Top Research Papers on Quantum Machine Learning

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.

The book series Quantum Science and Technology is dedicated to one of today's most active and rapidly expanding fields of research and development.In particular, the series will be a showcase for the growing number of experimental implementations and practical applications of quantum systems.These will include, but are not restricted to: quantum information processing, quantum computing, and quantum simulation; quantum communication and quantum cryptography; entanglement and other quantum resources; quantum interfaces and hybrid quantum systems; quantum memories and quantum repeaters; measurem...

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federa...

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.

It is argued that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.

A link between quantum machine learning classification and quantum hypothesis testing is established and it is shown that the accuracy and generalization capability of quantum classifiers depend on the (R\'enyi) mutual informations between the quantum state space $Q$ and the classical parameter space $X$ or class space $C$.

Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost th...

This paper reviews the state-of-the-art research of algorithms of quantum machine learning and shows a path of the research from the basic quantum information to quantum machine learning algorithms from the perspective of people in the perspective of computer science.

This work presents a simple, yet powerful, framework where the underlying invariances in the data are used to build QML models that, by construction, respect those symmetries that remain invariant under the action of any element of the symmetry group G associated to the dataset.

A view on the current state of affairs in this new exciting research field is offered, challenges of using ML in QC applications are described, and potential future developments are outlined.

A brief overview of the well-known techniques is presented but also their learning strategies using statistical physical insight to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

Two quantum machine learning models that leverage the principles of quantum mechanics for effective computations are introduced, enabling the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible.

The authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one, and propose a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.

Current methods and applications for quantum machine learning are reviewed, including differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning.

An overview of quantum machine learning in the light of classical approaches is presented, discussing various technical contributions, strengths and similarities of the research work in this domain and elaborate upon the recent progress of different quantum machinelearning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.

It is shown that machine learning can be used to identify central quantum protocols, including teleportation, entanglement purification and the quantum repeater, based on a model of a learning agent that combines reinforcement learning and decision making in a physically motivated framework.

An overview of the current state of knowledge in application of ML on QC is presented, and the speed up, and complexity advantages of using quantum machines are evaluated.

The first generation of ideas that use quantum machine learning on problems in HEP are reviewed and an outlook on future applications is provided.

It is proved that linear quantum models must utilize exponentially more qubits than data re-uploading models in order to solve certain learning tasks, while kernel methods additionally require exponentially more data points.

A method to extract the information about the unknown environment from a series of projective single-shot measurements on the system (without resorting to the process tomography) and the developed algorithm to learn unknown quantum environments enables one to efficiently control and manipulate quantum systems.

It is shown how equivariant gatesets can be used in variational quantum eigensolvers and benchmark the proposed methods on two toy problems that feature a non-trivial symmetry and observe a substantial increase in generalization performance.

In this Perspective, the authors review how machine learning, and more broadly methods of artificial intelligence, are utilized in advancing quantum technologies, specifically the design, control, calibration and optimization of quantum devices. They also discuss open challenges in the field and potential future directions within the next decade.

With near-term quantum devices available and the race for fault-tolerant quantum computers in full swing, researchers became interested in the question of what happens if we replace a supervised machine learning model with a quantum circuit. While such "quantum models" are sometimes called "quantum neural networks", it has been repeatedly noted that their mathematical structure is actually much more closely related to kernel methods: they analyse data in high-dimensional Hilbert spaces to which we only have access through inner products revealed by measurements. This technical manuscript summa...

In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered.

This study illustrates the steps needed to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.

It is proven that for any input distribution D(x), a classical ML model can provide accurate predictions on average by accessing E a number of times comparable to the optimal quantum ML model, and it is proved that the exponential quantum advantage is possible.

The proposed DL model has a high diabetes prediction accuracy as compared with the developed QML and existing state-of-the-art models and the performance of the QML model has been found as satisfactory and comparable with existing literature.

A general framework based on reinforcement learning to discover optimal thermodynamic cycles that maximize the power of out-of-equilibrium quantum heat engines and refrigerators is introduced.

The revised FCHL19 representation for atomic environments in molecules or condensed-phase systems is introduced, and when the revised FCHL19 representation is combined with the operator quantum machine learning regressor, forces and energies can be predicted in only a few milliseconds per atom.

This manuscript aims to present a Systematic Literature Review of the papers published between 2017 and 2023 to identify, analyze and classify the different algorithms used in quantum machine learning and their applications.

This work provides a comprehensive study of generalization performance in QML after training on a limited number N of training data points, and reports rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount ofTraining data.

A specially constructed algorithm shows that a formal quantum advantage is possible and is robust against additive errors in the kernel entries that arise from finite sampling statistics.

It is proved that classical ML algorithms can efficiently predict ground-state properties of gapped Hamiltonians after learning from other Hamiltonians in the same quantum phase of matter, under a widely accepted conjecture.

Abstract Carbon quantum dots (CQDs) have versatile applications in luminescence, whereas identifying optimal synthesis conditions has been challenging due to numerous synthesis parameters and multiple desired outcomes, creating an enormous search space. In this study, we present a novel multi-objective optimization strategy utilizing a machine learning (ML) algorithm to intelligently guide the hydrothermal synthesis of CQDs. Our closed-loop approach learns from limited and sparse data, greatly reducing the research cycle and surpassing traditional trial-and-error methods. Moreover, it also rev...

This work demonstrates how ML-based techniques can offer insight into the successful prediction, optimization and acceleration of CDs' synthesis process, as well as enhancing the process-related properties such as the fluorescent quantum yield (QY).

Machine learning has become a ubiquitous and effective technique for data processing and classification. Furthermore, due to the superiority and progress of quantum computing in many areas (e.g., cryptography, machine learning, healthcare), a combination of classical machine learning and quantum information processing has established a new field, called, quantum machine learning. One of the most frequently used applications of quantum computing is machine learning. This paper aims to present a comprehensive review of state-of-the-art advances in quantum machine learning. Besides, this paper ou...

The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation.

This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain.

Quantum machine learning with improved data efficiency and transferability has been achieved using on-the-fly selection of query-dependent training molecules, which are drawn from a ‘dictionary’ of atom-in-molecule-based fragments.