Papers
Live trends in quantum computing research, updated daily from arXiv.
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13,008 papers in 12 months (-3% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Amplitude-based implementation of the unit step function on a quantum computer
Jonas Koppe, Mark-Oliver Wolf·Jun 7, 2022
Modelling non-linear activation functions on quantum computers is vital for quantum neurons employed in fully quantum neural networks, however, remains a challenging task. We introduce an amplitude-based implementation for approximating non-linearity...
Iterative optimization in quantum metrology and entanglement theory using semidefinite programming
Árpád Lukács, Róbert Trényi, Tamás Vértesi +1 more·Jun 6, 2022
We discuss efficient methods to optimize the metrological performance over local Hamiltonians in a bipartite quantum system. For a given quantum state, our methods find the best local Hamiltonian for which the state outperforms separable states the m...
Extracting electronic many-body correlations from local measurements with artificial neural networks
Faluke Aikebaier, T. Ojanen, J. Lado·Jun 6, 2022
The characterization of many-body correlations provides a powerful tool for analyzing correlated quantum materials. However, experimental extraction of quantum entanglement in correlated electronic systems remains an open problem in practice. In part...
Quantum Neural Network Classifiers: A Tutorial
Weikang Li, Zhide Lu, D. Deng·Jun 6, 2022
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both...
Clifford Algebras, Quantum Neural Networks and Generalized Quantum Fourier Transform
M. Trindade, V.N.A. Lula-Rocha, S. Floquet +1 more·Jun 3, 2022
We propose models of quantum perceptrons and quantum neural networks based on Clifford algebras. These models are capable to capture geometric features of classical and quantum data as well as producing data entanglement. Due to their representations...
Towards retrieving dispersion profiles using quantum-mimic Optical Coherence Tomography and Machine Learnin
Krzysztof A. Maliszewski, P. Kolenderski, V. Vetrova +1 more·May 30, 2022
Artefacts in quantum-mimic optical coherence tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation, which is used in the quantum entanglement mimicking algorith...
Estimation of the geometric measure of entanglement with Wehrl moments through artificial neural networks
J'erome Denis, F. Damanet, John Martin·May 30, 2022
In recent years, artificial neural networks (ANNs) have become an increasingly popular tool for studying problems in quantum theory, and in particular entanglement theory. In this work, we analyse to what extent ANNs can accurately predict the geomet...
QSpeech: Low-Qubit Quantum Speech Application Toolkit
Zhenhou Hong, Jianzong Wang, Xiaoyang Qu +3 more·May 26, 2022
Quantum devices with low qubits are common in the Noisy Intermediate-Scale Quantum (NISQ) era. However, Quantum Neural Network (QNN) running on low-qubit quantum devices would be difficult since it is based on Variational Quantum Circuit (VQC), which...
Avoiding Barren Plateaus with Classical Deep Neural Networks
Lucas Friedrich, J. Maziero·May 26, 2022
Variational quantum algorithms (VQAs) are among the most promising algorithms in the era of Noisy Intermediate Scale Quantum Devices. Such algorithms are constructed using a parameterization U($\pmb{\theta}$) with a classical optimizer that updates t...
Quantum variational learning for entanglement witnessing
Francesco Scala, Stefano Mangini, C. Macchiavello +2 more·May 20, 2022
Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means. There has been, however, a limited amount of work on the characteriza...
Quantum neural networks
Kerstin Beer·May 17, 2022
This PhD thesis combines two of the most exciting research areas of the last decades: quantum computing and machine learning. We introduce dissipative quantum neural networks (DQNNs), which are designed for fully quantum learning tasks, are capable o...
Evolution strategies: application in hybrid quantum-classical neural networks
Lucas Friedrich, J. Maziero·May 17, 2022
With the rapid development of quantum computers, several applications are being proposed for them. Quantum simulations, simulation of chemical reactions, solution of optimization problems and quantum neural networks (QNNs) are some examples. However,...
Equivariant quantum circuits for learning on weighted graphs
Andrea Skolik, Michele Cattelan, S. Yarkoni +2 more·May 12, 2022
Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors ...
Quantum variational algorithms are swamped with traps
E. Anschuetz, B. Kiani·May 11, 2022
One of the most important properties of classical neural networks is how surprisingly trainable they are, though their training algorithms typically rely on optimizing complicated, nonconvex loss functions. Previous results have shown that unlike the...
Practical application-specific advantage through hybrid quantum computing
M. Perelshtein, A. Sagingalieva, Karan Pinto +7 more·May 10, 2022
Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrable functionality, and it is expected that scaling up to millions of qub...
Quantum neural network autoencoder and classifier applied to an industrial case study
Stefano Mangini, A. Marruzzo, M. Piantanida +3 more·May 9, 2022
Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers, it is rel...
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
Zeyi Tao, Jindi Wu, Qi Xia +1 more·May 5, 2022
Variational quantum algorithms (VQAs) have recently received much attention due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized p...
Tunable Quantum Neural Networks in the QPAC-Learning Framework
Viet Pham Ngoc, David Tuckey, H. Wiklicky·May 3, 2022
In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the se...
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm
Nicola Franco, Tom Wollschlager, Nicholas Gao +2 more·May 2, 2022
In recent years, quantum computers and algorithms have made significant progress indicating the prospective importance of quantum computing (QC). Especially combinatorial optimization has gained a lot of attention as an application field for near-ter...
A walk through of time series analysis on quantum computers
A. Daskin·May 2, 2022
—Because of the rotational components on quantum circuits, some quantum neural networks based on variational circuits can be considered equivalent to the classical Fourier networks. In addition, they can be used to predict the Fourier coefficients of ...