Quantum Advantage Seeker with Kernels (QuASK): a software framework to
speed up the research in quantum machine learning
- URL: http://arxiv.org/abs/2206.15284v2
- Date: Mon, 23 Oct 2023 15:52:03 GMT
- Title: Quantum Advantage Seeker with Kernels (QuASK): a software framework to
speed up the research in quantum machine learning
- Authors: Francesco Di Marcantonio, Massimiliano Incudini, Davide Tezza and
Michele Grossi
- Abstract summary: QuASK is an open-source quantum machine learning framework written in Python.
It implements most state-of-the-art algorithms to analyze the data through quantum kernels.
It can be used as a command-line tool to download datasets, pre-process them, quantum machine learning routines, analyze and visualize the results.
- Score: 0.9217021281095907
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Exploiting the properties of quantum information to the benefit of machine
learning models is perhaps the most active field of research in quantum
computation. This interest has supported the development of a multitude of
software frameworks (e.g. Qiskit, Pennylane, Braket) to implement, simulate,
and execute quantum algorithms. Most of them allow us to define quantum
circuits, run basic quantum algorithms, and access low-level primitives
depending on the hardware such software is supposed to run. For most
experiments, these frameworks have to be manually integrated within a larger
machine learning software pipeline. The researcher is in charge of knowing
different software packages, integrating them through the development of long
code scripts, analyzing the results, and generating the plots. Long code often
leads to erroneous applications, due to the average number of bugs growing
proportional with respect to the program length. Moreover, other researchers
will struggle to understand and reproduce the experiment, due to the need to be
familiar with all the different software frameworks involved in the code
script. We propose QuASK, an open-source quantum machine learning framework
written in Python that aids the researcher in performing their experiments,
with particular attention to quantum kernel techniques. QuASK can be used as a
command-line tool to download datasets, pre-process them, quantum machine
learning routines, analyze and visualize the results. QuASK implements most
state-of-the-art algorithms to analyze the data through quantum kernels, with
the possibility to use projected kernels, (gradient-descent) trainable quantum
kernels, and structure-optimized quantum kernels. Our framework can also be
used as a library and integrated into pre-existing software, maximizing code
reuse.
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