QuASK

Quantum Advantage Seeker with Kernel

QuASK is a quantum machine learning software written in Python that supports researchers in designing, experimenting, and assessing different quantum and classic kernels performance. This software is package agnostic and can be integrated with all major quantum software packages (e.g. IBM Qiskit, Xanadu’s Pennylane, Amazon Braket).

QuASK guides the user through a simple preprocessing of input data, definition and calculation of quantum and classic kernels, either custom or pre-defined ones. From this evaluation the package provide an assessment about potential quantum advantage and prediction bounds on generalization error.

Beyond theoretical framing, it allows for the generation of parametric quantum kernels that can be trained using gradient-descent-based optimization, grid search, or genetic algorithms. Projected quantum kernels, an effective solution to mitigate the curse of dimensionality induced by the exponential scaling dimension of large Hilbert spaces, is also calculated. QuASK can also generate the observable values of a quantum model and use them to study the prediction capabilities of the quantum and classical kernels.

The initial release is accompanied by the journal article "QuASK - Quantum Advantage Seeker with Kernels" available on arxiv.org.

Documentation

The documentation for QuASK can be accessed on the website Read The Docs.

Installation

The software has been tested on Python 3.9.10. We recommend using this version or a newer one.

The library is available on the Python Package Index (PyPI) with pip install quask.

Usage

Use quask as a library of software components

QuASK can be used as a library to extend your own software. Check if everything's working with:

import numpy as np
import quask.metrics
A = np.array([[1,2], [3,4]])
B = np.array([[5,6], [7,8]])
print(quask.metrics.calculate_frobenius_inner_product(A, B))  # 70

Use quask as a command-line interface tool

QuASK can be used as a command-line interface to analyze the dataset with the kernel methods. These are the commands implemented so far.

To retrieve the datasets available:

$ python3.9 -m quask get-dataset

To preprocess a dataset:

$ python3.9 -m quask preprocess-dataset

To analyze a dataset using quantum and classical kernels:

$ python3.9 -m quask apply-kernel

To create some plot of the property related to the generated Gram matrices:

$ python3.9 -m quask plot-metric --metric accuracy --train-gram training_linear_kernel.npy --train-y Y_train.npy --test-gram testing_linear_kernel.npy --test-y Y_test.npy --label linear

Credits

Please cite the work using the following Bibtex entry:

@article{https://doi.org/10.48550/arxiv.2206.15284,
  doi = {10.48550/ARXIV.2206.15284},
  url = {https://arxiv.org/abs/2206.15284},
  author = {Di Marcantonio, Francesco and Incudini, Massimiliano and Tezza, Davide and Grossi, Michele},
  keywords = {Quantum Physics (quant-ph), Machine Learning (cs.LG), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {QuASK -- Quantum Advantage Seeker with Kernels},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}