Package: quask
QuASKQuantum Advantage Seeker with KernelQuASK 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.DocumentationThe documentation for QuASK can be accessed on the websiteRead The Docs.InstallationThe 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) withpip install quask.UsageUse quask as a library of software componentsQuASK can be used as a library to extend your own software. Check if everything's working with:importnumpyasnpimportquask.metricsA=np.array([[1,2],[3,4]])B=np.array([[5,6],[7,8]])print(quask.metrics.calculate_frobenius_inner_product(A,B))# 70Use quask as a command-line interface toolQuASK 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-datasetTo preprocess a dataset:$ python3.9 -m quask preprocess-datasetTo analyze a dataset using quantum and classical kernels:$ python3.9 -m quask apply-kernelTo 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 linearCreditsPlease 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}
}