Related papers: Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK

Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK

URL: http://arxiv.org/abs/2403.18662v1
Date: Wed, 27 Mar 2024 15:05:55 GMT
Title: Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
Authors: Florian J. Kiwit, Maximilian A. Wolf, Marwa Marso, Philipp Ross, Jeanette M. Lorenz, Carlos A. Riofrío, Andre Luckow,
Abstract summary: This paper investigates the scalability and noise resilience of quantum generative learning applications. We employ rigorous benchmarking techniques to track progress and identify challenges in scaling QML algorithms. We show that QGANs are not as affected by the curse of dimensionality as QCBMs and to which extent QCBMs are resilient to noise.
Score: 0.3624329910445628
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to scale quantum machine learning (QML) to industrial-level applications. This paper investigates the scalability and noise resilience of quantum generative learning applications. We consider the training performance in the presence of statistical noise due to finite-shot noise statistics and quantum noise due to decoherence to analyze the scalability of QML methods. We employ rigorous benchmarking techniques to track progress and identify challenges in scaling QML algorithms, and show how characterization of QML systems can be accelerated, simplified, and made reproducible when the QUARK framework is used. We show that QGANs are not as affected by the curse of dimensionality as QCBMs and to which extent QCBMs are resilient to noise.