Quantum Counting

To understand this algorithm, it is important that you first understand both Grover’s algorithm and the quantum phase estimation algorithm. Whereas Grover’s algorithm attempts to find a solution to the Oracle, the quantum counting algorithm tells us how many of these solutions there are. This algorithm is interesting as it combines both quantum search and quantum phase estimation.

Contents

  1. Overview
    1.1 Intuition
    1.2 A Closer Look
  2. The Code
    2.1 Initialising our Code
    2.2 The Controlled-Grover Iteration
    2.3 The Inverse QFT
    2.4 Putting it Together
  3. Simulating
  4. Finding the Number of Solutions
  5. Exercises
  6. References

1. Overview

1.1 Intuition

In quantum counting, we simply use the quantum phase estimation algorithm to find an eigenvalue of a Grover search iteration. You will remember that an iteration of Grover’s algorithm, $G$, rotates the state vector by $\theta$ in the $|\omega\rangle$, $|s’\rangle$ basis: image1

The percentage number of solutions in our search space affects the difference between $|s\rangle$ and $|s’\rangle$. For example, if there are not many solutions, $|s\rangle$ will be very close to $|s’\rangle$ and $\theta$ will be very small. It turns out that the eigenvalues of the Grover iterator are $e^{\pm i\theta}$, and we can extract this using quantum phase estimation (QPE) to estimate the number of solutions ($M$).

1.2 A Closer Look

In the $|\omega\rangle$,$|s’\rangle$ basis we can write the Grover iterator as the matrix:

$$ G = \begin{pmatrix} \cos{\theta} && -\sin{\theta}\\ \sin{\theta} && \cos{\theta} \end{pmatrix} $$

The matrix $G$ has eigenvectors:

$$ \begin{pmatrix} -i\\ 1 \end{pmatrix} , \begin{pmatrix} i\\ 1 \end{pmatrix} $$

With the aforementioned eigenvalues $e^{\pm i\theta}$. Fortunately, we do not need to prepare our register in either of these states, the state $|s\rangle$ is in the space spanned by $|\omega\rangle$, $|s’\rangle$, and thus is a superposition of the two vectors. $$ |s\rangle = \alpha |\omega\rangle + \beta|s'\rangle $$

As a result, the output of the QPE algorithm will be a superposition of the two phases, and when we measure the register we will obtain one of these two values! We can then use some simple maths to get our estimate of $M$.

image2

2. The Code

2.1 Initialising our Code

First, let’s import everything we’re going to need:

import matplotlib.pyplot as plt
import numpy as np
import math

# importing Qiskit
import qiskit
from qiskit import IBMQ, Aer
from qiskit import QuantumCircuit, execute

# import basic plot tools
from qiskit.visualization import plot_histogram

In this guide will choose to ‘count’ on the first 4 qubits on our circuit (we call the number of counting qubits $t$, so $t = 4$), and to 'search' through the last 4 qubits ($n = 4$). With this in mind, we can start creating the building blocks of our circuit.

2.2 The Controlled-Grover Iteration

We have already covered Grover iterations in the Grover’s algorithm section. Here is an example with an Oracle we know has 5 solutions ($M = 5$) of 16 states ($N = 2^n = 16$), combined with a diffusion operator:

def example_grover_iteration():
    """Small circuit with 5/16 solutions"""
    # Do circuit
    qc = QuantumCircuit(4)
    # Oracle
    qc.h([2,3])
    qc.ccx(0,1,2)
    qc.h(2)
    qc.x(2)
    qc.ccx(0,2,3)
    qc.x(2)
    qc.h(3)
    qc.x([1,3])
    qc.h(2)
    qc.mct([0,1,3],2)
    qc.x([1,3])
    qc.h(2)
    # Diffuser
    qc.h(range(3))
    qc.x(range(3))
    qc.z(3)
    qc.mct([0,1,2],3)
    qc.x(range(3))
    qc.h(range(3))
    qc.z(3)
    return qc

Notice the python function takes no input and returns a QuantumCircuit object with 4 qubits. In the past the functions you created might have modified an existing circuit, but a function like this allows us to turn the QuantumCircuit object into a single gate we can then control.

We can use .to_gate() and .control() to create a controlled gate from a circuit. We will call our Grover iterator grit and the controlled Grover iterator cgrit:

# Create controlled-Grover
grit = example_grover_iteration().to_gate()
cgrit = grit.control()
cgrit.label = "Grover"

2.3 The Inverse QFT

We now need to create an inverse QFT. This code implements the QFT on n qubits:

def qft(n):
    """Creates an n-qubit QFT circuit"""
    circuit = QuantumCircuit(4)
    def swap_registers(circuit, n):
        for qubit in range(n//2):
            circuit.swap(qubit, n-qubit-1)
        return circuit
    def qft_rotations(circuit, n):
        """Performs qft on the first n qubits in circuit (without swaps)"""
        if n == 0:
            return circuit
        n -= 1
        circuit.h(n)
        for qubit in range(n):
            circuit.cu1(np.pi/2**(n-qubit), qubit, n)
        qft_rotations(circuit, n)
    
    qft_rotations(circuit, n)
    swap_registers(circuit, n)
    return circuit

Again, note we have chosen to return another QuantumCircuit object, this is so we can easily invert the gate. We create the gate with t = 4 qubits as this is the number of counting qubits we have chosen in this guide:

qft_dagger = qft(4).to_gate().inverse()
qft_dagger.label = "QFT†"

2.4 Putting it Together

We now have everything we need to complete our circuit! Let’s put it together.

First we need to put all qubits in the $|+\rangle$ state:

# Create QuantumCircuit
t = 4   # no. of counting qubits
n = 4   # no. of searching qubits
qc = QuantumCircuit(n+t, t) # Circuit with n+t qubits and t classical bits

# Initialise all qubits to |+>
for qubit in range(t+n):
    qc.h(qubit)

# Begin controlled Grover iterations
iterations = 1
for qubit in range(t):
    for i in range(iterations):
        qc.append(cgrit, [qubit] + [*range(t, n+t)])
    iterations *= 2
    
# Do inverse QFT on counting qubits
qc.append(qft_dagger, range(t))

# Measure counting qubits
qc.measure(range(t), range(t))

# Display the circuit
qc.draw()

Great! Now let’s see some results.

3. Simulating

# Execute and see results
emulator = Aer.get_backend('qasm_simulator')
job = execute(qc, emulator, shots=2048 )
hist = job.result().get_counts()
plot_histogram(hist)

We can see two values stand out, having a much higher probability of measurement than the rest. These two values correspond to $e^{i\theta}$ and $e^{-i\theta}$, but we can’t see the number of solutions yet. We need to little more processing to get this information, so first let us get our output into something we can work with (an int).

We will get the string of the most probable result from our output data:

measured_str = max(hist, key=hist.get)

Let us now store this as an integer:

measured_int = int(measured_str,2)
print("Register Output = %i" % measured_int)
Register Output = 5

4. Finding the Number of Solutions (M)

We will create a function, calculate_M() that takes as input the decimal integer output of our register, the number of counting qubits ($t$) and the number of searching qubits ($n$).

First we want to get $\theta$ from measured_int. You will remember that QPE gives us a measured $\text{value} = 2^n \phi$ from the eigenvalue $e^{2\pi i\phi}$, so to get $\theta$ we need to do:

$$ \theta = \text{value}\times\frac{2\pi}{2^t} $$

Or, in code:

theta = (measured_int/(2**t))*math.pi*2
print("Theta = %.5f" % theta)
Theta = 1.96350

You may remember that we can get the angle $\theta/2$ can from the inner product of $|s\rangle$ and $|s’\rangle$:

image3

$$ \langle s'|s\rangle = \cos{\tfrac{\theta}{2}} $$

And that the inner product of these vectors is:

$$ \langle s'|s\rangle = \sqrt{\frac{N-M}{N}} $$

We can combine these equations, then use some trigonometry and algebra to show:

$$ N\sin^2{\frac{\theta}{2}} = M $$

From the Grover's algorithm chapter, you will remember that a common way to create a diffusion operator, $U_s$, is actually to implement $-U_s$. This implementation is used in the Grover iteration provided in this chapter. In a normal Grover search, this phase is global and can be ignored, but now we are controlling our Grover iterations, this phase does have an effect. The result is that we have effectively searched for the states that are not solutions, and our quantum counting algorithm will tell us how many states are not solutions. To fix this, we simply calculate $N-M$.

And in code:

N = 2**n
M = N * (math.sin(theta/2)**2)
print("No. of Solutions = %.1f" % (N-M))
No. of Solutions = 4.9

And we can see we have (approximately) the correct answer! We can approximately calculate the error in this answer using:

m = t - 1 # Upper bound: Will be less than this 
err = (math.sqrt(2*M*N) + N/(2**(m-1)))*(2**(-m))
print("Error < %.2f" % err)
Error < 2.85

Explaining the error calculation is outside the scope of this article, but an explanation can be found in [1].

Finally, here is the finished function calculate_M():

def calculate_M(measured_int, t, n):
    """For Processing Output of Quantum Counting"""
    # Calculate Theta
    theta = (measured_int/(2**t))*math.pi*2
    print("Theta = %.5f" % theta)
    # Calculate No. of Solutions
    N = 2**n
    M = N * (math.sin(theta/2)**2)
    print("No. of Solutions = %.1f" % (N-M))
    # Calculate Upper Error Bound
    m = t - 1 #Will be less than this (out of scope) 
    err = (math.sqrt(2*M*N) + N/(2**(m-1)))*(2**(-m))
    print("Error < %.2f" % err)

5. Exercises

  1. Can you create an oracle with a different number of solutions? How does the accuracy of the quantum counting algorithm change?
  2. Can you adapt the circuit to use more or less counting qubits to get a different precision in your result?

6. References

[1] Michael A. Nielsen and Isaac L. Chuang. 2011. Quantum Computation and Quantum Information: 10th Anniversary Edition (10th ed.). Cambridge University Press, New York, NY, USA.

import qiskit
qiskit.__qiskit_version__
{'qiskit-terra': '0.14.2',
 'qiskit-aer': '0.5.2',
 'qiskit-ignis': '0.3.3',
 'qiskit-ibmq-provider': '0.7.2',
 'qiskit-aqua': '0.7.3',
 'qiskit': '0.19.6'}