Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,694
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1,159
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Research Volume
13,007 papers in 12 months (-3% vs prior quarter)
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Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
A Quantum Model for Multilayer Perceptron
Changpeng Shao·Aug 31, 2018
Multilayer perceptron is the most common used class of feed-forward artificial neural network. It contains many applications in diverse fields such as speech recognition, image recognition, and machine translation software. To cater for the fast deve...
Deep learning, quantum chaos, and pseudorandom evolution
Daniel W. F. Alves, Michael O. Flynn·Aug 30, 2018
By modeling quantum chaotic dynamics with ensembles of random operators, we explore how a deep learning architecture known as a convolutional neural network (CNN) can be used to detect pseudorandom behavior in qubit systems. We analyze samples consis...
Quantum optical neural networks
Gregory R. Steinbrecher, J. Olson, D. Englund +1 more·Aug 29, 2018
Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped in...
Quantum enhanced cross-validation for near-optimal neural networks architecture selection
Priscila G. M. dos Santos, Rodrigo S. Sousa, Ismael C. S. Araújo +1 more·Aug 27, 2018
This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory (PQM) and the possibility to train artificial neural networ...
Neural-network states for the classical simulation of quantum computing
Bjarni J'onsson, B. Bauer, Giuseppe Carleo·Aug 15, 2018
Simulating quantum algorithms with classical resources generally requires exponential resources. However, heuristic classical approaches are often very efficient in approximately simulating special circuit structures, for example with limited entangl...
Learning and Inference on Generative Adversarial Quantum Circuits
J. Zeng, Y. Wu, Jin-Guo Liu +2 more·Aug 10, 2018
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of qua...
Machine learning method for state preparation and gate synthesis on photonic quantum computers
J. Arrazola, T. Bromley, J. Izaac +3 more·Jul 27, 2018
We show how techniques from machine learning and optimization can be used to find circuits of photonic quantum computers that perform a desired transformation between input and output states. In the simplest case of a single input state, our method d...
Local-measurement-based quantum state tomography via neural networks
T. Xin, Sirui Lu, Ningping Cao +5 more·Jul 19, 2018
Quantum state tomography is a daunting challenge of experimental quantum computing, even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the...
Benchmarking Neural Networks For Quantum Computations
N. Nguyen, E. Behrman, M. A. Moustafa +1 more·Jul 9, 2018
The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theo...
Bayesian deep learning on a quantum computer
Zhikuan Zhao, Alejandro Pozas-Kerstjens, P. Rebentrost +1 more·Jun 29, 2018
Bayesian methods in machine learning, such as Gaussian processes, have great advantages compared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep archi...
Learning Simon's quantum algorithm
Kwok Ho Wan, Feiyang Liu, O. Dahlsten +1 more·Jun 27, 2018
We consider whether trainable quantum unitaries can be used to discover quantum speed-ups for classical problems. Using methods recently developed for training quantum neural nets, we consider Simon's problem, for which there is a known quantum algor...
A Universal Training Algorithm for Quantum Deep Learning
Guillaume Verdon, J. Pye, M. Broughton·Jun 25, 2018
We introduce the Backwards Quantum Propagation of Phase errors (Baqprop) principle, a central theme upon which we construct multiple universal optimization heuristics for training both parametrized quantum circuits and classical deep neural networks ...
Quantum codes from neural networks
Johannes Bausch, Felix Leditzky·Jun 22, 2018
We examine the usefulness of applying neural networks as a variational state ansatz for many-body quantum systems in the context of quantum information-processing tasks. In the neural network state ansatz, the complex amplitude function of a quantum ...
Quantum computing with classical bits
C. Wetterich·Jun 15, 2018
Abstract A bit-quantum map relates probabilistic information for Ising spins or classical bits to quantum spins or qubits. Quantum systems are subsystems of classical statistical systems. The Ising spins can represent macroscopic two-level observable...
From Probabilistic Graphical Models to Generalized Tensor Networks for Supervised Learning
I. Glasser, Nicola Pancotti, J. I. Cirac·Jun 15, 2018
Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning. In this work we explore the connection between tens...
Quantum analog-digital conversion
K. Mitarai, M. Kitagawa, K. Fujii·May 29, 2018
Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can be categorized into two types; analog-encoding where the data are stor...
Quantum neural networks to simulate many-body quantum systems
Bartłomiej Gardas, M. Rams, J. Dziarmaga·May 14, 2018
We conduct experimental simulations of many body quantum systems using a \emph{hybrid} classical-quantum algorithm. In our setup, the wave function of the transverse field quantum Ising model is represented by a restricted Boltzmann machine. This neu...
Circuit designs for superconducting optoelectronic loop neurons
J. Shainline, S. Buckley, A. McCaughan +4 more·May 4, 2018
Optical communication achieves high fanout and short delay advantageous for information integration in neural systems. Superconducting detectors enable signaling with single photons for maximal energy efficiency. We present designs of superconducting...
Quantum machine learning for data scientists
Dawid Kopczyk·Apr 25, 2018
This text aims to present and explain quantum machine learning algorithms to a data scientist in an accessible and consistent way. The algorithms and equations presented are not written in rigorous mathematical fashion, instead, the pressure is put o...
Machine learning assisted readout of trapped-ion qubits
Alireza Seif, K. Landsman, N. Linke +3 more·Apr 20, 2018
We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the...