mirror of
https://github.com/TheAlgorithms/Python.git
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Remove references to depreciated QasmSimulator (#7417)
* Fix typos * Replace depreciated QasmSimulator in Deutsch-Jozsa algorithm * Replace depreciated QasmSimulator in half adder algorithm * Replace depreciated QasmSimulator in not gate algorithm * Replace depreciated QasmSimulator in full adder algorithm * Simplify qiskit import * Make formatting more consistent * Replace depreciated QasmSimulator in quantum entanglement algorithm * Replace depreciated QasmSimulator in ripple adder algorithm * Replace depreciated QasmSimulator in qubit measure algorithm * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * updating DIRECTORY.md * updating DIRECTORY.md * Remove qiskit import alias for clarity Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Tianyi Zheng
GitHub
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50da472ddc
commit
2859d4bf3a
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@ -27,6 +27,7 @@
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* [Hamiltonian Cycle](backtracking/hamiltonian_cycle.py)
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* [Knight Tour](backtracking/knight_tour.py)
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* [Minimax](backtracking/minimax.py)
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* [Minmax](backtracking/minmax.py)
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* [N Queens](backtracking/n_queens.py)
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* [N Queens Math](backtracking/n_queens_math.py)
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* [Rat In Maze](backtracking/rat_in_maze.py)
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@ -157,6 +158,7 @@
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* [Binary Tree Mirror](data_structures/binary_tree/binary_tree_mirror.py)
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* [Binary Tree Node Sum](data_structures/binary_tree/binary_tree_node_sum.py)
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* [Binary Tree Traversals](data_structures/binary_tree/binary_tree_traversals.py)
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* [Diff Views Of Binary Tree](data_structures/binary_tree/diff_views_of_binary_tree.py)
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* [Fenwick Tree](data_structures/binary_tree/fenwick_tree.py)
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* [Inorder Tree Traversal 2022](data_structures/binary_tree/inorder_tree_traversal_2022.py)
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* [Lazy Segment Tree](data_structures/binary_tree/lazy_segment_tree.py)
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@ -513,6 +515,7 @@
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* [Gamma](maths/gamma.py)
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* [Gamma Recursive](maths/gamma_recursive.py)
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* [Gaussian](maths/gaussian.py)
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* [Gaussian Error Linear Unit](maths/gaussian_error_linear_unit.py)
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* [Greatest Common Divisor](maths/greatest_common_divisor.py)
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* [Greedy Coin Change](maths/greedy_coin_change.py)
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* [Hamming Numbers](maths/hamming_numbers.py)
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@ -601,6 +604,7 @@
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* [Inverse Of Matrix](matrix/inverse_of_matrix.py)
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* [Matrix Class](matrix/matrix_class.py)
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* [Matrix Operation](matrix/matrix_operation.py)
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* [Max Area Of Island](matrix/max_area_of_island.py)
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* [Nth Fibonacci Using Matrix Exponentiation](matrix/nth_fibonacci_using_matrix_exponentiation.py)
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* [Rotate Matrix](matrix/rotate_matrix.py)
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* [Searching In Sorted Matrix](matrix/searching_in_sorted_matrix.py)
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@ -1,6 +1,6 @@
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#!/usr/bin/env python3
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"""
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Deutsch-Josza Algorithm is one of the first examples of a quantum
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Deutsch-Jozsa Algorithm is one of the first examples of a quantum
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algorithm that is exponentially faster than any possible deterministic
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classical algorithm
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@ -22,10 +22,10 @@ References:
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"""
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import numpy as np
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import qiskit as q
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import qiskit
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def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
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def dj_oracle(case: str, num_qubits: int) -> qiskit.QuantumCircuit:
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"""
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Returns a Quantum Circuit for the Oracle function.
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The circuit returned can represent balanced or constant function,
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@ -33,7 +33,7 @@ def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
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"""
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# This circuit has num_qubits+1 qubits: the size of the input,
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# plus one output qubit
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oracle_qc = q.QuantumCircuit(num_qubits + 1)
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oracle_qc = qiskit.QuantumCircuit(num_qubits + 1)
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# First, let's deal with the case in which oracle is balanced
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if case == "balanced":
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@ -43,7 +43,7 @@ def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
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# Next, format 'b' as a binary string of length 'n', padded with zeros:
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b_str = format(b, f"0{num_qubits}b")
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# Next, we place the first X-gates. Each digit in our binary string
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# correspopnds to a qubit, if the digit is 0, we do nothing, if it's 1
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# corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
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# we apply an X-gate to that qubit:
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for index, bit in enumerate(b_str):
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if bit == "1":
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@ -70,13 +70,15 @@ def dj_oracle(case: str, num_qubits: int) -> q.QuantumCircuit:
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return oracle_gate
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def dj_algorithm(oracle: q.QuantumCircuit, num_qubits: int) -> q.QuantumCircuit:
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def dj_algorithm(
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oracle: qiskit.QuantumCircuit, num_qubits: int
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) -> qiskit.QuantumCircuit:
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"""
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Returns the complete Deustch-Jozsa Quantum Circuit,
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Returns the complete Deutsch-Jozsa Quantum Circuit,
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adding Input & Output registers and Hadamard & Measurement Gates,
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to the Oracle Circuit passed in arguments
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"""
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dj_circuit = q.QuantumCircuit(num_qubits + 1, num_qubits)
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dj_circuit = qiskit.QuantumCircuit(num_qubits + 1, num_qubits)
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# Set up the output qubit:
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dj_circuit.x(num_qubits)
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dj_circuit.h(num_qubits)
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@ -95,7 +97,7 @@ def dj_algorithm(oracle: q.QuantumCircuit, num_qubits: int) -> q.QuantumCircuit:
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return dj_circuit
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def deutsch_jozsa(case: str, num_qubits: int) -> q.result.counts.Counts:
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def deutsch_jozsa(case: str, num_qubits: int) -> qiskit.result.counts.Counts:
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"""
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Main function that builds the circuit using other helper functions,
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runs the experiment 1000 times & returns the resultant qubit counts
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@ -104,14 +106,14 @@ def deutsch_jozsa(case: str, num_qubits: int) -> q.result.counts.Counts:
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>>> deutsch_jozsa("balanced", 3)
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{'111': 1000}
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"""
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# Use Aer's qasm_simulator
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simulator = q.Aer.get_backend("qasm_simulator")
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# Use Aer's simulator
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simulator = qiskit.Aer.get_backend("aer_simulator")
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oracle_gate = dj_oracle(case, num_qubits)
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dj_circuit = dj_algorithm(oracle_gate, num_qubits)
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# Execute the circuit on the qasm simulator
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job = q.execute(dj_circuit, simulator, shots=1000)
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# Execute the circuit on the simulator
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job = qiskit.execute(dj_circuit, simulator, shots=1000)
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# Return the histogram data of the results of the experiment.
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return job.result().get_counts(dj_circuit)
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@ -10,10 +10,10 @@ https://en.wikipedia.org/wiki/Adder_(electronics)
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https://qiskit.org/textbook/ch-states/atoms-computation.html#4.2-Remembering-how-to-add-
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"""
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import qiskit as q
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import qiskit
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def half_adder(bit0: int, bit1: int) -> q.result.counts.Counts:
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def half_adder(bit0: int, bit1: int) -> qiskit.result.counts.Counts:
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"""
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>>> half_adder(0, 0)
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{'00': 1000}
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@ -24,10 +24,10 @@ def half_adder(bit0: int, bit1: int) -> q.result.counts.Counts:
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>>> half_adder(1, 1)
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{'10': 1000}
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"""
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# Use Aer's qasm_simulator
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simulator = q.Aer.get_backend("qasm_simulator")
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# Use Aer's simulator
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simulator = qiskit.Aer.get_backend("aer_simulator")
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qc_ha = q.QuantumCircuit(4, 2)
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qc_ha = qiskit.QuantumCircuit(4, 2)
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# encode inputs in qubits 0 and 1
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if bit0 == 1:
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qc_ha.x(0)
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@ -48,9 +48,9 @@ def half_adder(bit0: int, bit1: int) -> q.result.counts.Counts:
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qc_ha.measure(3, 1) # extract AND value
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# Execute the circuit on the qasm simulator
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job = q.execute(qc_ha, simulator, shots=1000)
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job = qiskit.execute(qc_ha, simulator, shots=1000)
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# Return the histogram data of the results of the experiment.
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# Return the histogram data of the results of the experiment
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return job.result().get_counts(qc_ha)
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@ -6,21 +6,23 @@ times and print the total count of the states finally observed.
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Qiskit Docs: https://qiskit.org/documentation/getting_started.html
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"""
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import qiskit as q
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import qiskit
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def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
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def single_qubit_measure(
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qubits: int, classical_bits: int
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) -> qiskit.result.counts.Counts:
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"""
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>>> single_qubit_measure(2, 2)
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{'11': 1000}
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>>> single_qubit_measure(4, 4)
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{'0011': 1000}
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"""
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# Use Aer's qasm_simulator
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simulator = q.Aer.get_backend("qasm_simulator")
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# Use Aer's simulator
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simulator = qiskit.Aer.get_backend("aer_simulator")
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# Create a Quantum Circuit acting on the q register
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circuit = q.QuantumCircuit(qubits, classical_bits)
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circuit = qiskit.QuantumCircuit(qubits, classical_bits)
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# Apply X (NOT) Gate to Qubits 0 & 1
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circuit.x(0)
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@ -30,7 +32,7 @@ def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Co
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circuit.measure([0, 1], [0, 1])
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# Execute the circuit on the qasm simulator
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job = q.execute(circuit, simulator, shots=1000)
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job = qiskit.execute(circuit, simulator, shots=1000)
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# Return the histogram data of the results of the experiment.
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return job.result().get_counts(circuit)
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@ -11,7 +11,6 @@ https://www.quantum-inspire.com/kbase/full-adder/
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import math
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import qiskit
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from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
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def quantum_full_adder(
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@ -78,12 +77,12 @@ def quantum_full_adder(
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raise ValueError("inputs must be less or equal to 2.")
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# build registers
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qr = QuantumRegister(4, "qr")
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cr = ClassicalRegister(2, "cr")
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qr = qiskit.QuantumRegister(4, "qr")
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cr = qiskit.ClassicalRegister(2, "cr")
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# list the entries
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entry = [input_1, input_2, carry_in]
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quantum_circuit = QuantumCircuit(qr, cr)
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quantum_circuit = qiskit.QuantumCircuit(qr, cr)
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for i in range(0, 3):
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if entry[i] == 2:
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@ -102,8 +101,8 @@ def quantum_full_adder(
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quantum_circuit.measure([2, 3], cr) # measure the last two qbits
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backend = Aer.get_backend("qasm_simulator")
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job = execute(quantum_circuit, backend, shots=1000)
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backend = qiskit.Aer.get_backend("aer_simulator")
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job = qiskit.execute(quantum_circuit, backend, shots=1000)
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return job.result().get_counts(quantum_circuit)
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@ -29,8 +29,8 @@ def quantum_entanglement(qubits: int = 2) -> qiskit.result.counts.Counts:
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"""
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classical_bits = qubits
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# Using Aer's qasm_simulator
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simulator = qiskit.Aer.get_backend("qasm_simulator")
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# Using Aer's simulator
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simulator = qiskit.Aer.get_backend("aer_simulator")
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# Creating a Quantum Circuit acting on the q register
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circuit = qiskit.QuantumCircuit(qubits, classical_bits)
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# Now measuring any one qubit would affect other qubits to collapse
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# their super position and have same state as the measured one.
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# Executing the circuit on the qasm simulator
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# Executing the circuit on the simulator
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job = qiskit.execute(circuit, simulator, shots=1000)
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return job.result().get_counts(circuit)
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@ -2,11 +2,11 @@
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# https://en.wikipedia.org/wiki/Adder_(electronics)#Full_adder
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# https://en.wikipedia.org/wiki/Controlled_NOT_gate
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from qiskit import Aer, QuantumCircuit, execute
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import qiskit
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from qiskit.providers import Backend
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def store_two_classics(val1: int, val2: int) -> tuple[QuantumCircuit, str, str]:
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def store_two_classics(val1: int, val2: int) -> tuple[qiskit.QuantumCircuit, str, str]:
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"""
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Generates a Quantum Circuit which stores two classical integers
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Returns the circuit and binary representation of the integers
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@ -21,10 +21,10 @@ def store_two_classics(val1: int, val2: int) -> tuple[QuantumCircuit, str, str]:
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# We need (3 * number of bits in the larger number)+1 qBits
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# The second parameter is the number of classical registers, to measure the result
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circuit = QuantumCircuit((len(x) * 3) + 1, len(x) + 1)
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circuit = qiskit.QuantumCircuit((len(x) * 3) + 1, len(x) + 1)
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# We are essentially "not-ing" the bits that are 1
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# Reversed because its easier to perform ops on more significant bits
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# Reversed because it's easier to perform ops on more significant bits
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for i in range(len(x)):
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if x[::-1][i] == "1":
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circuit.x(i)
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@ -36,7 +36,7 @@ def store_two_classics(val1: int, val2: int) -> tuple[QuantumCircuit, str, str]:
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def full_adder(
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circuit: QuantumCircuit,
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circuit: qiskit.QuantumCircuit,
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input1_loc: int,
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input2_loc: int,
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carry_in: int,
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@ -55,14 +55,14 @@ def full_adder(
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# The default value for **backend** is the result of a function call which is not
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# normally recommended and causes flake8-bugbear to raise a B008 error. However,
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# in this case, this is accptable because `Aer.get_backend()` is called when the
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# in this case, this is acceptable because `Aer.get_backend()` is called when the
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# function is defined and that same backend is then reused for all function calls.
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def ripple_adder(
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val1: int,
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val2: int,
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backend: Backend = Aer.get_backend("qasm_simulator"), # noqa: B008
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backend: Backend = qiskit.Aer.get_backend("aer_simulator"), # noqa: B008
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) -> int:
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"""
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Quantum Equivalent of a Ripple Adder Circuit
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@ -104,7 +104,7 @@ def ripple_adder(
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for i in range(len(x) + 1):
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circuit.measure([(len(x) * 2) + i], [i])
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res = execute(circuit, backend, shots=1).result()
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res = qiskit.execute(circuit, backend, shots=1).result()
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# The result is in binary. Convert it back to int
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return int(list(res.get_counts())[0], 2)
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@ -6,25 +6,27 @@ finally prints the total count of the states finally observed.
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Qiskit Docs: https://qiskit.org/documentation/getting_started.html
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"""
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import qiskit as q
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import qiskit
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def single_qubit_measure(qubits: int, classical_bits: int) -> q.result.counts.Counts:
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def single_qubit_measure(
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qubits: int, classical_bits: int
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) -> qiskit.result.counts.Counts:
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"""
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>>> single_qubit_measure(1, 1)
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{'0': 1000}
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"""
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# Use Aer's qasm_simulator
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simulator = q.Aer.get_backend("qasm_simulator")
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# Use Aer's simulator
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simulator = qiskit.Aer.get_backend("aer_simulator")
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# Create a Quantum Circuit acting on the q register
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circuit = q.QuantumCircuit(qubits, classical_bits)
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circuit = qiskit.QuantumCircuit(qubits, classical_bits)
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# Map the quantum measurement to the classical bits
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circuit.measure([0], [0])
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# Execute the circuit on the qasm simulator
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job = q.execute(circuit, simulator, shots=1000)
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# Execute the circuit on the simulator
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job = qiskit.execute(circuit, simulator, shots=1000)
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# Return the histogram data of the results of the experiment.
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return job.result().get_counts(circuit)
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