☆ 4.5 Review

Quantum machine learning: from physics to software engineering

ADVANCES IN PHYSICS-X (2023)

Related references

Note: Only part of the references are listed.
Article Optics

Quantum optics of soliton microcombs

Melissa A. Guidry et al.

Summary: This study utilizes second-order photon correlations to investigate the quantum processes of soliton microcombs in an integrated silicon carbide microresonator, showing that a stable temporal lattice of solitons can achieve all-to-all entanglement.

NATURE PHOTONICS (2022)

Add to Collection
Article Physics, Multidisciplinary

Extract the Degradation Information in Squeezed States with Machine Learning

Hsien-Yi Hsieh et al.

Summary: This study demonstrates fast, robust quantum state tomography using machine learning, achieving high fidelity reconstruction with a neural network. It also reveals degradation information in low and high noisy scenarios, showcasing the potential of machine learning in quantum state analysis.

PHYSICAL REVIEW LETTERS (2022)

Add to Collection
Review Materials Science, Multidisciplinary

Deep Learning for Photonic Design and Analysis: Principles and Applications

Bing Duan et al.

Summary: This paper reviews the recent advances of deep learning in photonic structure design and optical data analysis, based on the two major learning paradigms of supervised learning and unsupervised learning. It highlights optical neural networks as novel computing architectures with high parallelism and low energy consumption.

FRONTIERS IN MATERIALS (2022)

Add to Collection
Article Physics, Multidisciplinary

Solving Large-Scale Linear Systems of Equations by a Quantum Hybrid Algorithm

M. R. Perelshtein et al.

Summary: This study utilizes phase estimation and classical optimization algorithms for solving a specific class of large linear systems of equations. By introducing the entanglement properties of the associated phase estimation operation and a straightforward matrix-to-circuit map, an efficient search for solutions is achieved. A record-breaking result for solving a 2(17)-dimensional linear system is demonstrated on IBM quantum computer processors.

ANNALEN DER PHYSIK (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Quantum Machine Learning: A tutorial

Jose D. Martin-Guerrero et al.

Summary: This tutorial provides an overview of Quantum Machine Learning (QML), a discipline that combines concepts from Machine Learning (ML), Quantum Computing (QC), and Quantum Information (QI). QC has experienced significant development with the involvement of large technological companies and the success of ML, making QML a major field for researchers working at the intersection of Physics, Mathematics, and Computer Science. QML methods can be classified into those that use ML in a quantum experimentation environment and those that utilize QC and QI to find alternative and improved solutions to data-driven problems, often resulting in considerable speedup and enhanced performances.

NEUROCOMPUTING (2022)

Add to Collection
Article Multidisciplinary Sciences

Direct Parameter Estimations from Machine Learning-Enhanced Quantum State Tomography

Hsien-Yi Hsieh et al.

Summary: Machine-learning-enhanced quantum state tomography (QST) can extract complete information about quantum states by finding the best fit to arbitrarily complicated symmetry. By using a characteristic model that directly estimates parameters, ML-QST can avoid dealing with a large Hilbert space while maintaining high precision in feature extraction, making it a crucial diagnostic toolbox for applications with squeezed states.

SYMMETRY-BASEL (2022)

Add to Collection
Article Physics, Multidisciplinary

General parameter-shift rules for quantum gradients

David Wierichs et al.

Quantum (2022)

Add to Collection
Article Multidisciplinary Sciences

Capturing Symmetries of Quantum Optimization Algorithms Using Graph Neural Networks

Ajinkya Deshpande et al.

Summary: The study presents an algorithm based on a graph neural network that can choose between a quantum optimizer and classical optimizer using performance prediction. It also proposes a method to predict the optimal parameters of a variational quantum optimizer. The approach was tested on the Max-Cut problem with a specific quantum optimizer and achieved promising results.

SYMMETRY-BASEL (2022)

Add to Collection
Proceedings Paper Acoustics

QUANTUM FEDERATED LEARNING WITH QUANTUM DATA

Mahdi Chehimi et al.

Summary: This paper proposes the first fully quantum federated learning framework that operates over purely quantum data. It introduces a decentralized learning method for distributing quantum learning over quantum networks and provides the first quantum federated dataset. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution, which combines Google's TensorFlow Federated and TensorFlow Quantum.

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) (2022)

Add to Collection
Review Chemistry, Multidisciplinary

Quantum machine learning for chemistry and physics

Manas Sajjan et al.

Summary: Machine learning and deep learning have greatly contributed to the development of chemistry, especially in materials design and energy applications. These algorithms have also revolutionized chemical reaction dynamics and drug design. This review provides an overview of the contributions made by classical and quantum computing enhanced machine learning algorithms in chemistry, with the aim of promoting interdisciplinary collaboration.

CHEMICAL SOCIETY REVIEWS (2022)

Add to Collection
Article Optics

Neural-network quantum state tomography

Dominik Koutny et al.

Summary: Researchers revisit the application of neural networks to quantum state tomography and confirm that the positivity constraint can be successfully implemented through training. Their findings suggest that this approach can be used for quantum state reconstruction under various types of noise.

PHYSICAL REVIEW A (2022)

Add to Collection
Review Physics, Applied

Machine learning in the search for new fundamental physics

Georgia Karagiorgi et al.

Summary: Compelling experimental evidence suggests the existence of new physics beyond the standard model. Machine learning and deep learning methods have become increasingly important in dealing with the large and complex data from high-energy physics experiments.

NATURE REVIEWS PHYSICS (2022)

Add to Collection
Article Optics

Supervised graph classification for chiral quantum walks

Artem Kryukov et al.

Summary: Particle transport in quantum systems, modeled by quantum walks on graphs, shows faster propagation compared to classical systems. Additional advantages can be achieved by adding directional control in quantum walks, such as breaking time-reversal symmetry with chiral quantum walks. Graph properties classification remains an open problem, but using a graph convolutional neural network trained on simulated examples can help tackle this issue. Chiral quantum-walk dynamics almost always lead to faster transport on hypercubes compared to nonchiral dynamics, demonstrating the potential for experimental implementations of quantum-walk advantage.

PHYSICAL REVIEW A (2022)

Add to Collection
Article Quantum Science & Technology

Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus

Zoe Holmes et al.

Summary: Parametrized quantum circuits are a flexible paradigm for solving variational problems and programming near-term quantum computers. By extending the barren plateau phenomenon to arbitrary ansatze, we establish a fundamental relationship between expressibility and trainability, showing that highly expressive ansatze are more difficult to train.

PRX QUANTUM (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Attention-based quantum tomography

Peter Cha et al.

Summary: In this study, an attention mechanism-based generative network model is proposed for quantum state reconstruction of noisy quantum states. The research demonstrates that the model outperforms previous neural-network-based methods on identical tasks and accurately reconstructs the density matrix of a noisy quantum state realized experimentally.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2022)

Add to Collection
Article Quantum Science & Technology

Quantum machine learning with adaptive linear optics

Ulysse Chabaud et al.

Summary: This study investigates supervised learning algorithms using quantum devices for computational subroutines and designs quantum subroutines using Boson Sampling architectures. The results demonstrate explicit limits to the potential quantum advantage of adaptive linear optics compared to classical machine learning algorithms within certain parameter ranges.

QUANTUM (2021)

Add to Collection
Article Quantum Science & Technology

Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus

Chen Zhao et al.

Summary: This paper proposes a general scheme to analyze the gradient vanishing phenomenon in training quantum neural networks using the ZX-calculus. By representing integrations as ZX-diagrams and computing them with the ZX-calculus, the barren plateau phenomenon is studied on four concrete quantum neural networks with different structures. It is found that there are barren plateaus for hardware efficient ansatz and MPS-inspired ansatz, while no barren plateau exists for QCNN ansatz and tree tensor network ansatz.

QUANTUM (2021)

Add to Collection
Review Optics

Deep learning for the design of photonic structures

Wei Ma et al.

Summary: Innovative approaches and tools, particularly deep learning, are shaping the field of photonics by offering efficient means to design photonic structures and providing data-driven solutions complementary to traditional physics-based methods. The progress in deep-learning-based photonic design is promising, with various model architectures showing potential applications in specific photonic tasks.

NATURE PHOTONICS (2021)

Add to Collection
Editorial Material Computer Science, Hardware & Architecture

Deciphering Errors to Reduce the Cost of Quantum Computation

Daniel Gottesman

COMMUNICATIONS OF THE ACM (2021)

Add to Collection
Article Nanoscience & Nanotechnology

Machine Learning for Integrated Quantum Photonics

Zhaxylyk A. Kudyshev et al.

Summary: This Perspective focuses on the emerging field of physics-informed machine learning (ML) quantum photonics, discussing three main directions of ML-assisted quantum research: rapid preselection of single-photon sources via ML-assisted quantum measurements, hybrid ML-optimization approach for developing efficient quantum circuits elements, and ML-based frameworks for developing novel deterministic assembly of on-chip quantum emitters.

ACS PHOTONICS (2021)

Add to Collection
Article Quantum Science & Technology

Large gradients via correlation in random parameterized quantum circuits

Tyler Volkoff et al.

Summary: This study demonstrates that reducing the dimensionality of parameter space by utilizing circuit modules containing spatially or temporally correlated gate layers can help avoid the vanishing gradient phenomenon. In the variational versions of Grover's algorithm, as the number of layers increases towards O(2n/2), the bounds on cost function variation suggest a transition from vanishing gradients to efficient trainability.

QUANTUM SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Article Quantum Science & Technology

Quantum versus classical generative modelling in finance

Brian Coyle et al.

Summary: Comparing the capabilities of quantum and classical models in generative modeling in machine learning, it was found that the simulated Born machine matches or exceeds the performance of the Boltzmann machine, especially as the model scales. Entanglement capacity plays a role in problem instances where the Born machine demonstrates an advantage over the Boltzmann machine. Experiments were conducted on simulated and physical quantum chips using the Rigetti QCS(TM) platform, with the largest instance of a quantum circuit Born machine partially trained on quantum hardware.

QUANTUM SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Article Quantum Science & Technology

Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

Leonardo Banchi et al.

Summary: Hybrid quantum-classical optimization algorithms aim to optimize observable quantities with respect to classical parameters using feedback from quantum device measurements. This study introduces a stochastic algorithm for estimating gradients of multi-qubit parametric quantum evolutions directly from quantum measurements, without the need for ancillary qubits or Hamiltonian simulation techniques. The algorithm can handle noise in quantum gates and still provide accurate approximations for gradient estimation.

QUANTUM (2021)

Add to Collection
Article Physics, Multidisciplinary

Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light

Han-Sen Zhong et al.

Summary: Phase-programmable Gaussian boson sampling (GBS) is a new quantum technology that allows for high-purity and high-efficiency photon sampling through tuning the phase of squeezed input states. The experimental results demonstrate the capability of GBS to pass nonclassicality tests and exhibit nontrivial genuine high-order correlations, indicating robustness against classical simulation schemes.

PHYSICAL REVIEW LETTERS (2021)

Add to Collection
Article Physics, Multidisciplinary

Quantum State Tomography with Conditional Generative Adversarial Networks

Shahnawaz Ahmed et al.

Summary: In this study, CGANs are used for quantum state tomography, which reconstructs optical quantum states with high fidelity in significantly fewer iterative steps and with less data compared to other methods. The CGAN framework utilizes two dueling neural networks, a generator and a discriminator, to learn multimodal models from data.

PHYSICAL REVIEW LETTERS (2021)

Add to Collection
Article Physics, Multidisciplinary

Direct Fidelity Estimation of Quantum States Using Machine Learning

Xiaoqian Zhang et al.

Summary: The study introduces a machine-learning-based method for evaluating quantum state fidelity, which is more flexible and efficient compared to other methods. This approach is applicable to arbitrary quantum states and can achieve high-precision fidelity prediction with fewer measurement settings.

PHYSICAL REVIEW LETTERS (2021)

Add to Collection
Article Multidisciplinary Sciences

Experimental quantum speed-up in reinforcement learning agents

V. Saggio et al.

Summary: As artificial intelligence advances, reinforcement learning becomes increasingly important. Although some studies have used quantum mechanics to speed up the decision-making process of agents, a reduction in learning time has not yet been demonstrated.

NATURE (2021)

Add to Collection
Article Multidisciplinary Sciences

Noise-induced barren plateaus in variational quantum algorithms

Samson Wang et al.

Summary: The study demonstrates that local Pauli noise can render VQAs untrainable. It shows that noise-induced barren plateaus cause the gradient to exponentially vanish during the training process.

NATURE COMMUNICATIONS (2021)

Add to Collection
Article Multidisciplinary Sciences

Quantum semi-supervised generative adversarial network for enhanced data classification

Kouhei Nakaji et al.

Summary: In this paper, a quantum semi-supervised generative adversarial network (qSGAN) is proposed, consisting of a quantum generator and a classical discriminator/classifier. The goal is to train both components to achieve high classification accuracy for a given dataset. qSGAN requires no data loading or generation of pure quantum states, making it easier to implement than existing quantum algorithms. The generator, with its rich expressibility, serves as a stronger adversary than a classical one and is expected to be robust against noise, as demonstrated in numerical simulations.

SCIENTIFIC REPORTS (2021)

Add to Collection
Article Physics, Multidisciplinary

Quantum Machine Learning Tensor Network States

Andrey Kardashin et al.

Summary: Tensor network algorithms aim to compress classical data representing quantum states by minimizing correlations. These algorithms form the basis of numerical methods in many-body physics and find applications in machine learning. Efficiently finding and contracting tensor network states is a computational task that can be accelerated by quantum computing.

FRONTIERS IN PHYSICS (2021)

Add to Collection
Review Optics

Quantum Reinforcement Learning with Quantum Photonics

Lucas Lamata

Summary: Quantum machine learning, particularly quantum reinforcement learning, is a promising field aiming to design quantum agents to adapt to and interact with their environment. Quantum technology in photonics has the potential to enhance quantum computation, communication, and machine learning through their integration.

PHOTONICS (2021)

Add to Collection
Article Physics, Applied

Variational Inference with a Quantum Computer

Marcello Benedetti et al.

Summary: Inference is the task of drawing conclusions about unobserved variables given observations of related variables, and variational inference is an effective approximation method. In this work, quantum Born machines are used as variational distributions over discrete variables, implementing the framework of operator variational inference.

PHYSICAL REVIEW APPLIED (2021)

Add to Collection
Article Quantum Science & Technology

Effect of barren plateaus on gradient-free optimization

Andrew Arrasmith et al.

Summary: Barren plateau landscapes are shown to significantly impact gradient-based optimizers, and this study confirms that gradient-free optimizers are also unable to solve the barren plateau problem. The research reveals the limitations of gradient-free optimization and sheds light on the challenges of training quantum neural networks in barren plateaus.

QUANTUM (2021)

Add to Collection
Article Quantum Science & Technology

Encoding-dependent generalization bounds for parametrized quantum circuits

Matthias C. Caro et al.

Summary: Recent research has explored the potential of parametrized quantum circuits (PQCs) as machine learning models, proposing generalization bounds that depend on data-encoding strategies. These results emphasize the importance of well-considered data-encoding strategies for PQC-based models and facilitate optimal strategy selection through structural risk minimization.

QUANTUM (2021)

Add to Collection
Article Multidisciplinary Sciences

Cost function dependent barren plateaus in shallow parametrized quantum circuits

M. Cerezo et al.

Summary: In this study, the authors rigorously prove that defining cost functions with local observables can avoid the barren plateau problem, while defining them with global observables leads to exponentially vanishing gradients. The results indicate a connection between locality and trainability in variational quantum algorithms (VQAs).

NATURE COMMUNICATIONS (2021)

Add to Collection
Article Quantum Science & Technology

Device-independent quantum key distribution from generalized CHSH inequalities

Pavel Sekatski et al.

Summary: Device-independent quantum key distribution aims to provide security assurances even with largely uncharacterised devices. By deriving a security proof from a generalisation of the CHSH score that takes into account the individual values of two correlation functions, higher key rates can be achieved. The additional information considered in this technique, which is available in practice, offers potential advantages for realistic photonic implementations of device-independent quantum key distribution.

QUANTUM (2021)

Add to Collection
Article Astronomy & Astrophysics

Observation of Gravitational Waves from Two Neutron Star-Black Hole Coalescences

R. Abbott et al.

Summary: This study reports the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run, with properties consistent with neutron star-black hole (NSBH) binaries. The study provides detailed information on the source component masses, source luminosity distances, and more for the two events observed.

ASTROPHYSICAL JOURNAL LETTERS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Quantum circuit representation of Bayesian networks

Sima E. Borujeni et al.

Summary: Probabilistic graphical models like Bayesian networks are commonly used in modeling stochastic systems for various analyses, but can be computationally expensive in large systems. Applications of quantum computing leveraging amplitude amplification advantages show significant computational benefits compared to classical methods. Researchers have developed a systematic method for designing quantum circuits to represent generic discrete Bayesian networks with multiple states.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection
Article Chemistry, Medicinal

Quantum Machine Learning Algorithms for Drug Discovery Applications

Kushal Batra et al.

Summary: This article discusses how to compress and process large amounts of data in drug discovery using quantum computers, and explores the advantages and disadvantages of quantum computing compared to traditional computing methods. The authors demonstrate how to use algorithms such as SVM and DNN for data processing on QC, and reveal key steps to be aware of in order to apply quantum computing to drug discovery.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Add to Collection
Article Physics, Multidisciplinary

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)

Add to Collection
Article Physics, Multidisciplinary

Benchmarking quantum tomography completeness and fidelity with machine learning

Yong Siah Teo et al.

Summary: In this study, convolutional neural networks are trained to predict the completeness of information in quantum measurements, accelerating the characterization of quantum states. Experimental results show that trained networks can significantly reduce certification time and improve the computation yield of large-scale quantum processors.

NEW JOURNAL OF PHYSICS (2021)

Add to Collection
Article Multidisciplinary Sciences

Moore's Law revisited through Intel chip density

David Burg et al.

Summary: Gordon Moore's observation of exponential growth in the number of transistors in integrated circuits has sparked debate about the dynamics of computer processor evolution. The increase involves two related phenomena - integration of larger numbers of transistors and transistor miniaturization. Density of Intel processors from 1959 to 2013 follows a biphasic sigmoidal curve with characteristic times of 9.5 years, with consistent tenfold increases in transistor density within approximately six years followed by negligible growth rates for at least three years in each stage.

PLOS ONE (2021)

Add to Collection
Article Multidisciplinary Sciences

A squeezed quantum microcomb on a chip

Zijiao Yang et al.

Summary: A deterministic quantum microcomb consisting of 40 continuous-variable quantum modes has been successfully demonstrated on a silicon chip, offering new possibilities for applications in fields such as spectroscopy and quantum metrology.

NATURE COMMUNICATIONS (2021)

Add to Collection
Article Physics, Applied

Experimental Quantum Generative Adversarial Networks for Image Generation

He-Liang Huang et al.

Summary: Quantum machine learning is seen as one of the early practical applications of near-term quantum devices, with quantum generative adversarial networks showing potential exponential advantages over classical GANs. However, it is uncertain whether quantum GANs on near-term quantum devices can solve real-world learning tasks. Researchers have developed a flexible quantum GAN scheme and successfully applied it to learning and generating real-world handwritten digit images using a superconducting quantum processor, showcasing the potential of quantum GANs in learning tasks.

PHYSICAL REVIEW APPLIED (2021)

Add to Collection
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)

Add to Collection
Article Quantum Science & Technology

Anomaly detection with variational quantum generative adversarial networks

Daniel Herr et al.

Summary: Generative adversarial networks (GANs) consist of a generative model and a discriminative model for sampling from a target distribution and evaluating the proximity of a sample to the target distribution. The introduction of variational quantum-classical Wasserstein GANs (WGANs) addresses training instabilities and sampling efficiency issues for anomaly detection.

QUANTUM SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Proceedings Paper Quantum Science & Technology

Photonic Quantum Policy Learning in OpenAI Gym

Daniel Nagy et al.

Summary: In recent years, noisy intermediate scale quantum (NISQ) computing devices have become available, with quantum machine learning emerging as a promising application area. This study utilizes continuous-variable quantum machine learning to solve classical continuous control problems, presenting performance assessment using proximal policy optimization for photonic variational quantum agents and data re-uploading. The results show that the photonic policy learning achieves comparable performance levels and faster convergence than classical neural networks for the restricted CartPole problem.

2021 IEEE INTERNATIONAL CONFERENCE ON QUANTUM COMPUTING AND ENGINEERING (QCE 2021) / QUANTUM WEEK 2021 (2021)

Add to Collection
Article Optics

Analytic gradients in variational quantum algorithms: Algebraic extensions of the parameter-shift rule to general unitary transformations

Artur F. Izmaylov et al.

Summary: Optimization of unitary transformations in variational quantum algorithms benefits greatly from efficient evaluation of cost function gradients. We propose extensions of the parameter-shift rule to deal with gradients as linear combinations of expectation values for generators with general eigenspectra. These approaches are exact and do not require auxiliary qubits, relying instead on generator eigenspectrum analysis.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Optics

Tomography of time-dependent quantum Hamiltonians with machine learning

Chen-Di Han et al.

Summary: The study introduces a physics-enhanced machine learning framework, utilizing Heisenberg neural networks to understand time-dependent quantum Hamiltonians. Through local measurements, not only can the local Hamiltonian be accurately reconstructed, but also the interacting structure of the entire system can be faithfully recovered.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Quantum Science & Technology

How To Use Neural Networks To Investigate Quantum Many-Body Physics

Juan Carrasquilla et al.

Summary: This article reviews the applications of machine learning in condensed matter physics and quantum information, with a focus on providing hands-on tutorials for newcomers. The prerequisites for readers include basic knowledge of probability theory, calculus, linear algebra, neural networks, statistical physics, and quantum mechanics. The algorithms covered include supervised learning with convolutional neural networks, unsupervised learning with restricted Boltzmann machines, and the variational Monte Carlo method with recurrent neural networks.

PRX QUANTUM (2021)

Add to Collection
Article Optics

Generalized quantum circuit differentiation rules

Oleksandr Kyriienko et al.

Summary: This paper introduces a differentiation rule for quantum circuits with arbitrary generators, which is applicable to generators with a generic nondegenerate spectrum, and provides a simple formula for calculating derivatives. By measuring the weighted sum of expected values of the circuits, the derivatives can be calculated, and the number of function evaluations depends on the number of unique positive nonzero spectral gaps of the generator.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Quantum Science & Technology

Entanglement-Induced Barren Plateaus

Carlos Ortiz Marrero et al.

Summary: The article discusses how excess entanglement between visible and hidden units in quantum neural networks can hinder learning. Through arguments from quantum thermodynamics, it is shown that the volume law in entanglement entropy is typical and can lead to barren plateaus in the optimization landscape due to entanglement. This could cause both gradient-descent and gradient-free methods to fail.

PRX QUANTUM (2021)

Add to Collection
Article Optics

Boltzmann machine learning with a variational quantum algorithm

Yuta Shingu et al.

Summary: The Boltzmann machine is a powerful tool for modeling probability distributions in training data. A method for implementing Boltzmann machine learning using noisy intermediate-scale quantum devices is proposed, with successful numerical simulations confirming its effectiveness in approximating the probability distribution of the thermal equilibrium state. This scheme represents a significant advancement towards efficient machine learning using quantum hardware.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Physics, Multidisciplinary

Classification and reconstruction of optical quantum states with deep neural networks

Shahnawaz Ahmed et al.

Summary: We apply deep learning techniques to quantum state classification and reconstruction, achieving high accuracies even in noisy and data-scarce conditions. By using optical quantum states as examples, successful classification and reconstruction were demonstrated with the potential to reduce the cost of tomography.

PHYSICAL REVIEW RESEARCH (2021)

Add to Collection
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)

Add to Collection
Article Computer Science, Artificial Intelligence

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Max Wilson et al.

Summary: Generative models can model and generate new examples from a dataset, with applications driven by commercial and academic interest. This work introduces an algorithm for learning a latent variable generative model via generative adversarial learning, utilizing a graphical model and quantum processor for sampling. The algorithm shows equivalent performance between quantum and classical versions for learning implicit generative models.

QUANTUM MACHINE INTELLIGENCE (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Robust implementation of generative modeling with parametrized quantum circuits

Vicente Leyton-Ortega et al.

Summary: This study evaluated the on-hardware performance of hybrid quantum-classical algorithms using Rigetti's Quantum Cloud Services, focusing on different classical solvers and circuit ansatze. The results showed that gradient-free optimization algorithms outperformed gradient-based solvers, especially in handling noisy objective functions under experimental conditions.

QUANTUM MACHINE INTELLIGENCE (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Machine learning pipeline for quantum state estimation with incomplete measurements

Onur Danaci et al.

Summary: This paper explores the resilience of machine-learning-based quantum state estimation techniques to missing measurements, achieving quantum state estimation from partial measurement results that outperforms previously developed methods and traditional approaches.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection
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)

Add to Collection
Article Computer Science, Artificial Intelligence

Layerwise learning for quantum neural networks

Andrea Skolik et al.

Summary: The study focuses on a layerwise learning strategy for parametrized quantum circuits, which incrementally grows circuit depth and updates subsets of parameters to mitigate challenges posed by cost function landscapes; this strategy can help avoid barren plateaus of the error surface due to sampling noise, making it preferable for execution on noisy intermediate-scale quantum devices.

QUANTUM MACHINE INTELLIGENCE (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Reinforcement learning decoders for fault-tolerant quantum computation

Ryan Sweke et al.

Summary: Topological error correcting codes, particularly the surface code, are currently the most feasible roadmap for large-scale fault-tolerant quantum computation. This research shows that decoding such codes can be reformulated as interactions between a decoding agent and a code environment, using reinforcement learning to obtain decoding agents. By using deep Q learning, decoding agents for various simplified phenomenological noise models were obtained.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Article Optics

Solving nonlinear differential equations with differentiable quantum circuits

Oleksandr Kyriienko et al.

Summary: A quantum algorithm is proposed to solve systems of nonlinear differential equations using a quantum feature map encoding and differentiable quantum circuits. The hybrid quantum-classical workflow involves training DQCs to meet differential equations and specified boundary conditions, with a particular example demonstrating a spectral method in a high-dimensional feature space. The use of a Chebyshev quantum feature map offers a powerful basis set for fitting polynomials with rich expressivity, as shown in a simulation solving Navier-Stokes equations for fluid flow.

PHYSICAL REVIEW A (2021)

Add to Collection
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)

Add to Collection
Article Chemistry, Multidisciplinary

A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers

Jakob S. Kottmann et al.

Summary: The study developed computationally affordable and encoding independent gradient evaluation procedures for unitary coupled-cluster type operators applicable on quantum computers, allowing for evaluation of the gradient of an expectation value using four similar expectation values to reduce cost and enabling the construction of differentiable objective functions. Initial applications were illustrated through extended adaptive approaches for electronic ground and excited states.

CHEMICAL SCIENCE (2021)

Add to Collection
Article Optics

Universal variational quantum computation

Jacob Biamonte

Summary: Variational quantum algorithms are shown to be computationally universal by developing objective functions that minimize the output preparation of arbitrary quantum circuits. The optimization of expected values in variational quantum computation relies on an iterative classical-to-quantum outer loop optimization, with the quantum circuit itself providing an efficient solution. This approach is efficient for n-qubit circuits with non-Clifford gates, and can handle various quantum circuit structures effectively.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

A hybrid classical-quantum workflow for natural language processing

Lee J. O'Riordan et al.

Summary: This manuscript demonstrates the use of quantum computing models for NLP tasks, developing a hybrid workflow to process small and large scale corpus data sets, showing the efficacy of the method, and releasing the developed toolkit as an open software suite.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Review Computer Science, Artificial Intelligence

Quantum machine learning in high energy physics

Wen Guan et al.

Summary: Machine learning has been widely used in high energy physics for supervised classification, while quantum computing is providing new possibilities for machine learning applications by exploiting hardware capacity. Research on quantum machine learning in high energy physics is expected to have broader applications in the future.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Classifying global state preparation via deep reinforcement learning

Tobias Haug et al.

Summary: Deep reinforcement learning is used to achieve global quantum control in quantum information processing, preparing a continuous set of states. Protocols are represented using neural networks, which automatically group similar types of protocols, aiding in finding classes and extracting physical insights.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)

Add to Collection
Article Optics

Estimating the gradient and higher-order derivatives on quantum hardware

Andrea Mari et al.

Summary: For a large class of variational quantum circuits, arbitrary-order derivatives can be analytically evaluated using simple parameter-shift rules, which can be efficiently used to implement second-order optimization algorithms on a quantum computer. The impact of statistical noise on derivative estimators is considered, with the performance of different estimators and optimizers found to be intertwined with the values of different hyperparameters. Numerical and hardware experiments support these findings, including an estimation of the Hessian of a variational circuit and an implementation of the Newton optimizer.

PHYSICAL REVIEW A (2021)

Add to Collection
Article Computer Science, Information Systems

Image classification on IoT edge devices: profiling and modeling

Salma Abdel Magid et al.

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS (2020)

Add to Collection
Review Optics

Integrated photonic quantum technologies

Jianwei Wang et al.

NATURE PHOTONICS (2020)

Add to Collection
Article Psychology, Biological

Quantum reinforcement learning during human decision-making

Ji-An Li et al.

NATURE HUMAN BEHAVIOUR (2020)

Add to Collection
Article Nanoscience & Nanotechnology

A short introduction to the Lindblad master equation

Daniel Manzano

AIP ADVANCES (2020)

Add to Collection
Review Physics, Applied

The building blocks of a brain-inspired computer

Jack D. Kendall et al.

APPLIED PHYSICS REVIEWS (2020)

Add to Collection
Article Quantum Science & Technology

Experimental neural network enhanced quantum tomography

Adriano Macarone Palmieri et al.

NPJ QUANTUM INFORMATION (2020)

Add to Collection
Article Optics

Experiments quantum homodyne tomography via machine learning

E. S. Tiunov et al.

OPTICA (2020)

Add to Collection
Review Optics

Hybrid integrated quantum photonic circuits

Ali W. Elshaari et al.

NATURE PHOTONICS (2020)

Add to Collection
Article Physics, Multidisciplinary

Machine Learning Non-Markovian Quantum Dynamics

I. A. Luchnikov et al.

PHYSICAL REVIEW LETTERS (2020)

Add to Collection
Article Physics, Multidisciplinary

Retrieving Quantum Information with Active Learning

Yongcheng Ding et al.

PHYSICAL REVIEW LETTERS (2020)

Add to Collection
Article Quantum Science & Technology

The Born supremacy: quantum advantage and training of an Ising Born machine

Brian Coyle et al.

NPJ QUANTUM INFORMATION (2020)

Add to Collection
Article Physics, Multidisciplinary

Input Redundancy for Parameterized Quantum Circuits

Francisco Javier Gil Vidal et al.

FRONTIERS IN PHYSICS (2020)

Add to Collection
Article Physics, Multidisciplinary

Key questions for the quantum machine learner to ask themselves

Nathan Wiebe

NEW JOURNAL OF PHYSICS (2020)

Add to Collection
Article Computer Science, Information Systems

Quantum generative adversarial network for generating discrete distribution

Haozhen Situ et al.

INFORMATION SCIENCES (2020)

Add to Collection
Article Physics, Multidisciplinary

Setting Up Experimental Bell Tests with Reinforcement Learning

Alexey A. Melnikov et al.

PHYSICAL REVIEW LETTERS (2020)

Add to Collection
Article Physics, Multidisciplinary

Emerging Complexity in Distributed Intelligent Systems

Valentina Guleva et al.

ENTROPY (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

A differentiable programming method for quantum control

Frank Schafer et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection
Article Multidisciplinary Sciences

Quantum computational advantage using photons

Han-Sen Zhong et al.

SCIENCE (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Pseudo-dimension of quantum circuits

Matthias C. Caro et al.

QUANTUM MACHINE INTELLIGENCE (2020)

Add to Collection
Article Optics

Robust data encodings for quantum classifiers

Ryan LaRose et al.

PHYSICAL REVIEW A (2020)

Add to Collection
Article Quantum Science & Technology

Data re-uploading for a universal quantum classifier

Adrian Perez-Salinas et al.

QUANTUM (2020)

Add to Collection
Article Optics

Eigenstate extraction with neural-network tomography

Abhijeet Melkani et al.

PHYSICAL REVIEW A (2020)

Add to Collection
Article Quantum Science & Technology

Machine Learning for Long-Distance Quantum Communication

Julius Wallnofer et al.

PRX QUANTUM (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Quantum machine learning and quantum biomimetics: A perspective

Lucas Lamata

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Classical versus quantum models in machine learning: insights from a finance application

Javier Alcazar et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection
Review Computer Science, Information Systems

A Review of Optical Neural Networks

Xiubao Sui et al.

IEEE ACCESS (2020)

Add to Collection
Article Quantum Science & Technology

Machine Learning Transfer Efficiencies for Noisy Quantum Walks

Alexey A. Melnikov et al.

ADVANCED QUANTUM TECHNOLOGIES (2020)

Add to Collection
Article Physics, Multidisciplinary

Expressive power of parametrized quantum circuits

Yuxuan Du et al.

PHYSICAL REVIEW RESEARCH (2020)

Add to Collection
Article Optics

Circuit-centric quantum classifiers

Maria Schuld et al.

PHYSICAL REVIEW A (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Repetitive readout enhanced by machine learning

Genyue Liu et al.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2020)

Add to Collection
Article Multidisciplinary Sciences

Supervised learning with quantum-enhanced feature spaces

Vojtech Havlicek et al.

NATURE (2019)

Add to Collection
Review Optics

Quantum optical microcombs

Michael Kues et al.

NATURE PHOTONICS (2019)

Add to Collection
Article Physics, Multidisciplinary

Quantum Machine Learning in Feature Hilbert Spaces

Maria Schuld et al.

PHYSICAL REVIEW LETTERS (2019)

Add to Collection
Article Physics, Multidisciplinary

Adversarial quantum circuit learning for pure state approximation

Marcello Benedetti et al.

NEW JOURNAL OF PHYSICS (2019)

Add to Collection
Article Physics, Multidisciplinary

Machine Learning Nonlocal Correlations

Askery Canabarro et al.

PHYSICAL REVIEW LETTERS (2019)

Add to Collection
Article Quantum Science & Technology

A generative modeling approach for benchmarking and training shallow quantum circuits

Marcello Benedetti et al.

NPJ QUANTUM INFORMATION (2019)

Add to Collection
Article Chemistry, Multidisciplinary

Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy

Wei Ma et al.

ADVANCED MATERIALS (2019)

Add to Collection
Article Mathematics

Derivative-free optimization methods

Jeffrey Larson et al.

ACTA NUMERICA (2019)

Add to Collection
Article Quantum Science & Technology

Quantum optical neural networks

Gregory R. Steinbrecher et al.

NPJ QUANTUM INFORMATION (2019)

Add to Collection
Article Physics, Multidisciplinary

Quantum convolutional neural networks

Iris Cong et al.

NATURE PHYSICS (2019)

Add to Collection
Article Multidisciplinary Sciences

Quantum supremacy using a programmable superconducting processor

Frank Arute et al.

NATURE (2019)

Add to Collection
Article Multidisciplinary Sciences

Deterministic generation of a two-dimensional cluster state

Mikkel V. Larsen et al.

SCIENCE (2019)

Add to Collection
Article Multidisciplinary Sciences

Generation of time-domain-multiplexed two-dimensional cluster state

Warit Asavanant et al.

SCIENCE (2019)

Add to Collection
Article Quantum Science & Technology

Generalizable control for quantum parameter estimation through reinforcement learning

Han Xu et al.

NPJ QUANTUM INFORMATION (2019)

Add to Collection
Article Quantum Science & Technology

Local-measurement-based quantum state tomography via neural networks

Tao Xin et al.

NPJ QUANTUM INFORMATION (2019)

Add to Collection
Review Quantum Science & Technology

Parameterized quantum circuits as machine learning models

Marcello Benedetti et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

Add to Collection
Article Physics, Multidisciplinary

On the complexity and verification of quantum random circuit sampling

Adam Bouland et al.

NATURE PHYSICS (2019)

Add to Collection
Article Physics, Multidisciplinary

Predicting quantum advantage by quantum walk with convolutional neural networks

Alexey A. Melnikov et al.

NEW JOURNAL OF PHYSICS (2019)

Add to Collection
Article Physics, Multidisciplinary

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

Giacomo Torlai et al.

PHYSICAL REVIEW LETTERS (2019)

Add to Collection
Review Physics, Multidisciplinary

Machine learning and the physical sciences

Giuseppe Carleo et al.

REVIEWS OF MODERN PHYSICS (2019)

Add to Collection
Editorial Material Multidisciplinary Sciences

Quantum machine learning

Jonathan Allcock et al.

NATIONAL SCIENCE REVIEW (2019)

Add to Collection
Article Physics, Multidisciplinary

Hamiltonian learning for quantum error correction

Agnes Valenti et al.

PHYSICAL REVIEW RESEARCH (2019)

Add to Collection
Article Computer Science, Artificial Intelligence

Reconstructing quantum states with generative models

Juan Carrasquilla et al.

NATURE MACHINE INTELLIGENCE (2019)

Add to Collection
Article Optics

Generative model benchmarks for superconducting qubits

Kathleen E. Hamilton et al.

PHYSICAL REVIEW A (2019)

Add to Collection
Article Physics, Fluids & Plasmas

Performance of hybrid quantum-classical variational heuristics for combinatorial optimization

Giacomo Nannicini

PHYSICAL REVIEW E (2019)

Add to Collection
Article Quantum Science & Technology

Quantum variational autoencoder

Amir Khoshaman et al.

QUANTUM SCIENCE AND TECHNOLOGY (2019)

Add to Collection
Article Optics

Learning and inference on generative adversarial quantum circuits

Jinfeng Zeng et al.

PHYSICAL REVIEW A (2019)

Add to Collection
Proceedings Paper Nanoscience & Nanotechnology

Hitting time for quantum walks of identical particles

Alexey A. Melnikov et al.

INTERNATIONAL CONFERENCE ON MICRO- AND NANO-ELECTRONICS 2018 (2019)

Add to Collection
Article Quantum Science & Technology

Optimizing Quantum Error Correction Codes with Reinforcement Learning

Hendrik Poulsen Nautrup et al.

QUANTUM (2019)

Add to Collection
Article Quantum Science & Technology

AN INITIALIZATION STRATEGY FOR ADDRESSING BARREN PLATEAUS IN PARAMETRIZED QUANTUM CIRCUITS

Edward Grant et al.

QUANTUM (2019)

Add to Collection
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)

Add to Collection
Article Quantum Science & Technology

Reconstruction of a Photonic Qubit State with Reinforcement Learning

Shang Yu et al.

ADVANCED QUANTUM TECHNOLOGIES (2019)

Add to Collection
Article Materials Science, Multidisciplinary

Learnability scaling of quantum states: Restricted Boltzmann machines

Dan Sehayek et al.

PHYSICAL REVIEW B (2019)

Add to Collection
Article Optics

Evaluating analytic gradients on quantum hardware

Maria Schuld et al.

PHYSICAL REVIEW A (2019)

Add to Collection
Editorial Material Computer Science, Hardware & Architecture

A Future with Quantum Machine Learning

Erik P. DeBenedictis

COMPUTER (2018)

Add to Collection
Article Physics, Multidisciplinary

Characterizing quantum supremacy in near-term devices

Sergio Boixo et al.

NATURE PHYSICS (2018)

Add to Collection
Article Multidisciplinary Sciences

Active learning machine learns to create new quantum experiments

Alexey A. Melnikov et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)

Add to Collection
Review Multidisciplinary Sciences

Quantum machine learning: a classical perspective

Carlo Ciliberto et al.

PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2018)

Add to Collection
Review Physics, Multidisciplinary

v Machine learning & artificial intelligence in the quantum domain: a review of recent progress

Vedran Dunjko et al.

REPORTS ON PROGRESS IN PHYSICS (2018)

Add to Collection
Article Quantum Science & Technology

Learning hard quantum distributions with variational autoencoders

Andrea Rocchetto et al.

NPJ QUANTUM INFORMATION (2018)

Add to Collection
Article Physics, Multidisciplinary

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

Song Cheng et al.

ENTROPY (2018)

Add to Collection
Article Physics, Multidisciplinary

Quantum Generative Adversarial Learning

Seth Lloyd et al.

PHYSICAL REVIEW LETTERS (2018)

Add to Collection
Article Physics, Multidisciplinary

Learning the quantum algorithm for state overlap

Lukasz Cincio et al.

NEW JOURNAL OF PHYSICS (2018)

Add to Collection
Article Multidisciplinary Sciences

Barren plateaus in quantum neural network training landscapes

Jarrod R. McClean et al.

NATURE COMMUNICATIONS (2018)

Add to Collection
Article Multidisciplinary Sciences

A quantum machine learning algorithm based on generative models

X. Gao et al.

SCIENCE ADVANCES (2018)

Add to Collection
Article Optics

Differentiable learning of quantum circuit Born machines

Jin-Guo Liu et al.

PHYSICAL REVIEW A (2018)

Add to Collection
Article Quantum Science & Technology

Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices

Marcello Benedetti et al.

QUANTUM SCIENCE AND TECHNOLOGY (2018)

Add to Collection
Article Computer Science, Information Systems

Benchmarking Projective Simulation in Navigation Problems

Alexey A. Melnikov et al.

IEEE ACCESS (2018)

Add to Collection
Article Quantum Science & Technology

Quantum Computing in the NISQ era and beyond

John Preskill

QUANTUM (2018)

Add to Collection
Article Optics

Quantum generative adversarial networks

Pierre-Luc Dallaire-Demers et al.

PHYSICAL REVIEW A (2018)

Add to Collection
Review Multidisciplinary Sciences

Quantum computational supremacy

Aram W. Harrow et al.

NATURE (2017)

Add to Collection
Article Multidisciplinary Sciences

On-chip generation of high-dimensional entangled quantum states and their coherent control

Michael Kues et al.

NATURE (2017)

Add to Collection
Article Multidisciplinary Sciences

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets

Abhinav Kandala et al.

NATURE (2017)

Add to Collection
Review Multidisciplinary Sciences

Quantum machine learning

Jacob Biamonte et al.

NATURE (2017)

Add to Collection
Article Physics, Multidisciplinary

Efficient tomography of a quantum many-body system

B. P. Lanyon et al.

NATURE PHYSICS (2017)

Add to Collection
Article Physics, Multidisciplinary

Practical adaptive quantum tomography

Christopher Granade et al.

NEW JOURNAL OF PHYSICS (2017)

Add to Collection
Article Physics, Multidisciplinary

Continuous-Variable Instantaneous Quantum Computing is Hard to Sample

T. Douce et al.

PHYSICAL REVIEW LETTERS (2017)

Add to Collection
Article Multidisciplinary Sciences

Solving the quantum many-body problem with artificial neural networks

Giuseppe Carleo et al.

SCIENCE (2017)

Add to Collection
Article Multidisciplinary Sciences

Efficient representation of quantum many-body states with deep neural networks

Xun Gao et al.

NATURE COMMUNICATIONS (2017)

Add to Collection
Article Multidisciplinary Sciences

Experimental quantum compressed sensing for a seven-qubit system

C. A. Riofrio et al.

NATURE COMMUNICATIONS (2017)

Add to Collection
Article Multidisciplinary Sciences

Distributed secure quantum machine learning

Yu-Bo Sheng et al.

SCIENCE BULLETIN (2017)

Add to Collection
Article Quantum Science & Technology

Demonstration of quantum advantage in machine learning

Diego Riste et al.

NPJ QUANTUM INFORMATION (2017)

Add to Collection
Article Multidisciplinary Sciences

Exciton-polariton Josephson junctions at finite temperatures

M. E. Lebedev et al.

SCIENTIFIC REPORTS (2017)

Add to Collection
Article Multidisciplinary Sciences

Projective simulation with generalization

Alexey A. Melnikov et al.

SCIENTIFIC REPORTS (2017)

Add to Collection
Article Physics, Mathematical

Quantum Algorithm for Linear Differential Equations with Exponentially Improved Dependence on Precision

Dominic W. Berry et al.

COMMUNICATIONS IN MATHEMATICAL PHYSICS (2017)

Add to Collection
Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Add to Collection
Article Physics, Multidisciplinary

The theory of variational hybrid quantum-classical algorithms

Jarrod R. McClean et al.

NEW JOURNAL OF PHYSICS (2016)

Add to Collection
Article Physics, Multidisciplinary

Spatial Search by Quantum Walk is Optimal for Almost all Graphs

Shantanav Chakraborty et al.

PHYSICAL REVIEW LETTERS (2016)

Add to Collection
Article Physics, Multidisciplinary

Automated Search for new Quantum Experiments

Mario Krenn et al.

PHYSICAL REVIEW LETTERS (2016)

Add to Collection
Article Multidisciplinary Sciences

Generation of multiphoton entangled quantum states by means of integrated frequency combs

Christian Reimer et al.

SCIENCE (2016)

Add to Collection
Review Optics

Integrated photonic quantum walks

Markus Graefe et al.

JOURNAL OF OPTICS (2016)

Add to Collection
Article Computer Science, Information Systems

Meta-learning within Projective Simulation

Adi Makmal et al.

IEEE ACCESS (2016)

Add to Collection
Article Multidisciplinary Sciences

Quantum walks of interacting fermions on a cycle graph

Alexey A. Melnikov et al.

SCIENTIFIC REPORTS (2016)

Add to Collection
Review Physics, Multidisciplinary

Bayesian network structure learning using quantum annealing

B. O'Gorman et al.

EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS (2015)

Add to Collection
Article Computer Science, Hardware & Architecture

Projective Simulation for Classical Learning Agents: A Comprehensive Investigation

Julian Mautner et al.

NEW GENERATION COMPUTING (2015)

Add to Collection
Article Physics, Multidisciplinary

Quantum error correction for quantum memories

Barbara M. Terhal

REVIEWS OF MODERN PHYSICS (2015)

Add to Collection
Article Multidisciplinary Sciences

Adaptive quantum computation in changing environments using projective simulation

M. Tiersch et al.

SCIENTIFIC REPORTS (2015)

Add to Collection
Article Physics, Multidisciplinary

An introduction to quantum machine learning

Maria Schuld et al.

CONTEMPORARY PHYSICS (2015)

Add to Collection
Article Physics, Multidisciplinary

High-order quantum algorithm for solving linear differential equations

Dominic W. Berry

JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL (2014)

Add to Collection
Article Physics, Multidisciplinary

Quantum principal component analysis

Seth Lloyd et al.

NATURE PHYSICS (2014)

Add to Collection
Article Optics

Quantum process tomography of a Molmer-Sorensen interaction

Nir Navon et al.

PHYSICAL REVIEW A (2014)

Add to Collection
Article Physics, Multidisciplinary

Quantum Support Vector Machine for Big Data Classification

Patrick Rebentrost et al.

PHYSICAL REVIEW LETTERS (2014)

Add to Collection
Article Multidisciplinary Sciences

Defining and detecting quantum speedup

Troels F. Ronnow et al.

SCIENCE (2014)

Add to Collection
Article Multidisciplinary Sciences

A variational eigenvalue solver on a photonic quantum processor

Alberto Peruzzo et al.

NATURE COMMUNICATIONS (2014)

Add to Collection
Article Optics

Tomography of a spin qubit in a double quantum dot

Maki Takahashi et al.

PHYSICAL REVIEW A (2013)

Add to Collection
Article Physics, Multidisciplinary

Nobel Lecture: Controlling photons in a box and exploring the quantum to classical boundary

Serge Haroche

REVIEWS OF MODERN PHYSICS (2013)

Add to Collection
Article Physics, Multidisciplinary

Nobel Lecture: Superposition, entanglement, and raising Schrodinger's cat

David J. Wineland

REVIEWS OF MODERN PHYSICS (2013)

Add to Collection
Article Physics, Multidisciplinary

Quantum tomography via compressed sensing: error bounds, sample complexity and efficient estimators

Steven T. Flammia et al.

NEW JOURNAL OF PHYSICS (2012)

Add to Collection
Article Physics, Multidisciplinary

Efficient Method for Computing the Maximum-Likelihood Quantum State from Measurements with Additive Gaussian Noise

John A. Smolin et al.

PHYSICAL REVIEW LETTERS (2012)

Add to Collection
Article Multidisciplinary Sciences

Projective simulation for artificial intelligence

Hans J. Briegel et al.

SCIENTIFIC REPORTS (2012)

Add to Collection
Article Physics, Multidisciplinary

Interacting quantum observables: categorical algebra and diagrammatics

Bob Coecke et al.

NEW JOURNAL OF PHYSICS (2011)

Add to Collection
Article Physics, Multidisciplinary

Quantum algorithms for classical lattice models

G. De las Cuevas et al.

NEW JOURNAL OF PHYSICS (2011)

Add to Collection
Article Physics, Multidisciplinary

Efficient Measurement of Quantum Dynamics via Compressive Sensing

A. Shabani et al.

PHYSICAL REVIEW LETTERS (2011)

Add to Collection
Article Quantum Science & Technology

Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design

Vicky Choi

QUANTUM INFORMATION PROCESSING (2011)

Add to Collection
Review Physics, Multidisciplinary

Quantum picturalism

Bob Coecke

CONTEMPORARY PHYSICS (2010)

Add to Collection
Review Multidisciplinary Sciences

Quantum computers

T. D. Ladd et al.

NATURE (2010)

Add to Collection
Article Physics, Multidisciplinary

Optimal, reliable estimation of quantum states

Robin Blume-Kohout

NEW JOURNAL OF PHYSICS (2010)

Add to Collection
Article Physics, Multidisciplinary

Permutationally Invariant Quantum Tomography

G. Toth et al.

PHYSICAL REVIEW LETTERS (2010)

Add to Collection
Article Physics, Multidisciplinary

Quantum State Tomography via Compressed Sensing

David Gross et al.

PHYSICAL REVIEW LETTERS (2010)

Add to Collection
Article Multidisciplinary Sciences

Efficient quantum state tomography

Marcus Cramer et al.

NATURE COMMUNICATIONS (2010)

Add to Collection
Article Physics, Multidisciplinary

Quantum Algorithm for Linear Systems of Equations

Aram W. Harrow et al.

PHYSICAL REVIEW LETTERS (2009)

Add to Collection
Article Physics, Multidisciplinary

Quantum and Classical Correlations in Waveguide Lattices

Yaron Bromberg et al.

PHYSICAL REVIEW LETTERS (2009)

Add to Collection
Article Multidisciplinary Sciences

Temporally unstructured quantum computation

Dan Shepherd et al.

PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2009)

Add to Collection
Article Biology

A quantum probability explanation for violations of 'rational' decision theory

Emmanuel M. Pothos et al.

PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES (2009)

Add to Collection
Review Physics, Multidisciplinary

Continuous-variable optical quantum-state tomography

A. I. Lvovsky et al.

REVIEWS OF MODERN PHYSICS (2009)

Add to Collection
Article Mathematics, Applied

BREAKING THE CURSE OF DIMENSIONALITY, OR HOW TO USE SVD IN MANY DIMENSIONS

I. V. Oseledets et al.

SIAM JOURNAL ON SCIENTIFIC COMPUTING (2009)

Add to Collection
Article Automation & Control Systems

Quantum reinforcement learning

Daoyi Dong et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2008)

Add to Collection
Article Computer Science, Artificial Intelligence

Representational power of restricted Boltzmann machines and deep belief networks

Nicolas Le Roux et al.

NEURAL COMPUTATION (2008)

Add to Collection
Article Multidisciplinary Sciences

Coherence dynamics in photosynthesis: Protein protection of excitonic coherence

Hohjai Lee et al.

SCIENCE (2007)

Add to Collection
Article Physics, Mathematical

Efficient quantum algorithms for simulating sparse Hamiltonians

Dominic W. Berry et al.

COMMUNICATIONS IN MATHEMATICAL PHYSICS (2007)

Add to Collection
Article Computer Science, Artificial Intelligence

Random walks for image segmentation

Leo Grady

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)

Add to Collection
Article Optics

Hitting time for quantum walks on the hypercube

H Krovi et al.

PHYSICAL REVIEW A (2006)

Add to Collection
Article Computer Science, Hardware & Architecture

Random walks in peer-to-peer networks: Algorithms and evaluation

C Gkantsidis et al.

PERFORMANCE EVALUATION (2006)

Add to Collection
Article Optics

Continuous-time quantum walks on a cycle graph

D Solenov et al.

PHYSICAL REVIEW A (2006)

Add to Collection
Article Statistics & Probability

Discrete quantum walks hit exponentially faster

J Kempe

PROBABILITY THEORY AND RELATED FIELDS (2005)

Add to Collection
Article Physics, Multidisciplinary

Quantum process tomography of a controlled-NOT gate

JL O'Brien et al.

PHYSICAL REVIEW LETTERS (2004)

Add to Collection
Article Optics

Iterative maximum-likelihood reconstruction in quantum homodyne tomography

AI Lvovsky

JOURNAL OF OPTICS B-QUANTUM AND SEMICLASSICAL OPTICS (2004)

Add to Collection
Article Computer Science, Theory & Methods

Equivalences and separations between quantum and classical learnability

RA Servedio et al.

SIAM JOURNAL ON COMPUTING (2004)

Add to Collection
Article Computer Science, Theory & Methods

QUANTUM WALKS AND THEIR ALGORITHMIC APPLICATIONS

Andris Ambainis

INTERNATIONAL JOURNAL OF QUANTUM INFORMATION (2003)

Add to Collection
Review Physics, Multidisciplinary

Quantum random walks: an introductory overview

J Kempe

CONTEMPORARY PHYSICS (2003)

Add to Collection
Article Optics

Quantum inference of states and processes -: art. no. 012305

M Jezek et al.

PHYSICAL REVIEW A (2003)

Add to Collection
Article Optics

Measurement of qubits

DFV James et al.

PHYSICAL REVIEW A (2001)

Add to Collection
Article Multidisciplinary Sciences

Direct observation of hole transfer through DNA by hopping between adenine bases and by tunnelling

B Giese et al.

NATURE (2001)

Add to Collection
Article Optics

Encoding a qubit in an oscillator

D Gottesman et al.

PHYSICAL REVIEW A (2001)

Add to Collection
Article Optics

Iterative algorithm for reconstruction of entangled states -: art. no. 040303

J Rehácek et al.

PHYSICAL REVIEW A (2001)

Add to Collection
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.