Artificial intelligence and machine learning for quantum technologies

M Krenn, J Landgraf, T Foesel, F Marquardt - Physical Review A, 2023‏ - APS
In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

Deep reinforcement learning in chemistry: A review

B Sridharan, A Sinha, J Bardhan… - Journal of …, 2024‏ - Wiley Online Library
Reinforcement learning (RL) has been applied to various domains in computational
chemistry and has found wide‐spread success. In this review, we first motivate the …

Filtering variational quantum algorithms for combinatorial optimization

D Amaro, C Modica, M Rosenkranz… - Quantum Science …, 2022‏ - iopscience.iop.org
Current gate-based quantum computers have the potential to provide a computational
advantage if algorithms use quantum hardware efficiently. To make combinatorial …

KANQAS: Kolmogorov-Arnold network for quantum architecture search

A Kundu, A Sarkar, A Sadhu - EPJ Quantum Technology, 2024‏ - epjqt.epj.org
Quantum architecture Search (QAS) is a promising direction for optimization and automated
design of quantum circuits towards quantum advantage. Recent techniques in QAS …

Quantum circuit synthesis with diffusion models

F Fürrutter, G Muñoz-Gil, HJ Briegel - Nature Machine Intelligence, 2024‏ - nature.com
Quantum computing has recently emerged as a transformative technology. Yet, its promised
advantages rely on efficiently translating quantum operations into viable physical …

Quantum architecture search via deep reinforcement learning

EJ Kuo, YLL Fang, SYC Chen - arxiv preprint arxiv:2104.07715, 2021‏ - arxiv.org
Recent advances in quantum computing have drawn considerable attention to building
realistic application for and using quantum computers. However, designing a suitable …

Quantumdarts: differentiable quantum architecture search for variational quantum algorithms

W Wu, G Yan, X Lu, K Pan… - … conference on machine …, 2023‏ - proceedings.mlr.press
With the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era and the fast
development of machine learning, variational quantum algorithms (VQA) including …

Variational quantum pulse learning

Z Liang, H Wang, J Cheng, Y Ding… - 2022 IEEE …, 2022‏ - ieeexplore.ieee.org
Quantum computing is among the most promising emerging techniques to solve problems
that are computationally intractable on classical hardware. A large body of existing works …

Training-free quantum architecture search

Z He, M Deng, S Zheng, L Li, H Situ - Proceedings of the AAAI …, 2024‏ - ojs.aaai.org
Variational quantum algorithm (VQA) derives advantages from its error resilience and high
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …

Physics-informed bayesian optimization of variational quantum circuits

K Nicoli, CJ Anders, L Funcke… - Advances in …, 2023‏ - proceedings.neurips.cc
In this paper, we propose a novel and powerful method to harness Bayesian optimization for
variational quantum eigensolvers (VQEs)-a hybrid quantum-classical protocol used to …