How to use quantum_inspired_algorithms

This package provides quantum-inspired algorithms for linear regression. It assumes a linear system of the form Ax=b, where A is the training data, x is a vector of unknown coefficients, and b is a vector of target values.

The class estimator.QILinearEstimator provides three methods: fit, predict_x, and predict_b. Once the fit method has been called using A and b, predict_x can be used to sample entries of the estimated coefficient vector. Alternatively, predict_b can be used to sample entries of predictions corresponding to (un)observed target values.

The project setup is documented in project_setup.md.

Installation

To install quantum_inspired_algorithms from GitHub repository, do:

git clone git@github.com:QuantumApplicationLab/quantum-inspired-algorithms.gitcd quantum-inspired-algorithmspython -m pip install .

Example

importnumpyasnpfromsklearn.datasetsimportmake_low_rank_matrixfromsklearn.model_selectionimporttrain_test_splitfromquantum_inspired_algorithms.estimatorimportQILinearEstimatorrng=np.random.RandomState(7)
# Generate example datam=700n=100A=make_low_rank_matrix(n_samples=m, n_features=n, effective_rank=3, random_state=rng, tail_strength=0.1)
x=rng.normal(0, 1, A.shape[1])
b=A @ x# Create training and test datasetsA_train, A_test, b_train, b_test=train_test_split(A, b, test_size=0.3, random_state=rng)
# Fit quantum-inspired modelrank=3r=100c=30n_samples=100# for Monte Carlo methodsqi=QILinearEstimator(r, c, rank, n_samples, rng, sketcher_name="fkv")
qi=qi.fit(A_train, b_train)
# Sample from b (vector of predictions)n_entries_b=1000sampled_indices_b, sampled_b=qi.predict_b(A_test, n_entries_b)

More examples can be found in the tests directory.

Contributing

If you want to contribute to the development of quantum_inspired_algorithms, have a look at the contribution guidelines.

Credits

The algorithms found in this repository have been developed by the Quantum Application Lab and have been based on:

• https://github.com/XanaduAI/quantum-inspired-algorithms
• "Quantum-inspired algorithms in practice", by Juan Miguel Arrazola, Alain Delgado, Bhaskar Roy Bardhan, and Seth Lloyd. 2020-08-13, volume 4, page 307. Quantum 4, 307 (2020).
• "Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension", by András Gilyén, Seth Lloyd, Ewin Tang. (2018). ArXiv, abs/1811.04909.

This work was supported by the Dutch National Growth Fund (NGF), as part of the Quantum Delta NL programme.

This package was created with Cookiecutter and the NLeSC/python-template.