Top Research Papers on Quantum Machine Learning

Since the processing power of quantum computers is significantly higher than classical computers, it is expected to predict earthquakes accurately and give an early warning to alert the locals residing in that area.

The findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility.

Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives. The way we do research is almost certainly no exception and ML, with its unprecedented ability to find hidden patterns in data, will be assisting future scientific discoveries. Quantum physics on the other side, even though it is sometimes not entirely intuitive, is one of the most successful physical theories and ...

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...

Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits (VQC) are used to develop QML architectures on noisy intermediate-scale quantum (NISQ) devices. We discuss machine learning for the quantum computing paradigm, showcasing ou...

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.

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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.

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.

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.

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.

This work presents an operational semantics of QML programs using quantum circuits, and a denotational semantics using superoperators, and introduces the language QML, a functional language for quantum computations on finite types.

This paper goes over the detailed design and framework of QMLS, including MLMG, MLMV, and QS, which aims to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD.

The inner arrangement of the quantum mechanics dataset QM9 is investigated in this study and methods for detecting outliers, clustering are used.

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.

A quantum k-means algorithm with a low time complexity of $\mathcal{O}(NK log(D)I/C)$ to apply it to the fundamental problem of discriminating quantum states at readout and shows cross-talk in the (1, 2) and (2, 3) neighboring qubit couples for the analyzed device.

An overview of quantum machine learning, quantum reinforcement learning, and the field of quantum biomimetics is given, describing the related research carried out by the scientific community.

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.

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.

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 work presents the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems and highlights the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

The distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training, and demonstrates a promising future research direction for scaling and privacy aspects.

An asymptotically optimal, machine learning based, adaptive single-photon phase estimation protocol that allows us to reach the standard quantum limit when a very limited number of photons is employed.

The state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning are presented.

Quantum computing and machine learning are two technologies that have generated unparalleled amounts of hype among the scientific community and popular press and are on a collision course with each other.

It is found that the FRQI format not only provides the best classification accuracy compared to NEQR and other popular encoding methods but also is consistent across different circuit depths of QNN.

This survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues.

Empirical results show that quantum neural networks can be trained efficiently and are implemented using Qiskit as a proof of concept and tested on a real quantum computer.

The integration of QML into machine learning workflows can lead to the development of advanced AI systems capable of personalized treatment recommendations, scientific discovery, and data-driven decision-making, thereby transforming the landscape of artificial intelligence and decision-making processes.

This chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work ofQML.

The next round of invited talks on quantum autoencoders for efficient compression of quantum data and quantum Hamiltonian learning using Bayesian inference on a quantum photonic simulator are presented.

Abstract

There is no doubt artificial intelligence and machine learning have been significantly modifying the scientific and technological landscape, as well as society at large, over the last decade. Search engines, autonomous cars, language and speech translators, as well as image classifiers are only some possibilities this discipline enables and there is certainly more to come in the near future. Significant research efforts have been dedicated in the direction of quantum computing, a topic that has been receiving ever-growing attention from governmental institutions, multinational companies, and a...

An automated search algorithm (QES), which derives optimal design of entangling layout for supervised quantum machine learning by establishing the connection between the structures of entanglement using CNOT gates and the representations of directed multi-graphs, enabling a well-defined search space.

A parallel quantum eigensolver for solving a set of machine learning problems, which is based on quantum multi-resonant transitions that simultaneously trigger multiple energy transitions in the systems on demand, and is hardware efficient, such that it is implementable in near-term quantum computers.

This work proposes an approach for the systematic treatment of machine learning, from the perspective of quantum information, and shows that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

This paper is considering a person identification problem based on an image of his face using the ensemble learning method and approaches to the optimization problems in the context of a training of machine learning models.

The Bayes optimal solutions for both strategies are presented, showing that there certainly exists a fully quantum matching procedure that is strictly superior to the straightforward semiclassical extension of the conventional matching strategy based on the learning process.

A data dependent projected quantum kernel was shown to provide significant advantage over classical kernels and results of investigations and ideas pertaining to extending the use of quantum kernels as a feature extraction layer in a Convolutional Neural Networks that is a widely used architecture in deep-learning applications are presented.

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.

It is shown that kernel principal component analysis and one-class support vector machine can be performed using resources logarithmic in the dimensionality of quantum states, which makes these algorithms potentially applicable to big quantum data applications.

This study has focused on surveying the enhancement of ML with QC, introducing QML concepts with current research advances and facing challenges in the new technology and future work considerations.

The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machin...

This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine 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.

The quantum dynamic equation (QDE) of machine learning is obtained based on Schrodinger equation and potential energy equivalence relationship, offering a theoretical framework for investigating machine learning iterations through quantum and mathematical theories.

An innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms and a proposal for the first version of a QML playground for learning and exploring QML models are proposed.

It is concluded that the 1 qubit has the worst performance at readout evidenced by the signal data not being visually separable and the low scores obtained on both clustering algorithms compared to the other qubits.

The results uncover the notable vulnerability of quantum machine learning systems to adversarial perturbations, which not only reveals a novel perspective in bridging machine learning and quantum physics in theory but also provides valuable guidance for practical applications of quantum classifiers based on both near-term and future quantum technologies.

A comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms is undertaken, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints.