Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,694
This Month
1,159
Today
0
Research Volume
13,008 papers in 12 months (-3% vs prior quarter)
Research Focus Areas
Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
A Quantum Convolutional Neural Network for Image Classification
Yanxuan Lü, Qing Gao, Jinhu Lü +2 more·Jul 8, 2021
Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data wit...
A Leap among Quantum Computing and Quantum Neural Networks: A Survey
F. V. Massoli, Lucia Vadicamo, G. Amato +1 more·Jul 6, 2021
In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scienti...
Quantum Annealing Formulation for Binary Neural Networks
M. Sasdelli, Tat-Jun Chin·Jul 5, 2021
Quantum annealing is a promising paradigm for building practical quantum computers. Compared to other approaches, quantum annealing technology has been scaled up to a larger number of qubits. On the other hand, deep learning has been profoundly succe...
Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity
Wei-Ming Li, Shi-Ju Ran·Jul 1, 2021
In quantum and quantum-inspired machine learning, a key step is to embed the data in the quantum space known as Hilbert space. Studying quantum kernel function, which defines the distances among the samples in the Hilbert space, belongs to the fundam...
Predicting quantum dynamical cost landscapes with deep learning
Mogens Dalgaard, F. Motzoi, J. Sherson·Jun 30, 2021
State-of-the-art quantum algorithms routinely tune dynamically parametrized cost functionals for combinatorics, machine learning, equation-solving, or energy minimization. However, large search complexity often demands many (noisy) quantum measuremen...
Generalized "Square roots of Not" matrices, their application to the unveiling of hidden logical operators and to the definition of fully matrix circular Euler functions
E. Mizraji·Jun 22, 2021
The square root of Not is a logical operator of importance in quantum computing theory and of interest as a mathematical object in its own right. In physics, it is a square complex matrix of dimension 2. In the present work it is a complex square mat...
On the Cryptographic Hardness of Learning Single Periodic Neurons
M. Song, Ilias Zadik, Joan Bruna·Jun 20, 2021
We show a simple reduction which demonstrates the cryptographic hardness of learning a single periodic neuron over isotropic Gaussian distributions in the presence of noise. More precisely, our reduction shows that any polynomial-time algorithm (not ...
QFCNN: Quantum Fourier Convolutional Neural Network
Feihong Shen, Jun Liu·Jun 19, 2021
The neural network and quantum computing are both significant and appealing fields, with their interactive disciplines promising for large-scale computing tasks that are untackled by conventional computers. However, both developments are restricted b...
Quantum Generative Training Using R\'enyi Divergences
M. Kieferová, Carlos Ortiz Marrero, N. Wiebe·Jun 17, 2021
Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of ...
Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States
Jack Y. Araz, M. Spannowsky·Jun 15, 2021
Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniq...
The quantum annealing gap and quench dynamics in the exact cover problem
Bernhard Irsigler, T. Grass·Jun 15, 2021
Quenching and annealing are extreme opposites in the time evolution of a quantum system: Annealing explores equilibrium phases of a Hamiltonian with slowly changing parameters and can be exploited as a tool for solving complex optimization problems. ...
Variational Quanvolutional Neural Networks with enhanced image encoding
Denny Mattern, Darya Martyniuk, Henri Willems +2 more·Jun 14, 2021
Image classification is an important task in various machine learning applications. In recent years, a number of classification methods based on quantum machine learning and different quantum image encoding techniques have been proposed. In this pape...
Classical and Quantum Algorithms for Orthogonal Neural Networks
Iordanis Kerenidis, Jonas Landman, Natansh Mathur·Jun 14, 2021
Orthogonal neural networks have recently been introduced as a new type of neural networks imposing orthogonality on the weight matrices. They could achieve higher accuracy and avoid evanescent or explosive gradients for deep architectures. Several cl...
Variational Quantum-Neural Hybrid Eigensolver.
Shi-Xin Zhang, Z. Wan, Chee-Kong Lee +3 more·Jun 9, 2021
The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-scale quantum (NISQ) era, and is generally speculated to deliver one of the first quantum advantages for the ground-state simulat...
IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization
Areeq I. Hasan·May 25, 2021
We propose a novel paradigm of integration of Grover's algorithm in a machine learning framework: the inductive Grover oracular quantum neural network (IGO-QNN). The model defines a variational quantum circuit with hidden layers of parameterized quan...
Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials.
M. Sajjan, S. Sureshbabu, S. Kais·May 20, 2021
Quantum machine-learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure calculatio...
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Nanqing Dong, Michael C. Kampffmeyer, I. Voiculescu +1 more·May 20, 2021
Entanglement is a physical phenomenon, which has fueled recent successes of quantum algorithms. Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, for the time being, the effect of ...
Optimizing the walk coin in the quantum random walk search algorithm
Hristo Tonchev, Petar Danev·May 17, 2021
This paper examines the stability of the quantum random walk search algorithm, when the walk coin is constructed by generalized Householder reflection and additional phase shift, against inaccuracies in the phases used to construct the coin. The opti...
Neural Error Mitigation of Near-Term Quantum Simulations
Elizabeth R. Bennewitz, Florian Hopfmueller, B. Kulchytskyy +2 more·May 17, 2021
Near-term quantum computers provide a promising platform for finding the ground states of quantum systems, which is an essential task in physics, chemistry and materials science. However, near-term approaches are constrained by the effects of noise, ...
Quantum error reduction with deep neural network applied at the post-processing stage
A. Zhukov, W. Pogosov·May 17, 2021
Deep neural networks (DNN) can be applied at the post-processing stage for the improvement of the results of quantum computations on noisy intermediate-scale quantum (NISQ) processors. Here, we propose a method based on this idea, which is most suita...