying market rates for capable people or paying the cost in lost opportunities.

Final words

When I first came across the NYT Innovation report, I expected to read about another example of the innovators’ dilemma, where rational business decisions kept them from moving into a new market. Instead, the report is the tale of how the organisation structure, culture and processes that made The New York Times great in the past are actively inhibiting its success in the present. Some of these seem to have become sacred cows and it is difficult for the organisation to get rid of them. It will require courage – and a dedication to innovation – to change the organisation into one that is able to compete effectively.