Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

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.

arXiv commentClose to the published version

References in corpus (34)

• Quantum Computing in the NISQ era and beyond
• Fault-tolerant quantum computation by anyons
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum algorithms: an overview
• The power of quantum neural networks
• Power of data in quantum machine learning
• Connecting ansatz expressibility to gradient magnitudes and barren plateaus
• Quantum advantage in learning from experiments
• A rigorous and robust quantum speed-up in supervised machine learning
• ProjectQ: An Open Source Software Framework for Quantum Computing
• Everything You Always Wanted to Know About LOCC (But Were Afraid to Ask)
• t$|$ket$⟩$ : A Retargetable Compiler for NISQ Devices
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Interacting Quantum Observables: Categorical Algebra and Diagrammatics
• General parameter-shift rules for quantum gradients
• Effect of barren plateaus on gradient-free optimization
• Is quantum advantage the right goal for quantum machine learning?
• A Lambda Calculus for Quantum Computation
• Quantum machine learning for quantum anomaly detection
• Barren plateaus preclude learning scramblers
• Qibo: a framework for quantum simulation with hardware acceleration
• Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
• Towards understanding the power of quantum kernels in the NISQ era
• Realizing a dynamical topological phase in a trapped-ion quantum simulator
• Automatic design of quantum feature maps
• Quantum Volume in Practice: What Users Can Expect from NISQ Devices
• Quantum kernels for real-world predictions based on electronic health records
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Classical Concepts in Quantum Programming
• Noisy Quantum Kernel Machines

Cited by in corpus (6)

• Quantum Kernel Methods under Scrutiny: A Benchmarking Study
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Benchmarking quantum machine learning kernel training for classification tasks
• Automatic and effective discovery of quantum kernels
• A Study on Quantum Graph Neural Networks Applied to Molecular Physics
• Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data