Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Fast correlated-photon imaging enhanced by deep learning
Zhanming Li, Shi-Bao Wu, Jun Gao +5 more·Jun 16, 2020
Correlated photon pairs, carrying strong quantum correlations, have been harnessed to bring quantum advantages to various fields from biological imaging to range finding. Such inherent non-classical properties support extracting more valid signals to...
Coherent Ising machines—Quantum optics and neural network Perspectives
Yoshihisa Yamamoto, T. Leleu, S. Ganguli +1 more·Jun 10, 2020
A coherent Ising machine (CIM) is a network of optical parametric oscillators (OPOs), in which the “strongest” collective mode of oscillation at well above threshold corresponds to an optimum solution of a given Ising problem. When a pump rate or net...
Variational Quantum Singular Value Decomposition
Xin Wang, Zhixin Song, Youle Wang·Jun 3, 2020
Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algo...
Advances in Quantum Deep Learning: An Overview
Siddhant Garg, Goutham Ramakrishnan·May 8, 2020
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep ...
Neuromorphic quantum computing.
Christian Pehle, C. Wetterich·May 4, 2020
Quantum computation builds on the use of correlations. Correlations could also play a central role for artificial intelligence, neuromorphic computing or "biological computing." As a step toward a systematic exploration of "correlated computing" we d...
Using Deep Learning to Understand and Mitigate the Qubit Noise Environment
D. Wise, J. Morton, S. Dhomkar·May 3, 2020
Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise sp...
Characterizing the memory capacity of transmon qubit reservoirs
S. Dasgupta, Kathleen E. Hamilton, A. Banerjee·Apr 15, 2020
Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5–7 qubits possess computational capabilities comparable to conventional recurrent...
Predicting human-generated bitstreams using classical and quantum models
Alex Bocharov, M. Freedman, Eshan Kemp +2 more·Apr 9, 2020
A school of thought contends that human decision making exhibits quantum-like logic. While it is not known whether the brain may indeed be driven by actual quantum mechanisms, some researchers suggest that the decision logic is phenomenologically non...
Methods for accelerating geospatial data processing using quantum computers
Maxwell P. Henderson, Jarred Gallina, M. Brett·Apr 7, 2020
Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning processes. Machine learning processes enable automated image classification ...
Optimizing quantum annealing schedules with Monte Carlo tree search enhanced with neural networks
Yu-Qin Chen, Yu Chen, Chee-Kong Lee +2 more·Apr 6, 2020
Quantum annealing is a practical approach to approximately implement the adiabatic quantum computational model in a real-world setting. The goal of an adiabatic algorithm is to prepare the ground state of a problem-encoded Hamiltonian at the end of a...
Tunable Quantum Neural Networks for Boolean Functions
Viet Pham Ngoc, H. Wiklicky·Mar 31, 2020
In this paper we propose a new approach to quantum neural networks. Our multi-layer architecture avoids the use of measurements that usually emulate the non-linear activation functions which are characteristic of the classical neural networks. Despit...
Quantum Semantic Learning by Reverse Annealing an Adiabatic Quantum Computer
Lorenzo Rocutto, C. Destri, E. Prati·Mar 25, 2020
Boltzmann Machines constitute a class of neural networks with applications to image reconstruction, pattern classification and unsupervised learning in general. Their most common variants, called Restricted Boltzmann Machines (RBMs) exhibit a good tr...
Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components
Chen Miao, Shaohua Ma·Mar 23, 2020
For a linear system, the response to a stimulus is often superposed by its responses to other decomposed stimuli. In quantum mechanics, a state is the superposition of multiple eigenstates. Here, by taking advantage of the phase difference, a common ...
Realising and compressing quantum circuits with quantum reservoir computing
Sanjib Ghosh, Tanjung Krisnanda, T. Paterek +1 more·Mar 21, 2020
Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture w...
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
·Mar 6, 2020
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative...
Layerwise learning for quantum neural networks
Andrea Skolik, J. McClean, M. Mohseni +2 more·Mar 2, 2020
With the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of parametrized quantum circuits, strategies for effective training are be...
Event Classification with Quantum Machine Learning in High-Energy Physics
K. Terashi, M. Kaneda, T. Kishimoto +3 more·Feb 23, 2020
We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach ...
A hybrid quantum enabled RBM advantage: convolutional autoencoders for quantum image compression and generative learning
Jennifer Sleeman, J. Dorband, M. Halem·Jan 31, 2020
Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work explores the feasibility of using the D-Wave as a sampler for a machine learning task. We describe a hybrid method that combine...
Temporal Information Processing on Noisy Quantum Computers
Jiayin Chen, H. Nurdin, N. Yamamoto·Jan 26, 2020
The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems. Reservoir computing is an efficient learning paradigm that utilizes nonlinear dynamical systems for temporal i...
Parametric Probabilistic Quantum Memory
Rodrigo S. Sousa, Priscila G. M. dos Santos, T. M. L. Veras +2 more·Jan 11, 2020
Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural...