Raijeku / discriminating-quantum-states

Discriminating Quantum States with Quantum Machine Learning. This repository contains all the source code used to generate results presented in "Discriminating Quantum States with Quantum Machine Learning".

Discriminating Quantum States with Quantum Machine Learning

This repository contains all the source code used to generate results presented in "Discriminating Quantum States with Quantum Machine Learning".

Contents

• qkmeans.py: Module for quantum k-means algorithm with a class containing sk-learn style functions resembling the k-means algorithm.
• dataset.ipynb: Code for retrieval of in-phase and quadrature (IQ) signal data from IBMQ Bogota after applying pulses that drive qubits to the |0> and |1> states. Arrays of signal data are retrieved from |00>, |01>, |10> and |11> prepared state schedules for all qubit couples in a quantum device.
• classical_correlation.ipynb: Classical correlation analysis for IBMQ Bogota using Pearson Correlation coefficients and the k-means algorithm.
• quantum_correlation.ipynb: Quantum correlation analysis for IBMQ Bogota using Pearson Correlation coefficients and the qk-means algorithm.

Usage example

Example code for use of the qk-means algorithm:

import numpy as np
import pandas as pd
from qkmeans import *

backend = IBMQ.load_account().get_backend('ibmq_qasm_simulator')
X = pd.DataFrame(np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]))
qk_means = QuantumKMeans(backend, n_clusters=2, verbose=True, map_type='angle')
qk_means.fit(X)
print(qk_means.labels_) 

Authors

David Quiroga, Prasanna Date, Raphael Pooser.

If you are doing any research using this source code, please cite the following paper:

David Quiroga, Prasanna Date, Raphael Pooser. Discriminating Quantum States with Quantum Machine Learning. arXiv, 2021. arXiv:2112.00313

License

This source code is free and open source, released under the Apache License, Version 2.0.