Top Research Papers on Quantum Machine Learning

QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained.

This chapter introduces the fundamentals of QML and provides a brief overview of the recent progress and future trends in the field of QML, and highlights key opportunities for achieving quantum advantage in ML tasks, as well as describe some open challenges currently facing the field of QML.

This special session includes six high-quality papers dealing with some of the most relevant aspects of QML, including analysis of learning in quantum computing and quantum annealers, quantum versions of classical ML models –like neural networks or learning vector quantization–, and quantum learning approaches for measurement and control.

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We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.

The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.

This work gives an example based on a quantum optical perceptron where the energy is dissipated as spontaneous emission at optical frequencies so this perceptron is as efficient as it is possible to get.

It is demonstrated that the quantum learning machine learns Deutsch’s task and develops itself a quantum algorithm, that is different from but equivalent to the original one.

An artificial neural network is used to represent the wave function of a quantum many-body system and to make the neural network "learn" what the ground state (or dynamics) of the system is, and is found to perform better than the current state-of-the-art numerical simulation methods.

A short review on the recent development and adaptation of machine learning ideas for the purpose of advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems.

This paper presents the basis for future research work related to development of quantum algorithms in the domain of machine learning and provides an insight on the foundational principles, essential algorithms, various associated platforms, potential applications and challenges being faced by QML.

Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage ...

The current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models are examined, and the importance of interdisciplinary collaboration in the continued advancement of this field is emphasized.

This paper first understands the mathematical intuition for the implementation of quantum feature space and successfully simulates quantum properties and algorithms like Fidelity and Grover's Algorithm via the Qiskit python library and the IBM Quantum Experience platform.

This paper first understands the mathematical intuition for the implementation of quantum feature space and successfully simulates quantum properties and algorithms like Fidelity and Grover's Algorithm via the Qiskit python library and the IBM Quantum Experience platform.

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. This is done by considering software selection approaches, as wel...

The findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets.

This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge.

This article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.