Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,694
This Month
1,159
Today
0
Research Volume
13,007 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
Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
Guillaume Verdon, Jacob A. Marks, Sasha Nanda +2 more·Oct 4, 2019
We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for quantum-probabilistic hybrid variational learning, where we efficiently ...
Symmetries for a high-level neural decoder on the toric code
Thomas Wagner, H. Kampermann, D. Bruß·Oct 3, 2019
Surface codes are a promising method of quantum error correction and the basis of many proposed quantum computation implementations. However, their efficient decoding is still not fully explored. Recently, approaches based on machine learning techniq...
A quantum search decoder for natural language processing
Johannes Bausch, Sathyawageeswar Subramanian, Stephen Piddock·Sep 9, 2019
Probabilistic language models, e.g. those based on recurrent neural networks such as long short-term memory models (LSTMs), often face the problem of finding a high probability prediction from a sequence of random variables over a set of tokens. This...
Quantum adiabatic machine learning with zooming
Alexander Zlokapa, A. Mott, Joshua Job +3 more·Aug 13, 2019
Recent work has shown that quantum annealing for machine learning (QAML) can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose a variant algorithm (QAML-Z) that itera...
Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon, M. Broughton, J. McClean +5 more·Jul 11, 2019
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges. One such challenge is finding good parameter initialization heuristic...
Hamiltonian learning for quantum error correction
Agnes Valenti, Evert P L van Nieuwenburg, S. Huber +1 more·Jul 4, 2019
The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information processin...
Variational Quantum Circuits for Deep Reinforcement Learning
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi +3 more·Jun 30, 2019
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-gian...
Machine Learning Phase Transitions with a Quantum Processor
A. Uvarov, A. Kardashin, J. Biamonte·Jun 24, 2019
Machine learning has emerged as a promising approach to unveil properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods---such as Monte Carlo---which are known to e...
Convolution filter embedded quantum gate autoencoder
Kodai Shiba, K. Sakamoto, Koichi Yamaguchi +2 more·Jun 4, 2019
The autoencoder is one of machine learning algorithms used for feature extraction by dimension reduction of input data, denoising of images, and prior learning of neural networks. At the same time, autoencoders using quantum computers are also being ...
Defining Quantum Neural Networks via Quantum Time Evolution
Aditya Dendukuri, B. Keeling, A. Fereidouni +3 more·May 27, 2019
This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, i...
Neural ensemble decoding for topological quantum error-correcting codes
Milap Sheth, S. Jafarzadeh, Vlad Gheorghiu·May 7, 2019
Topological quantum error-correcting codes are a promising candidate for building fault-tolerant quantum computers. Decoding topological codes optimally, however, is known to be a computationally hard problem. Various decoders have been proposed that...
Building quantum neural networks based on a swap test
Jian Zhao, Yuanhua Zhang, Changpeng Shao +3 more·Apr 29, 2019
Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, the other is nonlinear. The linear operation is inner product and the n...
Variational quantum unsampling on a quantum photonic processor
J. Carolan, M. Mohseni, J. Olson +9 more·Apr 23, 2019
A promising route towards the demonstration of near-term quantum advantage (or supremacy) over classical systems relies on running tailored quantum algorithms on noisy intermediate-scale quantum machines. These algorithms typically involve sampling f...
Quanvolutional neural networks: powering image recognition with quantum circuits
Maxwell P. Henderson, Samriddhi Shakya, S. Pradhan +1 more·Apr 9, 2019
Convolutional neural networks (CNNs) have rapidly risen in popularity for many machine learning applications, particularly in the field of image recognition. Much of the benefit generated from these networks comes from their ability to extract featur...
Neural network agent playing spin Hamiltonian games on a quantum computer
O. M. Sotnikov, V. Mazurenko·Apr 4, 2019
Quantum computing is expected to provide new promising approaches for solving the most challenging problems in material science, communication, search, machine learning and other domains. However, due to the decoherence and gate imperfection errors m...
Framework for atomic-level characterisation of quantum computer arrays by machine learning
M. Usman, Yi Z. Wong, C. Hill +1 more·Apr 3, 2019
Atomic-level qubits in silicon are attractive candidates for large-scale quantum computing; however, their quantum properties and controllability are sensitive to details such as the number of donor atoms comprising a qubit and their precise location...
Quantum Generative Adversarial Networks for learning and loading random distributions
Christa Zoufal, Aurélien Lucchi, Stefan Woerner·Mar 29, 2019
Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the ability to load classical data efficiently into quantum states. However, the best known method...
Machine learning methods in quantum computing theory
D. V. Fastovets, Y. Bogdanov, B. Bantysh +1 more·Mar 15, 2019
Classical machine learning theory and theory of quantum computations are among of the most rapidly developing scientific areas in our days. In recent years, researchers investigated if quantum computing can help to improve classical machine learning ...
Deep reinforcement learning for quantum gate control
Zheng An, Duan-Lu Zhou·Feb 22, 2019
How to implement multi-qubit gates efficiently with high precision is essential for realizing universal fault-tolerant computing. For a physical system with some external controllable parameters, it is a great challenge to control the time dependence...
Decoding surface code with a distributed neural network–based decoder
Savvas Varsamopoulos, K. Bertels, C. G. Almudéver·Jan 30, 2019
There has been a rise in decoding quantum error correction codes with neural network–based decoders, due to the good decoding performance achieved and adaptability to any noise model. However, the main challenge is scalability to larger code distance...