Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
U(1)-symmetric recurrent neural networks for quantum state reconstruction
Stewart Morawetz, Isaac J. S. De Vlugt, J. Carrasquilla +1 more·Oct 27, 2020
Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experimental measurements, and can aid in the calculation of physical observable...
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen +4 more·Oct 26, 2020
We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for...
Quantum machine learning for particle physics using a variational quantum classifier
A. Blance, M. Spannowsky·Oct 14, 2020
Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving clas...
Neural Group Actions
Span Spanbauer, Luke Sciarappa·Oct 8, 2020
We introduce an algorithm for designing Neural Group Actions, collections of deep neural network architectures which model symmetric transformations satisfying the laws of a given finite group. This generalizes involutive neural networks $\mathcal{N}...
Adaptive pruning-based optimization of parameterized quantum circuits
Sukin Sim, J. Romero, J. Gonthier +1 more·Oct 1, 2020
Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by t...
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
T. M. Khan, A. Robles-Kelly·Sep 23, 2020
In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. Here, we depart from the classical perceptron and the elemental operations on quantum bits, i.e. qubits, so as to formulate ...
Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
I. D. Lazarev, Marek Narozniak, T. Byrnes +1 more·Sep 19, 2020
Unsupervised machine learning is one of the main techniques employed in artificial intelligence. Quantum computers offer opportunities to speed up such machine learning techniques. Here, we introduce an algorithm for quantum assisted unsupervised dat...
Coordinated inference, holographic neural networks, and quantum error correction
A. Patrascu·Sep 17, 2020
Coordinated inference problems are being introduced as a basis for a neural network representation of the locality problem in the holographic bulk. It is argued that a type of problem originating in the ‘prisoners and hats’ dilemma involves non-local...
Quantum Long Short-Term Memory
Samuel Yen-Chi Chen, Shinjae Yoo, Yao-Lung L. Fang·Sep 3, 2020
Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence and temporal dependency data modeling and its effectiveness has been extensively established. In this work, we propose a hybrid quantum-classical model of LSTM, w...
A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud
Wenjie Liu, Ying Zhang, Zhiliang Deng +2 more·Sep 3, 2020
As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial ne...
Classical variational simulation of the Quantum Approximate Optimization Algorithm
Matija Medvidović, Giuseppe Carleo·Sep 3, 2020
A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating quantum syst...
Solving the Liouvillian Gap with Artificial Neural Networks.
D. Yuan, He Wang, Zhong Wang +1 more·Aug 31, 2020
We propose a machine-learning inspired variational method to obtain the Liouvillian gap, which plays a crucial role in characterizing the relaxation time and dissipative phase transitions of open quantum systems. By using "spin bi-base mapping," we m...
Neural-network variational quantum algorithm for simulating many-body dynamics
Chee-Kong Lee, P. Patil, Shengyu Zhang +1 more·Aug 31, 2020
We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems. Based on a modified restricted Boltzmann machine (RBM) wavefunction ansatz, the proposed algorithm can be efficiently implemented i...
Solving quantum master equations with deep quantum neural networks
Zidu Liu, L. Duan, D. Deng·Aug 12, 2020
Deep quantum neural networks may provide a promising way to achieve quantum learning advantage with noisy intermediate scale quantum devices. Here, we use deep quantum feedforward neural networks capable of universal quantum computation to represent ...
Quantum computing model of an artificial neuron with continuously valued input data
Stefano Mangini, F. Tacchino, D. Gerace +2 more·Jul 28, 2020
Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration...
Enhanced Quantum Key Distribution using Hybrid Channels and Natural Random Numbers
Hemant Rana, Nitin Verma·Jul 28, 2020
Since the introduction of quantum computation by Richard Feynman in 1982, Quantum computation has shown exemplary results in various applications of computer science including unstructured database search, factorization, molecular simulations to name...
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
Cenk Tuysuz, F. Carminati, B. Demirkoz +6 more·Jul 14, 2020
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching...
A co-design framework of neural networks and quantum circuits towards quantum advantage
Weiwen Jiang, Jinjun Xiong, Yiyu Shi·Jun 26, 2020
Despite the pursuit of quantum advantages in various applications, the power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that effectively designs a neural network suitable for quantum ...
Deep learning enhanced individual nuclear-spin detection
Kyunghoon Jung, M. Abobeih, Jiwon Yun +5 more·Jun 24, 2020
The detection of nuclear spins using individual electron spins has enabled diverse opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin samples and ...
Continuous Variable Single Mode Quantum Decoder for Image Reconstruction and Denoising
J. Basani, A. Bhattacherjee·Jun 19, 2020
Quantum computation using optical modes has been well-established in its ability to construct deep neural networks. We introduce a model that is the quantum analogue of the classical autoencoder - a neural network model that can reconstruct its input...