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Quantum machine learning for chemistry and physics

CHEMICAL SOCIETY REVIEWS (2022)

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Quantum Machine Learning Algorithms for Drug Discovery Applications

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Convergence of a Reconstructed Density Matrix to a Pure State Using the Maximal Entropy Approach

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Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Artificial Neural Networks

Bipeng Wang et al.

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Dependence between Structural and Electronic Properties of CsPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics

Spencer M. Mangan et al.

Summary: Using unsupervised machine learning on trajectories from a nonadiabatic molecular dynamics simulation with time-dependent Kohn-Sham density functional theory, we elucidated the structural parameters influencing nonradiative recombination in CsPbI3. The I-I-I angles between PbI6 octahedra and the Cs-I distance have the strongest impact on bandgap and nonadiabatic coupling, even though Cs does not contribute to electron and hole wave functions. The surprising finding that nonadiabatic coupling is most influenced by static properties suggests new insights into the process.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2021)

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Application of quantum machine learning using the quantum variational classifier method to high energy physics analysis at the LHC on IBM quantum computer simulator and hardware with 10 qubits

Sau Lan Wu et al.

Summary: This study demonstrates the potential of quantum machine learning in differentiating between signal and background in realistic physics datasets, with promising results obtained on both quantum simulator and hardware. The use of quantum machine learning in future high-luminosity LHC physics analyses, including Higgs boson self-couplings measurements and dark matter searches, is foreseen.

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Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

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NATURE (2021)

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Structures of rhodopsin in complex with G-protein-coupled receptor kinase 1

Qiuyan Chen et al.

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NATURE (2021)

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A rigorous and robust quantum speed-up in supervised machine learning

Yunchao Liu et al.

Summary: Research shows that heuristic quantum kernel methods can achieve quantum speed-up with only classical data access, and a classification problem is constructed to demonstrate this.

NATURE PHYSICS (2021)

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Quantum Simulation with Hybrid Tensor Networks

Xiao Yuan et al.

Summary: The paper introduces the framework of hybrid tensor networks which utilize measurable quantum states and classically contractable tensors. Through examples, it demonstrates the possibility of efficient quantum simulation on a quantum computer for solving large practical problems.

PHYSICAL REVIEW LETTERS (2021)

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Quantum Principal Component Analysis Only Achieves an Exponential Speedup Because of Its State Preparation Assumptions

Ewin Tang

Summary: The study introduces a new classical algorithm input model that captures the features and nuances of quantum linear algebra algorithms. Through this model, the authors describe classical analogs to quantum algorithms for principal component analysis and nearest-centroid clustering.

PHYSICAL REVIEW LETTERS (2021)

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Article Quantum Science & Technology

Adaptive quantum state tomography with neural networks

Yihui Quek et al.

Summary: NAQT is a fast and flexible quantum state tomography algorithm based on machine learning that integrates measurement adaptation and statistical inference, providing orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy.

NPJ QUANTUM INFORMATION (2021)

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Article Quantum Science & Technology

Experimental quantum kernel trick with nuclear spins in a solid

Takeru Kusumoto et al.

Summary: The research demonstrates experimental quantum kernel machine learning using spins in solid state, and obtaining kernel values through nuclear magnetic resonance, achieving a scheme where the dimension of feature space greatly exceeds the number of data.

NPJ QUANTUM INFORMATION (2021)

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Article Multidisciplinary Sciences

Resonant quantum principal component analysis

Zhaokai Li et al.

Summary: A resonant analysis algorithm with minimal resource has been developed to efficiently extract principal components of a density matrix in a quantum system. Experimental results demonstrate the effectiveness of the algorithm, showing the speedup ability of quantum algorithms in data dimension reduction.

SCIENCE ADVANCES (2021)

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Article Optics

Success of digital adiabatic simulation with large Trotter step

Changhao Yi

Summary: This study addresses the error analysis problem in quantum simulation of adiabatic process using Trotter formulas. The circuit depth can be linear in simulation time T with additional conditions. The fidelity error can't be estimated by the norm distance between evolution operators, which highlights the robustness of discretization in digital adiabatic simulation.

PHYSICAL REVIEW A (2021)

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Article Quantum Science & Technology

Learnability of Quantum Neural Networks

Yuxuan Du et al.

Summary: The study examines the learning potential of parameterized quantum circuits with gradient-based classical optimizers on noisy intermediate-scale quantum devices and finds that high gate noise, limited quantum measurements, and deep circuit depth may result in slower learning. It also proves that certain concept classes can be efficiently learned through this method.

PRX QUANTUM (2021)

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Article Quantum Science & Technology

Generalization in Quantum Machine Learning: A Quantum Information Standpoint

Leonardo Banchi et al.

Summary: Quantum classification and hypothesis testing are closely related subjects, with the ability to predict the correct class of previously unseen states depending on the mutual information between quantum state space and classical parameter space. Factors such as the dimension of the Hilbert space, noise level, and neglected information from classical data affect classification accuracy and generalization. The introduction of a quantum version of the information bottleneck principle allows for exploration of trade-offs between accuracy and generalization.

PRX QUANTUM (2021)

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Review Biochemistry & Molecular Biology

A review on machine learning approaches and trends in drug discovery

Paula Carracedo-Reboredo et al.

Summary: In recent years, machine learning techniques have been widely used in drug discovery to improve efficiency and reduce costs. To achieve the goals set by the Precision Medicine initiative, higher requirements have been proposed for the robustness, standardization, and reproducibility of computational methods.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

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Article Computer Science, Interdisciplinary Applications

Efficient parallelization of tensor network contraction for simulating quantum computation

Cupjin Huang et al.

Summary: The algorithmic framework efficiently parallelizes tensor network contraction through index slicing, achieving significant acceleration in quantum computation simulation. It not only aids in the development of quantum algorithms and error correction, but may also find further applications in computational science.

NATURE COMPUTATIONAL SCIENCE (2021)

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Review Physics, Applied

Variational quantum algorithms

M. Cerezo et al.

Summary: Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.

NATURE REVIEWS PHYSICS (2021)

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Article Quantum Science & Technology

Maximal Entropy Approach for Quantum State Tomography

Rishabh Gupta et al.

Summary: Quantum computation has been rapidly growing, with companies like IBM, Google, and IonQ developing quantum computing devices with a large number of qubits. However, the noise and limitations in these systems make traditional quantum tomography impractical. An alternative approach based on maximal information entropy is proposed to predict values of unknown observables and reconstruct the density matrix with high fidelity, even with missing observable results. A practical method is needed for inferring the quantum state using only partial information.

PRX QUANTUM (2021)

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Article Computer Science, Artificial Intelligence

Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

Thomas Hubregtsen et al.

Summary: The research shows a moderate to strong correlation between a circuit's ability to uniformly address the Hilbert space and classification accuracy in quantum machine learning. In contrast, there is a weak correlation between entangling capability and classification accuracy in a similar setup. Future work will focus on evaluating these correlations for different circuit designs.

QUANTUM MACHINE INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Universal discriminative quantum neural networks

H. Chen et al.

Summary: Recent research shows the success of training quantum circuits to classify quantum data despite the fundamental limitations of deterministic discrimination in quantum mechanics. By balancing error rates and inconclusiveness, the method achieves performance close to optimal values and generalizes well to unseen quantum data. This distinguishes the research from previous results and presents a unique quantum machine learning task without a classical analogue.

QUANTUM MACHINE INTELLIGENCE (2021)

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Review Physics, Applied

Physics-informed machine learning

George Em Karniadakis et al.

Summary: Physics-informed learning seamlessly integrates data and mathematical models through neural networks or kernel-based regression networks for accurate inference of realistic and high-dimensional multiphysics problems. Challenges remain in incorporating noisy data seamlessly, complex mesh generation, and addressing high-dimensional problems.

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Article Optics

Learning temporal data with a variational quantum recurrent neural network

Yuto Takaki et al.

Summary: The proposed method utilizes a parametrized quantum circuit that has a similar structure to recurrent neural networks for learning temporal data. Through numerical simulations, it shows potential capabilities for predicting cosine and triangular waves, as well as quantum spin dynamics tasks.

PHYSICAL REVIEW A (2021)

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Review Chemistry, Multidisciplinary

Ab initio nonadiabatic molecular dynamics of charge carriers in metal halide perovskites

Wei Li et al.

Summary: This review summarizes recent theoretical investigations of excited state dynamics in metal halide perovskites, using a state-of-the-art methodology combining nonadiabatic molecular dynamics with real-time time-dependent density functional theory. The simulations allow for the study of charge carrier evolution at the ab initio level and in direct connection with time-resolved spectroscopy experiments, providing atomistic insights into excited state dynamics under realistic conditions for the design of advanced solar energy and optoelectronic devices.

NANOSCALE (2021)

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Article Computer Science, Interdisciplinary Applications

The power of quantum neural networks

Amira Abbas et al.

Summary: This study investigates the advantage of near-term quantum computers for machine learning tasks by comparing the power and trainability of quantum machine learning models with classical neural networks. The effective dimension, a data-dependent measure based on Fisher information, is proposed to evaluate a model's ability to generalize on new data. Numerical demonstrations show that quantum neural networks outperform comparable feedforward networks in effective dimension and training speed, indicating an advantage for quantum machine learning validated on real quantum hardware.

NATURE COMPUTATIONAL SCIENCE (2021)

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Article Physics, Multidisciplinary

Neural-network variational quantum algorithm for simulating many-body dynamics

Chee Kong Lee et al.

Summary: The proposed algorithm combines neural networks and variational quantum methods to simulate the time evolution of quantum many-body systems. It can be efficiently implemented on near-term quantum computers and addresses the issue of vanishing gradient using strategies like ancilla qubits and stochastic Schrodinger equation. Numerical simulations show that the algorithm accurately captures the dynamics of closed and open quantum many-body systems.

PHYSICAL REVIEW RESEARCH (2021)

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Article Physics, Multidisciplinary

Machine learning time-local generators of open quantum dynamics

Paolo P. Mazza et al.

Summary: In the study of closed many-body quantum systems, one often focuses on the evolution of a subset of degrees of freedom, which can be approached by decomposing the problem into a bath and a system. The evolution of the reduced state of the system is typically governed by a quantum master equation with a time-independent generator. Utilizing a neural network function approximator, it is possible to predict open quantum dynamics described by time-local generators from an underlying unitary dynamics, providing insight into unseen dynamics and thermalization in closed quantum systems.

PHYSICAL REVIEW RESEARCH (2021)

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Review Chemistry, Multidisciplinary

A review on the stability of inorganic metal halide perovskites: challenges and opportunities for stable solar cells

Wanchun Xiang et al.

Summary: Inorganic perovskite based solar cells have garnered global attention for their high thermal stability and theoretical power conversion efficiencies. However, their poorer phase stability compared to hybrid counterparts requires further research. This review summarizes the progress in stability research on inorganic perovskites, focusing on degradation mechanisms towards temperature, moisture, and oxygen.

ENERGY & ENVIRONMENTAL SCIENCE (2021)

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Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

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Article Optics

Effect of data encoding on the expressive power of variational quantum-machine-learning models

Maria Schuld et al.

Summary: This research reveals that by repeating simple data-encoding gates, quantum models can access increasingly rich frequency spectra, and some quantum models are able to realize all possible sets of Fourier coefficients, making them universal function approximators.

PHYSICAL REVIEW A (2021)

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Article Quantum Science & Technology

Real- and Imaginary-Time Evolution with Compressed Quantum Circuits

Sheng-Hsuan Lin et al.

Summary: The current generation of noisy intermediate-scale quantum computers introduces new opportunities for studying quantum many-body systems. It is shown in this paper that quantum circuits can provide a more efficient representation of quantum states generated under nonequilibrium quantum dynamics compared to current classical numerics. By benchmarking algorithms on classical computers and implementing (classically optimized) gates on a quantum processing unit, the authors demonstrate the effectiveness of their algorithm in capturing real-time evolution.

PRX QUANTUM (2021)

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Article Biochemistry & Molecular Biology

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

Abhinit Kumar et al.

Summary: This study aims to discover new small molecule inhibitors for treating COVID-19 by analyzing the chemical space. The analysis of the anticorona dataset demonstrates diverse chemical scaffolds but lack of drug-like properties.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)

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Article Quantum Science & Technology

Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

Jonathan Romero et al.

Summary: A hybrid quantum-classical approach for modeling continuous classical probability distributions using a variational quantum circuit was proposed. The quantum generator architecture includes encoding classical random variables into quantum states and training a parameterized quantum circuit to mimic the target distribution. The model can interface smoothly with classical functions like neural networks, and is trained using an adversarial learning approach.

ADVANCED QUANTUM TECHNOLOGIES (2021)

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Review Chemistry, Medicinal

An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation

Andy H. Vo et al.

CHEMICAL RESEARCH IN TOXICOLOGY (2020)

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Article Medical Informatics

Development of a targeted client communication intervention to women using an electronic maternal and child health registry: a qualitative study

Binyam Bogale et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2020)

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Article Chemistry, Physical

FCHL revisited: Faster and more accurate quantum machine learning

Anders S. Christensen et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

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Article Optics

TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion

Zhengchun Liu et al.

JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION (2020)

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Article Mathematics, Applied

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network

Alex Sherstinsky

PHYSICA D-NONLINEAR PHENOMENA (2020)

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Article Quantum Science & Technology

Supervised learning with a quantum classifier using multi-level systems

Soumik Adhikary et al.

QUANTUM INFORMATION PROCESSING (2020)

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Article Multidisciplinary Sciences

U1 snRNP regulates cancer cell migration and invasion in vitro

Jung-Min Oh et al.

NATURE COMMUNICATIONS (2020)

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Inverse design of porous materials using artificial neural networks

Baekjun Kim et al.

SCIENCE ADVANCES (2020)

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Article Multidisciplinary Sciences

Re-epithelialization and immune cell behaviour in an ex vivo human skin model

Ana Rakita et al.

SCIENTIFIC REPORTS (2020)

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Article Physics, Applied

Phase diagrams classification based on machine learning and phenomenological investigation of physical properties in K1-xNaxNbO3 thin films

Duansheng Liu et al.

JOURNAL OF APPLIED PHYSICS (2020)

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Article Quantum Science & Technology

Experimental neural network enhanced quantum tomography

Adriano Macarone Palmieri et al.

NPJ QUANTUM INFORMATION (2020)

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Perspectives of quantum annealing: methods and implementations

Philipp Hauke et al.

REPORTS ON PROGRESS IN PHYSICS (2020)

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Machine learning in materials genome initiative: A review

Yingli Liu et al.

JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY (2020)

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Article Chemistry, Physical

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Julia Westermayr et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2020)

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Article Multidisciplinary Sciences

Fermionic neural-network states for ab-initio electronic structure

Kenny Choo et al.

NATURE COMMUNICATIONS (2020)

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Quantum classifier with tailored quantum kernel

Carsten Blank et al.

NPJ QUANTUM INFORMATION (2020)

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Review Biochemistry & Molecular Biology

A Review on Applications of Computational Methods in Drug Screening and Design

Xiaoqian Lin et al.

MOLECULES (2020)

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Article Chemistry, Physical

Structural Deformation Controls Charge Losses in MAPbI3: Unsupervised Machine Learning of Nonadiabatic Molecular Dynamics

Guoqing Zhou et al.

ACS ENERGY LETTERS (2020)

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Article Chemistry, Physical

Completing density functional theory by machine learning hidden messages from molecules

Ryo Nagai et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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A practical and efficient approach for Bayesian quantum state estimation

Joseph M. Lukens et al.

NEW JOURNAL OF PHYSICS (2020)

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Quantum Overlapping Tomography

Jordan Cotler et al.

PHYSICAL REVIEW LETTERS (2020)

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Article Physics, Multidisciplinary

Machine Learning Non-Markovian Quantum Dynamics

I. A. Luchnikov et al.

PHYSICAL REVIEW LETTERS (2020)

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Hybrid Quantum-Classical Neural Network for Calculating Ground State Energies of Molecules

Rongxin Xia et al.

ENTROPY (2020)

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Article Multidisciplinary Sciences

Molecular docking with Gaussian Boson Sampling

Leonardo Banchi et al.

SCIENCE ADVANCES (2020)

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Article Chemistry, Physical

Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning

Pedro Borlido et al.

NPJ COMPUTATIONAL MATERIALS (2020)

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Article Physics, Multidisciplinary

Tensor network investigation of the double layer Kagome compound Ca10Cr7O28

Augustine Kshetrimayum et al.

ANNALS OF PHYSICS (2020)

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Predicting many properties of a quantum system from very few measurements

Hsin-Yuan Huang et al.

NATURE PHYSICS (2020)

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Quantum Algorithms for Quantum Chemistry and Quantum Materials Science

Bela Bauer et al.

CHEMICAL REVIEWS (2020)

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Deep Learning the Hohenberg-Kohn Maps of Density Functional Theory

Javier Robledo Moreno et al.

PHYSICAL REVIEW LETTERS (2020)

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Article Physics, Multidisciplinary

Variational Quantum Simulation of General Processes

Suguru Endo et al.

PHYSICAL REVIEW LETTERS (2020)

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Article Physics, Applied

Machine learning formation enthalpies of intermetallics

Zhaohan Zhang et al.

JOURNAL OF APPLIED PHYSICS (2020)

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Article Physics, Applied

Application of machine learning methods for predicting new superhard materials

Efim Mazhnik et al.

JOURNAL OF APPLIED PHYSICS (2020)

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Article Chemistry, Multidisciplinary

Quantum machine learning using atom-in-molecule-based fragments selected on the fly

Bing Huang et al.

NATURE CHEMISTRY (2020)

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Article Chemistry, Multidisciplinary

Deep-neural-network solution of the electronic Schrodinger equation

Jan Hermann et al.

NATURE CHEMISTRY (2020)

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Article Astronomy & Astrophysics

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

Asad Khan et al.

PHYSICS LETTERS B (2020)

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Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy

Ingrid C. Romero et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

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Article Multidisciplinary Sciences

Hartree-Fock on a superconducting qubit quantum computer

SCIENCE (2020)

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Article Multidisciplinary Sciences

Quantum chemical accuracy from density functional approximations via machine learning

Mihail Bogojeski et al.

NATURE COMMUNICATIONS (2020)

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Editorial Material Multidisciplinary Sciences

Retrospective on a decade of machine learning for chemical discovery

O. Anatole von Lilienfeld et al.

NATURE COMMUNICATIONS (2020)

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Article Quantum Science & Technology

Transfer learning in hybrid classical-quantum neural networks

Andrea Mari et al.

QUANTUM (2020)

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Article Physics, Multidisciplinary

Nonadiabatic Molecular Quantum Dynamics with Quantum Computers

Pauline J. Ollitrault et al.

PHYSICAL REVIEW LETTERS (2020)

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Article Physics, Multidisciplinary

Tensor network wave function of S = 1 Kitaev spin liquids

Hyun-Yong Lee et al.

PHYSICAL REVIEW RESEARCH (2020)

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Article Materials Science, Multidisciplinary

Time evolution of many-body localized systems in two spatial dimensions

A. Kshetrimayum et al.

PHYSICAL REVIEW B (2020)

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Article Materials Science, Multidisciplinary

Topological quantum phase transitions retrieved through unsupervised machine learning

Yanming Che et al.

PHYSICAL REVIEW B (2020)

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Article Quantum Science & Technology

Data re-uploading for a universal quantum classifier

Adrian Perez-Salinas et al.

QUANTUM (2020)

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Review Chemistry, Multidisciplinary

Machine learning the ropes: principles, applications and directions in synthetic chemistry

Felix Strieth-Kalthoff et al.

CHEMICAL SOCIETY REVIEWS (2020)

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Quantum certification and benchmarking

Jens Eisert et al.

NATURE REVIEWS PHYSICS (2020)

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Review Computer Science, Artificial Intelligence

Convolutional neural network: a review of models, methodologies and applications to object detection

Anamika Dhillon et al.

PROGRESS IN ARTIFICIAL INTELLIGENCE (2020)

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Article Chemistry, Organic

Optimizing chemical reaction conditions using deep learning: a case study for the Suzuki-Miyaura cross-coupling reaction

Zunyun Fu et al.

ORGANIC CHEMISTRY FRONTIERS (2020)

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Article Computer Science, Interdisciplinary Applications

Computer Vision for Autonomous Vehicles

Joel Janai et al.

FOUNDATIONS AND TRENDS IN COMPUTER GRAPHICS AND VISION (2020)

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Article Optics

Machine learning phase transitions with a quantum processor

A. Uvarov et al.

PHYSICAL REVIEW A (2020)

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Article Optics

Encoding of matrix product states into quantum circuits of one- and two-qubit gates

Shi-Ju Ran

PHYSICAL REVIEW A (2020)

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Article Optics

Circuit-centric quantum classifiers

Maria Schuld et al.

PHYSICAL REVIEW A (2020)

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Article Materials Science, Multidisciplinary

Tensor network strategies for calculating biexcitons and trions in monolayer two-dimensional materials beyond the ground state

Sandra C. Kuhn et al.

PHYSICAL REVIEW B (2020)

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Article Optics

Digital-analog quantum computation

Adrian Parra-Rodriguez et al.

PHYSICAL REVIEW A (2020)

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Article Computer Science, Interdisciplinary Applications

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

Stefan Chmiela et al.

COMPUTER PHYSICS COMMUNICATIONS (2019)

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Article Chemistry, Physical

Training Neural Nets To Learn Reactive Potential Energy Surfaces Using Interactive Quantum Chemistry in Virtual Reality

Silvia Amabilino et al.

JOURNAL OF PHYSICAL CHEMISTRY A (2019)

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Article Chemistry, Physical

Quantum computing methods for electronic states of the water molecule

Teng Bian et al.

MOLECULAR PHYSICS (2019)

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Article Multidisciplinary Sciences

Supervised learning with quantum-enhanced feature spaces

Vojtech Havlicek et al.

NATURE (2019)

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Quantum Algorithms for Systems of Linear Equations Inspired by Adiabatic Quantum Computing

Yigit Subasi et al.

PHYSICAL REVIEW LETTERS (2019)

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Article Physics, Multidisciplinary

Tensor Network Annealing Algorithm for Two-Dimensional Thermal States

A. Kshetrimayum et al.

PHYSICAL REVIEW LETTERS (2019)

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Article Physics, Multidisciplinary

Quantum Machine Learning in Feature Hilbert Spaces

Maria Schuld et al.

PHYSICAL REVIEW LETTERS (2019)

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Article Multidisciplinary Sciences

Artificial neural networks for density-functional optimizations in fermionic systems

Caio A. Custodio et al.

SCIENTIFIC REPORTS (2019)

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Article Multidisciplinary Sciences

Quantum generative adversarial learning in a superconducting quantum circuit

Ling Hu et al.

SCIENCE ADVANCES (2019)

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Article Engineering, Electrical & Electronic

Teeth category classification via seven-layer deep convolutional neural network with max pooling and global average pooling

Zhi Li et al.

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY (2019)

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Article Physics, Multidisciplinary

Improved Handwritten Digit Recognition using Quantum K-Nearest Neighbor Algorithm

Yuxiang Wang et al.

INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS (2019)

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Article Chemistry, Physical

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

Oliver T. Unke et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Review Computer Science, Artificial Intelligence

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

Yong Yu et al.

NEURAL COMPUTATION (2019)

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Electronic coarse graining: Predictive atomistic modeling of condensed matter

F. S. Cipcigan et al.

REVIEWS OF MODERN PHYSICS (2019)

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Article Chemistry, Multidisciplinary

Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

Mohammed Zidan et al.

APPLIED SCIENCES-BASEL (2019)

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Article Quantum Science & Technology

A generative modeling approach for benchmarking and training shallow quantum circuits

Marcello Benedetti et al.

NPJ QUANTUM INFORMATION (2019)

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Article Chemistry, Physical

Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

Kamal Choudhary et al.

CHEMISTRY OF MATERIALS (2019)

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Article Chemistry, Physical

Ehrenfest and classical path dynamics with decoherence and detailed balance

Parmeet Nijjar et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

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Article Chemistry, Physical

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

Annika Stuke et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

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Review Physics, Applied

Trapped-ion quantum computing: Progress and challenges

Colin D. Bruzewicz et al.

APPLIED PHYSICS REVIEWS (2019)

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Article Quantum Science & Technology

Variational quantum state diagonalization

Ryan LaRose et al.

NPJ QUANTUM INFORMATION (2019)

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Article Chemistry, Multidisciplinary

Learning Retrosynthetic Planning through Simulated Experience

John S. Schreck et al.

ACS CENTRAL SCIENCE (2019)

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Review Chemistry, Multidisciplinary

Quantum Chemistry in the Age of Quantum Computing

Yudong Cao et al.

CHEMICAL REVIEWS (2019)

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Article Computer Science, Artificial Intelligence

An overview of quantum cellular automata

P. Arrighi

NATURAL COMPUTING (2019)

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Article Physics, Multidisciplinary

Quantum convolutional neural networks

Iris Cong et al.

NATURE PHYSICS (2019)

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Article Physics, Multidisciplinary

Quantum Teleportation in High Dimensions

Yi-Han Luo et al.

PHYSICAL REVIEW LETTERS (2019)

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Article Chemistry, Physical

Computing vibrational eigenstates with tree tensor network states (TTNS)

Henrik R. Larsson

JOURNAL OF CHEMICAL PHYSICS (2019)

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Article Chemistry, Physical

Machine Learning Configuration Interaction for ab Initio Potential Energy Curves

Jeremy P. Coe

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Chemistry, Physical

Machine Learning the Physical Nonlocal Exchange-Correlation Functional of Density-Functional Theory

Jonathan Schmidt et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2019)

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Article Multidisciplinary Sciences

Quantum supremacy using a programmable superconducting processor

Frank Arute et al.

NATURE (2019)

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Article Multidisciplinary Sciences

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K. T. Schuett et al.

NATURE COMMUNICATIONS (2019)

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Article Quantum Science & Technology

Local-measurement-based quantum state tomography via neural networks

Tao Xin et al.

NPJ QUANTUM INFORMATION (2019)

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Review Quantum Science & Technology

Parameterized quantum circuits as machine learning models

Marcello Benedetti et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

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Article Chemistry, Medicinal

Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification

Javier L. Baylon et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

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Article Chemistry, Physical

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

K. T. Schutt et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)

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Article Biochemistry & Molecular Biology

PubChem 2019 update: improved access to chemical data

Sunghwan Kim et al.

NUCLEIC ACIDS RESEARCH (2019)

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Article Geography, Physical

Mammalian faunas, ecological indices, and machine-learning regression for the purpose of paleoenvironment reconstruction in the Miocene of South America

Jackson P. Spradley et al.

PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY (2019)

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Article Multidisciplinary Sciences

Occurrence of the potent mutagens 2-nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles

Aldenor G. Santos et al.

SCIENTIFIC REPORTS (2019)

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Article Optics

Probing 10 μK stability and residual drifts in the cross-polarized dual-mode stabilization of single-crystal ultrahigh-Q optical resonators

Jinkang Lim et al.

LIGHT-SCIENCE & APPLICATIONS (2019)

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Machine learning and the physical sciences

Giuseppe Carleo et al.

REVIEWS OF MODERN PHYSICS (2019)

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Article Quantum Science & Technology

Soundness and completeness of quantum root-mean-square errors

Masanao Ozawa

NPJ QUANTUM INFORMATION (2019)

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Article Physics, Condensed Matter

Superconducting Qubits: Current State of Play

Morten Kjaergaard et al.

Annual Review of Condensed Matter Physics (2019)

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Photonic quantum information processing: A concise review

Sergei Slussarenko et al.

APPLIED PHYSICS REVIEWS (2019)

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Article Chemistry, Physical

A Bayesian approach to NMR crystal structure determination

Edgar A. Engel et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2019)

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Article Quantum Science & Technology

Strawberry Fields: A Software Platform for Photonic Quantum Computing

Nathan Killoran et al.

QUANTUM (2019)

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Article Computer Science, Artificial Intelligence

Reconstructing quantum states with generative models

Juan Carrasquilla et al.

NATURE MACHINE INTELLIGENCE (2019)

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Article Chemistry, Physical

Bayesian machine learning for quantum molecular dynamics

R. V. Krems

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2019)

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Continuous-variable quantum neural networks

Nathan Killoran et al.

PHYSICAL REVIEW RESEARCH (2019)

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Article Quantum Science & Technology

Optimising matrix product state simulations of Shor's algorithm

Aidan Dang et al.

QUANTUM (2019)

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Article Quantum Science & Technology

Quantum variational autoencoder

Amir Khoshaman et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

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Article Quantum Science & Technology

Theory of variational quantum simulation

Xiao Yuan et al.

QUANTUM (2019)

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Variational quantum eigensolver with fewer qubits

Jin-Guo Liu et al.

PHYSICAL REVIEW RESEARCH (2019)

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Machine learning for email spam filtering: review, approaches and open research problems

Emmanuel Gbenga Dada et al.

HELIYON (2019)

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Article Computer Science, Information Systems

Towards a Quantum-Inspired Binary Classifier

Prayag Tiwari et al.

IEEE ACCESS (2019)

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Deep learning and density-functional theory

Kevin Ryczko et al.

PHYSICAL REVIEW A (2019)

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Article Quantum Science & Technology

Towards quantum machine learning with tensor networks

William Huggins et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

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Article Quantum Science & Technology

Machine learning method for state preparation and gate synthesis on photonic quantum computers

Juan Miguel Arrazola et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

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Article Quantum Science & Technology

Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum-Classical Algorithms

Sukin Sim et al.

ADVANCED QUANTUM TECHNOLOGIES (2019)

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Evaluating analytic gradients on quantum hardware

Maria Schuld et al.

PHYSICAL REVIEW A (2019)

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Article Chemistry, Multidisciplinary

Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees

Abdul Karim et al.

ACS OMEGA (2019)

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Quantum autoencoders via quantum adders with genetic algorithms

L. Lamata et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

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Article Chemistry, Multidisciplinary

A Machine-Driven Hunt for Global Reaction Coordinates of Azobenzene Photoisomerization

Pedram Tavadze et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2018)

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Article Biochemistry & Molecular Biology

DrugBank 5.0: a major update to the DrugBank database for 2018

David S. Wishart et al.

NUCLEIC ACIDS RESEARCH (2018)

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Editorial Material Multidisciplinary Sciences

Deep learning for natural language processing: advantages and challenges

Hang Li

NATIONAL SCIENCE REVIEW (2018)

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Machine Learning in Computer-Aided Synthesis Planning

Connor W. Coley et al.

ACCOUNTS OF CHEMICAL RESEARCH (2018)

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User's guide to Monte Carlo methods for evaluating path integrals

Marise J. E. Westbroek et al.

AMERICAN JOURNAL OF PHYSICS (2018)

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Quantum Machine Learning in Chemical Compound Space

O. Anatole von Lilienfeld

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2018)

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Article Chemistry, Physical

Gaussian process regression for geometry optimization

Alexander Denzel et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

Alchemical and structural distribution based representation for universal quantum machine learning

Felix A. Faber et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

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Article Chemistry, Physical

T3NS: Three-Legged Tree Tensor Network States

Klaas Gunst et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)

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Article Chemistry, Physical

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

Deping Hu et al.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2018)

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Article Physics, Multidisciplinary

Mutual information, neural networks and the renormalization group

Maciej Koch-Janusz et al.

NATURE PHYSICS (2018)

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Neural-network quantum state tomography

Giacomo Torlai et al.

NATURE PHYSICS (2018)

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A study of deep convolutional auto-encoders for anomaly detection in videos

Manasses Ribeiro et al.

PATTERN RECOGNITION LETTERS (2018)

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Article Physics, Multidisciplinary

Cloud Quantum Computing of an Atomic Nucleus

E. F. Dumitrescu et al.

PHYSICAL REVIEW LETTERS (2018)

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Article Physics, Multidisciplinary

Experimental Machine Learning of Quantum States

Jun Gao et al.

PHYSICAL REVIEW LETTERS (2018)

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Article Physics, Multidisciplinary

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Linfeng Zhang et al.

PHYSICAL REVIEW LETTERS (2018)

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Article Physics, Multidisciplinary

Adiabatic quantum computation

Tameem Albash et al.

REVIEWS OF MODERN PHYSICS (2018)

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Article Multidisciplinary Sciences

Improving refugee integration through data-driven algorithmic assignment

Kirk Bansak et al.

SCIENCE (2018)

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Article Chemistry, Multidisciplinary

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

Kun Yao et al.

CHEMICAL SCIENCE (2018)

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Structural absorption by barbule microstructures of super black bird of paradise feathers

Dakota E. McCoy et al.

NATURE COMMUNICATIONS (2018)

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Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks

Siavash Sakhavi et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)

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Article Quantum Science & Technology

Adequacy of Si:P chains as Fermi-Hubbard simulators

Amintor Dusko et al.

NPJ QUANTUM INFORMATION (2018)

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Article Chemistry, Multidisciplinary

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

ACS CENTRAL SCIENCE (2018)

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Article Chemistry, Physical

Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study

Won-Mi Choi et al.

NPJ COMPUTATIONAL MATERIALS (2018)

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Article Automation & Control Systems

IMA health state evaluation using deep feature learning with quantum neural network

Zehai Gao et al.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)

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Article Physics, Multidisciplinary

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

Song Cheng et al.

ENTROPY (2018)

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Nonadiabatic Excited-State Dynamics with Machine Learning

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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

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Random Quantum Circuits are Approximate 2-designs

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COMMUNICATIONS IN MATHEMATICAL PHYSICS (2009)

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Scaling of Multimillion-Atom Biological Molecular Dynamics Simulation on a Petascale Supercomputer

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Quantum Algorithm for Linear Systems of Equations

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Almost every pure state of three qubits is completely determined by its two-particle reduced density matrices

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Gate errors in solid-state quantum-computer architectures

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PHYSICAL REVIEW LETTERS (2001)

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PHYSICAL REVIEW LETTERS (2001)

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