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This page was generated from tutorials/algorithms/09_textbook_algorithms.ipynb.

Run interactively in the IBM Quantum lab.

Textbook and Shor’s algorithms

Qiskit contains implementations of the well known textbook quantum algorithms such as the Deutsch-Jozsa algorithm, the Bernstein-Vazirani algorithm and Simon’s algorithm.

Qiskit also has an implementation of Shor’s algorithm.

The preceding references have detailed explanations and build-out of circuits whereas this notebook has examples with the pre-built algorithms in Qiskit that you can use for experimentation and education purposes.

[1]:
import math
import numpy as np
from qiskit import BasicAer
from qiskit.aqua import QuantumInstance
from qiskit.aqua.algorithms import BernsteinVazirani, DeutschJozsa, Simon, Shor
from qiskit.aqua.components.oracles import TruthTableOracle

Deutsch-Jozsa algorithm

Lets start with the Deutsch-Jozsa algorithm which can determine if a function is 'balanced' or 'constant' given such a function as input. We can experiment with it in Qiskit using an oracles created from a truth tables. So for example, we can create a TruthTableOracle instance as follows.

[2]:
bitstr = '11110000'
oracle = TruthTableOracle(bitstr)

As shown, the truthtable is specified with the bitstr containing values of all entries in the table. It has length \(8\), so the corresponding truth table is of \(3\) input bits. We can of course see that this truth table represents a 'balanced' function as half of values are \(1\) and the other half \(0\).

It might seem like a moot point running Deutsch-Jozsa on a truthtable as the function outputs are literally listed as the truthtable’s values. The intention is to create an oracle circuit whose groundtruth information is readily available to us but obviously not to the quantum Deutsch-Jozsa algorithm that is to act upon the oracle circuit. In more realistic situations, the oracle circuit would be provided as a blackbox to the algorihtm.

Above said, we can inspect the circuit corresponding to the function encoded in the TruthTableOracle instance.

[3]:
oracle.circuit.draw(output='mpl')
[3]:
../../_images/tutorials_algorithms_09_textbook_algorithms_5_0.png

As seen, the \(v_i\)’s correspond to the 3 input bits; the \(o_0\) is the oracle’s output qubit; the \(a_0\) is an ancilla qubit.

Next we can simply create a DeutschJozsa instance using the oracle, and run it to check the result.

[4]:
dj = DeutschJozsa(oracle)
backend = BasicAer.get_backend('qasm_simulator')
result = dj.run(QuantumInstance(backend, shots=1024))
print(f'The truth table {bitstr} represents a \'{result["result"]}\' function.')
The truth table 11110000 represents a 'balanced' function.

We can of course quickly put together another example for a 'constant' function, as follows.

[5]:
bitstr = '1' * 16
oracle = TruthTableOracle(bitstr)
dj = DeutschJozsa(oracle)
backend = BasicAer.get_backend('qasm_simulator')
result = dj.run(QuantumInstance(backend, shots=1024))
print(f'The truth table {bitstr} represents a \'{result["result"]}\' function.')
The truth table 1111111111111111 represents a 'constant' function.

Bernstein-Vazirani algorithm

Next the Bernstein-Vazirani algorithm which tries to find a hidden string. Again, for the example, we create a TruthTableOracle instance.

[6]:
bitstr = '00111100'
oracle = TruthTableOracle(bitstr)

As shown, the truthtable is specified with the bitstr containing values of all entries in the table. It has length \(8\), so the corresponding truth table is of \(3\) input bits. The truthtable represents the function mappings as follows:

  • \(\mathbf{a} \cdot 000 \mod 2 = 0\)

  • \(\mathbf{a} \cdot 001 \mod 2 = 0\)

  • \(\mathbf{a} \cdot 010 \mod 2 = 1\)

  • \(\mathbf{a} \cdot 011 \mod 2 = 1\)

  • \(\mathbf{a} \cdot 100 \mod 2 = 1\)

  • \(\mathbf{a} \cdot 101 \mod 2 = 1\)

  • \(\mathbf{a} \cdot 110 \mod 2 = 0\)

  • \(\mathbf{a} \cdot 111 \mod 2 = 0\)

And obviously the goal is to find the bitstring \(\mathbf{a}\) that satisfies all the inner product equations.

Lets again look at the oracle circuit, that now corresponds to the binary function encoded in the TruthTableOracle instance.

[7]:
oracle.circuit.draw(output='mpl')
[7]:
../../_images/tutorials_algorithms_09_textbook_algorithms_13_0.png

Again the \(v_i\)’s correspond to the 3 input bits; the \(o_0\) is the oracle’s output qubit; the \(a_0\) is an ancilla qubit.

Let us first compute the groundtruth \(\mathbf{a}\) classically:

[8]:
a_bitstr = ""
num_bits = math.log2(len(bitstr))
for i in reversed(range(3)):
    bit = bitstr[2 ** i]
    a_bitstr += bit
print(f'The groundtruth result bitstring is {a_bitstr}.')
The groundtruth result bitstring is 110.

Next we can create a BernsteinVazirani instance using the oracle, and run it to check the result against the groundtruth.

[9]:
bv = BernsteinVazirani(oracle)
backend = BasicAer.get_backend('qasm_simulator')
result = bv.run(QuantumInstance(backend, shots=1024))
print(f'The result bitstring computed using Bernstein-Vazirani is {result["result"]}.')
assert(result['result'] == a_bitstr)
The result bitstring computed using Bernstein-Vazirani is 110.

Simon’s algorithm

Simon’s algorithm is used to solve Simon’s problem. Once again, for the example, we create a TruthTableOracle instance, where the construction shows a different form.

[10]:
bitmaps = [
    '01101001',
    '10011001',
    '01100110'
]
oracle = TruthTableOracle(bitmaps)

As shown, the truthtable is specified with three length-8 bitstrings, each containing the values of all entries for a particular output column in the table. Each bitstring has length \(8\), so the truthtable has \(3\) input bits; There are \(3\) bitstrings, so the truthtable has \(3\) output bits.

The function \(f\) represented by the truthtable is promised to be either 1-to-1 or 2-to-1. Our goal is to determine which. For the case of 2-to-1, we also need to compute the mask \(\mathbf{s}\), which satisfies \(\forall \mathbf{x},\mathbf{y}\): \(\mathbf{x} \oplus \mathbf{y} = \mathbf{s}\) iff \(f(\mathbf{x}) = f(\mathbf{y})\). Apparently, if \(f\) is 1-to-1, the corresponding mask \(\mathbf{s} = \mathbf{0}\).

Let us first compute the groundtruth mask \(\mathbf{s}\) classically:

[11]:
def compute_mask(input_bitmaps):
    vals = list(zip(*input_bitmaps))[::-1]
    def find_pair():
        for i in range(len(vals)):
            for j in range(i + 1, len(vals)):
                if vals[i] == vals[j]:
                    return i, j
        return 0, 0

    k1, k2 = find_pair()
    return np.binary_repr(k1 ^ k2, int(np.log2(len(input_bitmaps[0]))))

mask = compute_mask(bitmaps)
print(f'The groundtruth mask is {mask}.')
The groundtruth mask is 011.
[12]:
simon = Simon(oracle)
backend = BasicAer.get_backend('qasm_simulator')
result = simon.run(QuantumInstance(backend, shots=1024))
print(f'The mask computed using Simon is {result["result"]}.')
assert(result['result'] == mask)
The mask computed using Simon is 011.

We can also quickly try a truthtable that represents a 1-to-1 function (i.e., the corresponding mask is \(\mathbf{0}\)), as follows.

[13]:
bitmaps = [
    '00011110',
    '01100110',
    '10101010'
]
mask = compute_mask(bitmaps)
print(f'The groundtruth mask is {mask}.')

oracle = TruthTableOracle(bitmaps)
simon = Simon(oracle)
result = simon.run(QuantumInstance(backend, shots=1024))
print(f'The mask computed using Simon is {result["result"]}.')
assert(result['result'] == mask)
The groundtruth mask is 000.
The mask computed using Simon is 000.

Shor’s Factoring algorithm

Shor’s Factoring algorithm is one of the most well-known quantum algorithms and finds the prime factors for input integer \(N\) in polynomial time. The algorithm implementation in Qiskit is simply provided a target integer to be factored and run, as follows:

[14]:
N = 15
shor = Shor(N)
backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024)
result = shor.run(quantum_instance)
print(f"The list of factors of {N} as computed by the Shor's algorithm is {result['factors'][0]}.")
The list of factors of 15 as computed by the Shor's algorithm is [3, 5].

Note: this implementation of Shor’s algorithm uses \(4n + 2\) qubits, where \(n\) is the number of bits representing the integer in binary. So in practice, for now, this implementation is restricted to factorizing small integers. Given the above value of N we compute \(4n +2\) below and confirm the size from the actual circuit.

[15]:
print(f'Computed of qubits for circuit: {4 * math.ceil(math.log(N, 2)) + 2}')
print(f'Actual number of qubits of circuit: {shor.construct_circuit().num_qubits}')
Computed of qubits for circuit: 18
Actual number of qubits of circuit: 18
[16]:
import qiskit.tools.jupyter
%qiskit_version_table
%qiskit_copyright

Version Information

Qiskit SoftwareVersion
Qiskit0.23.6
Terra0.16.4
Aer0.7.5
Ignis0.5.2
Aqua0.8.2
IBM Q Provider0.11.1
System information
Python3.8.7 (default, Jan 25 2021, 16:23:06) [GCC 9.3.0]
OSLinux
CPUs2
Memory (Gb)6.791378021240234
Thu Feb 18 22:26:48 2021 UTC

This code is a part of Qiskit

© Copyright IBM 2017, 2021.

This code is licensed under the Apache License, Version 2.0. You may
obtain a copy of this license in the LICENSE.txt file in the root directory
of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.

Any modifications or derivative works of this code must retain this
copyright notice, and modified files need to carry a notice indicating
that they have been altered from the originals.