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.