Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Amplitude Ratios and Neural Network Quantum States
Vojtěch Havlíček·Jan 22, 2022
Neural Network Quantum States (NQS) represent quantum wavefunctions by artificial neural networks. Here we study the wavefunction access provided by NQS defined in [Science, 355, 6325, pp. 602-606 (2017)] and relate it to results from distribution te...
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
Xing Ai, Zhihong Zhang, Luzhe Sun +2 more·Jan 13, 2022
Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert sp...
Systematic Literature Review: Quantum Machine Learning and its applications
David Peral Garc'ia, Juan Cruz-Benito, Francisco Jos'e Garc'ia-Penalvo·Jan 11, 2022
Quantum computing is the process of performing calculations using quantum mechanics. This field studies the quantum behavior of certain subatomic particles for subsequent use in performing calculations, as well as for large-scale information processi...
Exploring superadditivity of coherent information of noisy quantum channels through genetic algorithms
Govind Lal Sidhardh, Mir Alimuddin, Manik Banik·Jan 11, 2022
Machine learning techniques are increasingly being used in fundamental research to solve various challenging problems. Here we explore one such technique to address an important problem in quantum communication scenario. While transferring quantum in...
Quantum activation functions for quantum neural networks
Marco Maronese, C. Destri, E. Prati·Jan 10, 2022
The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural networks,...
A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification
Davis Arthur, Prasanna Date·Jan 5, 2022
Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become ...
An approach to interfacing the brain with quantum computers: practical steps and caveats
E. Miranda, S. Venkatesh, J. Martín-Guerrero +3 more·Jan 4, 2022
We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed a method to encode neural correlates of mental activity as instructions for a quantum computer. Brain signals are detected uti...
Variational Quantum‐Neural Hybrid Error Mitigation
Shi-Xin Zhang, Z. Wan, Chang-Yu Hsieh +2 more·Dec 20, 2021
Quantum error mitigation (QEM) is crucial for obtaining reliable results on quantum computers by suppressing quantum noise with moderate resources. It is a key factor for successful and practical quantum algorithm implementations in the noisy interme...
Variational Quantum Soft Actor-Critic
Qingfeng Lan·Dec 20, 2021
Quantum computing has a superior advantage in tackling specific problems, such as integer factorization and Simon's problem. For more general tasks in machine learning, by applying variational quantum circuits, more and more quantum algorithms have b...
Quantum Approximate Optimization Algorithm applied to the binary perceptron
Pietro Torta, G. Mbeng, Carlo Baldassi +2 more·Dec 19, 2021
We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance w...
Explainable natural language processing with matrix product states
J. Tangpanitanon, Chanatip Mangkang, P. Bhadola +3 more·Dec 16, 2021
Despite empirical successes of recurrent neural networks (RNNs) in natural language processing (NLP), theoretical understanding of RNNs is still limited due to intrinsically complex non-linear computations. We systematically analyze RNNs’ behaviors i...
Building separable approximations for quantum states via neural networks
Antoine Girardin, N. Brunner, Tam'as Kriv'achy·Dec 15, 2021
Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable. To tackle this task, we parametrize separable states with a neural network and t...
Dissipative quantum generative adversarial networks
Kerstin Beer, Gabriel Muller·Dec 11, 2021
Noisy intermediate-scale quantum (NISQ) devices build the first generation of quantum computers. Quantum neural networks (QNNs) gained high interest as one of the few suitable quantum algorithms to run on these NISQ devices. Most of the QNNs exploit ...
Quantum readout error mitigation via deep learning
Jihye Kim, Byungdu Oh, Y. Chong +2 more·Dec 7, 2021
Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since nois...
A Quantum Approach Towards the Adaptive Prediction of Cloud Workloads
Ashutosh Kumar Singh, D. Saxena, J. Kumar +1 more·Dec 1, 2021
This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating thi...
High robustness quantum walk search algorithm with qudit Householder traversing coin, machine learning study
Hristo Tonchev, Petar Danev·Nov 21, 2021
In this work the quantum random walk search algorithm with walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with arbitrary dimension. Monte Carlo simulations, in combina...
Generalization in quantum machine learning from few training data
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo +4 more·Nov 9, 2021
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehe...
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
Manuel S. Rudolph, Sukin Sim, A. Raza +5 more·Nov 8, 2021
Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near to mid-term quantum computers. There has been an increasing effort to study the intricacies of VQAs, such as the presence or absence of barren p...
Quantum algorithms for unsupervised machine learning and neural networks
Jonas Landman·Nov 5, 2021
Cette thèse vise à étudier si les algorithmes quantiques peuvent être utilisés dans le domaine de l'apprentissage automatique, ou intelligence artificielle. Nous rappelons d'abord les principes fondamentaux de l'apprentissage automatique et de l'in...
Graph neural network initialisation of quantum approximate optimisation
Nishant Jain, Brian Coyle, E. Kashefi +1 more·Nov 4, 2021
Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term. In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving ...