Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Approximating power of machine-learning ansatz for quantum many-body states
A. Borin, D. Abanin·Jan 24, 2019
An artificial neural network (ANN) with the restricted Boltzmann machine (RBM) architecture was recently proposed as a versatile variational quantum many-body wave function. In this work we provide physical insights into the performance of this ansat...
Neural Decoder for Topological Codes using Pseudo-Inverse of Parity Check Matrix
C. Chinni, Abhishek Kulkarni, Dheeraj M. Pai +2 more·Jan 21, 2019
Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other ...
QuCumber: wavefunction reconstruction with neural networks
M. Beach, Isaac J.S. De Vlugt, A. Golubeva +6 more·Dec 21, 2018
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a s...
Parameter optimization and real-time calibration of a measurement-device-independent quantum key distribution network based on a back propagation artificial neural network
Feng-Yu Lu, Zhen-Qiang Yin, Chao Wang +8 more·Dec 20, 2018
Selection of parameters (e.g., the probability of choosing an X-basis or Z-basis, the intensity of signal state and decoy state, etc.) and system calibrating are more challenging when the number of users of a measurement-device-independent quantum ke...
Machine learning for optimal parameter prediction in quantum key distribution
Wenyuan Wang, H. Lo·Dec 19, 2018
For a practical quantum key distribution (QKD) system, parameter optimization, the choice of intensities and the probabilities of sending them, is a crucial step in gaining optimal performance, especially when one realistically considers a finite com...
Adaptive quantum state tomography with neural networks
Yihui Quek, Stanislav Fort, H. Ng·Dec 17, 2018
Current algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we i...
Quantum Algorithms for Feedforward Neural Networks
J. Allcock, Chang-Yu Hsieh, Iordanis Kerenidis +1 more·Dec 7, 2018
Quantum machine learning has the potential for broad industrial applications, and the development of quantum algorithms for improving the performance of neural networks is of particular interest given the central role they play in machine learning to...
Quantum error correction for the toric code using deep reinforcement learning
Philip Andreasson, J. Johansson, S. Liljestrand +1 more·Nov 29, 2018
We implement a quantum error correction algorithm for bit-flip errors on the topological toric code using deep reinforcement learning. An action-value Q-function encodes the discounted value of moving a defect to a neighboring site on the square grid...
Comparing Neural Network Based Decoders for the Surface Code
Savvas Varsamopoulos, K. Bertels, C. G. Almudéver·Nov 29, 2018
Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error correction codes can be efficiently decoded by employing machine learnin...
An artificial neuron implemented on an actual quantum processor
F. Tacchino, C. Macchiavello, D. Gerace +1 more·Nov 6, 2018
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt’s “perceptron”, but its long term practical...
Learning robust and high-precision quantum controls
R. Wu, Haijin Ding, Daoyi Dong +1 more·Nov 5, 2018
Robust and high-precision quantum control is extremely important but challenging for the functionalization of scalable quantum computation. In this paper, we show that this hard problem can be translated to a supervised machine learning task by think...
Image Classification Using Quantum Inference on the D-Wave 2X
Nga T. T. Nguyen, Garrett T. Kenyon·Nov 1, 2018
We use a quantum annealing D-Wave 2X computer to obtain solutions to NP-hard sparse coding problems. To reduce the dimensionality of the sparse coding problem to fit on the quantum D-Wave 2X hardware, we passed downsampled MNIST images through a bott...
Destabilization of Local Minima in Analog Spin Systems by Correction of Amplitude Heterogeneity.
T. Leleu, Y. Yamamoto, P. McMahon +1 more·Oct 30, 2018
The relaxation of binary spins to analog values has been the subject of much debate in the field of statistical physics, neural networks, and more recently quantum computing, notably because the benefits of using an analog state for finding lower ene...
Quantum advantage in training binary neural networks.
Yidong Liao, Daniel Ebler, Feiyang Liu +1 more·Oct 30, 2018
The performance of a neural network for a given task is largely determined by the initial calibration of the network parameters. Yet, it has been shown that the calibration, also referred to as training, is generally NP-complete. This includes networ...
Emulating quantum computation with artificial neural networks.
Christian Pehle, K. Meier, M. Oberthaler +1 more·Oct 24, 2018
We demonstrate, that artificial neural networks (ANN) can be trained to emulate single or multiple basic quantum operations. In order to realize a quantum state, we implement a novel "quantumness gate" that maps an arbitrary matrix to the real repres...
Topographic Representation for Quantum Machine Learning
B. MacLennan·Oct 13, 2018
This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges. A principa...
Quantum convolutional neural networks
Iris Cong, Soonwon Choi, M. Lukin·Oct 9, 2018
Neural network-based machine learning has recently proven successful for many complex applications ranging from image recognition to precision medicine. However, its direct application to problems in quantum physics is challenging due to the exponent...
Error correction in quantum cryptography based on artificial neural networks
Marcin Niemiec·Oct 1, 2018
Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key dis...
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
J. P. Zwolak, Sandesh S. Kalantre, Xingyao Wu +2 more·Sep 26, 2018
Background Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybe...
Quantum algorithms for structured prediction
Behrooz Sepehry, E. Iranmanesh, M. Friedlander +1 more·Sep 11, 2018
We introduce two quantum algorithms for solving structured prediction problems. We first show that a stochastic gradient descent that uses the quantum minimum finding algorithm and takes its probabilistic failure into account solves the structured pr...