Version 1
: Received: 20 June 2023 / Approved: 7 July 2023 / Online: 7 July 2023 (07:35:19 CEST)
How to cite:
Atzarakis, K.; Kalogeris, I.; Stavroulakis, G.; Papadopoulos, V. Cross-pollinating Quantum Computing and Machine Learning for the Solution of Parameterized Problems in Computational Science and Engineering. Preprints2023, 2023070470. https://doi.org/10.20944/preprints202307.0470.v1
Atzarakis, K.; Kalogeris, I.; Stavroulakis, G.; Papadopoulos, V. Cross-pollinating Quantum Computing and Machine Learning for the Solution of Parameterized Problems in Computational Science and Engineering. Preprints 2023, 2023070470. https://doi.org/10.20944/preprints202307.0470.v1
Atzarakis, K.; Kalogeris, I.; Stavroulakis, G.; Papadopoulos, V. Cross-pollinating Quantum Computing and Machine Learning for the Solution of Parameterized Problems in Computational Science and Engineering. Preprints2023, 2023070470. https://doi.org/10.20944/preprints202307.0470.v1
APA Style
Atzarakis, K., Kalogeris, I., Stavroulakis, G., & Papadopoulos, V. (2023). Cross-pollinating Quantum Computing and Machine Learning for the Solution of Parameterized Problems in Computational Science and Engineering. Preprints. https://doi.org/10.20944/preprints202307.0470.v1
Chicago/Turabian Style
Atzarakis, K., George Stavroulakis and Vissarion Papadopoulos. 2023 "Cross-pollinating Quantum Computing and Machine Learning for the Solution of Parameterized Problems in Computational Science and Engineering" Preprints. https://doi.org/10.20944/preprints202307.0470.v1
Abstract
The potential of quantum computing for scientific and industrial breakthroughs is immense, however, we are still in the Noisy Intermediate-Scale Quantum (NISQ) era, where the currently available quantum devices contain small numbers of qubits, are very sensitive to environmental conditions and prone to quantum decoherence. Even so, existing NISQ computers have already been shown to outperform conventional computers on specific problems and the key question is how to make use of today’s NISQ devices to achieve quantum advantage in the field of computational science and engineering (CSE). In this direction, this work proposes a hybrid computing formulation by combining quantum computing with machine learning for accelerating the solution of parameterized linear systems in NISQ devices. In particular, it focuses on the Variational Quantum Linear Solver (VQLS), which is a hybrid quantum- classical algorithm for solving linear systems of equations. VQLS employs a short-depth quantum circuit to efficiently evaluate a cost function related to the system solution. The algorithm’s circuit consists of a quantum gate sequence (unitary operators) that involves a set of tunable parameters. Then, well-established classical optimizers are utilized to tune the parameters of the sequence in order to minimize the cost function, which is equivalent to finding the system solution at an acceptable level of accuracy. In this work, it is demonstrated that machine learning tools such as feed-forward neural networks and nearest-neighbor interpolation techniques can be successfully employed to accelerate the convergence of the VQLS algorithm towards the optimal values for the circuit parameters when applied to parameterized linear systems that need to be solved for multiple parameter instances.
Keywords
Quantum Computing; Quantum Linear System; Variational Quantum Linear Solver; Machine Learning; multi-query
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
