Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics
Agnes Valenti, Guliuxin Jin, J. L'eonard +2 more·Mar 1, 2021
Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large sc...
Variational Learning for Quantum Artificial Neural Networks
F. Tacchino, Stefano Mangini, P. Barkoutsos +4 more·Feb 26, 2021
In the past few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing...
Training Gaussian boson sampling by quantum machine learning
C. Conti·Feb 24, 2021
We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate comp...
Clustering by quantum annealing on the three-level quantum elements qutrits
V. Zobov, I. Pichkovskiy·Feb 18, 2021
Clustering is grouping of data by the proximity of some properties. We report on the possibility of increasing the efficiency of clustering of points in a plane using artificial quantum neural networks after the replacement of the two-level neurons c...
Scalable Neural Decoder for Topological Surface Codes.
Kai Meinerz, Chae-Yeun Park, S. Trebst·Jan 18, 2021
With the advent of noisy intermediate-scale quantum (NISQ) devices, practical quantum computing has seemingly come into reach. However, to go beyond proof-of-principle calculations, the current processing architectures will need to scale up to larger...
Classical Artificial Neural Network Training Using Quantum Walks as a Search Procedure
Luciano S. de Souza, Jonathan H. A. de Carvalho, T. Ferreira·Jan 13, 2021
This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights ...
Quantum Generative Models for Small Molecule Drug Discovery
Junde Li, R. Topaloglu, Swaroop Ghosh·Jan 9, 2021
Existing drug discovery pipelines take 5–10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space, which could be on the order of $10^{60}$. Deep...
Miniaturizing neural networks for charge state autotuning in quantum dots
Stefanie Czischek, Victor Yon, Marc-Antoine Genest +8 more·Jan 8, 2021
A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots (QDs), the gate voltages required to stabilize quantized charges are unique for each individual qubit, resulting in a high-dim...
Markovian Quantum Neuroevolution for Machine Learning
Zhide Lu, Pei-Xin Shen, D. Deng·Dec 30, 2020
Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks. It bears a number of intriguing capabilities that are typically inaccessible to gradient...
When Machine Learning Meets Quantum Computers: A Case Study
Weiwen Jiang, Jinjun Xiong, Yiyu Shi·Dec 18, 2020
Along with the development of AI democratization, the machine learning approach, in particular neural networks, has been applied to wide-range applications. In different application scenarios, the neural network will be accelerated on the tailored co...
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
Jiahao Yao, Paul Köttering, Hans Gundlach +2 more·Dec 12, 2020
Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-bod...
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
Tom Struck, Javed Lindner, Arne Hollmann +4 more·Dec 8, 2020
Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of single-shot spin detection events, which are the readout signal of our qu...
Performance of Particle Tracking Using a Quantum Graph Neural Network
Cenk Tuysuz, Kristiane Novotny, C. Rieger +7 more·Dec 2, 2020
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, ...
A Hybrid System for Learning Classical Data in Quantum States
S. Stein, Ryan L'Abbate, W. Mu +6 more·Dec 1, 2020
Deep neural network powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of comp...
Effect of barren plateaus on gradient-free optimization
A. Arrasmith, M. Cerezo, Piotr Czarnik +2 more·Nov 24, 2020
Barren plateau landscapes correspond to gradients that vanish exponentially in the number of qubits. Such landscapes have been demonstrated for variational quantum algorithms and quantum neural networks with either deep circuits or global cost functi...
Reservoir Computing Approach to Quantum State Measurement
Gerasimos Angelatos, S. Khan, H. Türeci·Nov 19, 2020
Rapid and accurate quantum state measurement is important for maximizing the extracted information from a quantum system. Its optimization plays a critical role in particular for multi-qubit quantum processors deployed for NISQ-era quantum algorithms...
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
Enrico Fontana, M. Cerezo, A. Arrasmith +2 more·Nov 17, 2020
Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs). Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum Algorithms, which may allow for near-term quantum advantage. Such applications ...
Scrambling ability of quantum neural network architectures
Yadong Wu, Pengfei Zhang, H. Zhai·Nov 16, 2020
In this letter we propose a general principle for how to build up a quantum neural network with high learning efficiency. Our stratagem is based on the equivalence between extracting information from input state to readout qubit and scrambling inform...
Accelerating spiking neural networks using quantum algorithm with high success probability and high calculation accuracy
Yanhu Chen, Cen Wang, Hongxiang Guo +2 more·Nov 10, 2020
Utilizing quantum computers to deploy artificial neural networks (ANNs) will bring the potential of significant advancements in both speed and scale. In this paper, we propose a kind of quantum spike neural networks (SNNs) as well as comprehensively ...
Qualities, challenges and future of genetic algorithms: a literature review
A. Vié, Alissa M. Kleinnijenhuis, D. Farmer·Nov 5, 2020
Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to mod...