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
Differentiable Learning of Quantum Circuit Born Machine
Jin-Guo Liu, Lei Wang·Apr 11, 2018
Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits ex...
Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao +5 more·Apr 10, 2018
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to clas...
Neural network decoder for topological color codes with circuit level noise
P. Baireuther, M. D. Caio, B. Criger +2 more·Apr 9, 2018
A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to ta...
Barren plateaus in quantum neural network training landscapes
J. McClean, S. Boixo, V. Smelyanskiy +2 more·Mar 29, 2018
Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation,...
Deep neural decoders for near term fault-tolerant experiments
C. Chamberland, Pooya Ronagh·Feb 18, 2018
Finding efficient decoders for quantum error correcting codes adapted to realistic experimental noise in fault-tolerant devices represents a significant challenge. In this paper we introduce several decoding algorithms complemented by deep neural dec...
Classification with Quantum Neural Networks on Near Term Processors
E. Farhi, H. Neven·Feb 16, 2018
We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input...
Quantum Circuit Design for Training Perceptron Models
Yu Zheng, Sicong Lu, R. Wu·Feb 15, 2018
Perceptron model is a fundamental linear classifier in machine learning and also the building block of artificial neural networks. Recently, Wiebe {\it et al} (arXiv:1602.04799) proposed that the training of a perceptron can be quadratically speeded ...
Reinforcement Learning with Neural Networks for Quantum Feedback
T. Fosel, Petru Tighineanu, Talitha Weiss +1 more·Feb 14, 2018
Machine learning with artificial neural networks is revolutionizing science. The most advanced challenges require discovering answers autonomously. This is the domain of reinforcement learning, where control strategies are improved according to a rew...
General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes
Amarsanaa Davaasuren, Yasunari Suzuki, K. Fujii +1 more·Jan 13, 2018
Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on ma...
Accelerating Deep Learning with Memcomputing
Haik Manukian, F. Traversa, M. Ventra·Jan 1, 2018
Restricted Boltzmann machines (RBMs) and their extensions, often called "deep-belief networks", are powerful neural networks that have found applications in the fields of machine learning and artificial intelligence. The standard way to train these m...
A quantum algorithm to train neural networks using low-depth circuits
Guillaume Verdon, M. Broughton, J. Biamonte·Dec 14, 2017
The question has remained open if near-term gate model quantum computers will offer a quantum advantage for practical applications in the pre-fault tolerance noise regime. A class of algorithms which have shown some promise in this regard are the so-...
Machine learning techniques for state recognition and auto-tuning in quantum dots
Sandesh S. Kalantre, J. P. Zwolak, Stephen Ragole +4 more·Dec 13, 2017
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation has relied upon effective tools for achieving and maintaining good experimental parameters, i.e., tuning up devices. In many cases, including quant...
Quantum Neuron: an elementary building block for machine learning on quantum computers
Yudong Cao, G. Guerreschi, Alán Aspuru-Guzik·Nov 30, 2017
Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear function to the resulting weighted su...
How deep learning works -The geometry of deep learning
Xiao Dong, Jiasong Wu, Ling Zhou·Oct 30, 2017
Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the ...
Scalable Neural Network Decoders for Higher Dimensional Quantum Codes
N. P. Breuckmann, Xiaotong Ni·Oct 25, 2017
Machine learning has the potential to become an important tool in quantum error correction as it allows the decoder to adapt to the error distribution of a quantum chip. An additional motivation for using neural networks is the fact that they can be ...
Quantum Hopfield neural network
P. Rebentrost, T. Bromley, C. Weedbrook +1 more·Oct 10, 2017
Quantum computing allows for the potential of significant advancements in both the speed and the capacity of widely used machine learning techniques. Here we employ quantum algorithms for the Hopfield network, which can be used for pattern recognitio...
Learning hard quantum distributions with variational autoencoders
Andrea Rocchetto, Edward Grant, Sergii Strelchuk +2 more·Oct 2, 2017
The exact description of many-body quantum systems represents one of the major challenges in modern physics, because it requires an amount of computational resources that scales exponentially with the size of the system. Simulating the evolution of a...
Neural Networks Architecture Evaluation in a Quantum Computer
A. J. D. Silva, Rodolfo Luan Franco de Oliveira·Oct 1, 2017
In this work, we propose a quantum algorithm to evaluate neural networks architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The proposed algorithm is based on a quantum associative memory and the learning algorithm for artifi...
Quantum autoencoders via quantum adders with genetic algorithms
L. Lamata, U. Alvarez-Rodriguez, J. Martín-Guerrero +2 more·Sep 21, 2017
The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers w...
Quantum dynamics in transverse-field Ising models from classical networks
M. Schmitt, M. Heyl·Jul 20, 2017
The efficient representation of quantum many-body states with classical resources is a key challenge in quantum many-body theory. In this work we analytically construct classical networks for the description of the quantum dynamics in transverse-fiel...