Papers
Live trends in quantum computing research, updated daily from arXiv.
Total Papers
27,548
This Month
1,041
Today
0
Research Volume
12,931 papers in 12 months (-5% vs prior quarter)
Research Focus Areas
Papers by research theme (12 months). Hover for details.
Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Qubit-Wise Architecture Search Method for Variational Quantum Circuits
Jialin Chen, Zhiqiang Cai, Ke Xu +2 more·Mar 7, 2024
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural architecture search (NAS), quantum ...
Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations
Yin Mo, Lei Zhang, Yuanyi Chen +3 more·Mar 6, 2024
Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quantum comb remains a challenging...
Quantum Mixed-State Self-Attention Network
Fu Chen, Qinglin Zhao, Li Feng +3 more·Mar 5, 2024
Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language...
Operator Learning Renormalization Group
Xiu-Zhe Luo, D. Luo, R. Melko·Mar 5, 2024
In this paper, we present a general framework for quantum many-body simulations called the operator learning renormalization group (OLRG). Inspired by machine learning perspectives, OLRG is a generalization of Wilson's numerical renormalization group...
Computing exact moments of local random quantum circuits via tensor networks
Paolo Braccia, Pablo Bermejo, L. Cincio +1 more·Mar 4, 2024
A basic primitive in quantum information is the computation of the moments EU[Tr[UρU†O]t]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \use...
Reducing the Error Rate of a Superconducting Logical Qubit using Analog Readout Information
Hany Ali, J. Marques, Ophelia Crawford +7 more·Mar 1, 2024
Quantum error correction allows for quantum information to be preserved using logical qubits, which are subject to lower error rates than their constituent physical qubits. The degree of error suppression depends on the choice of error correcting cod...
Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks
Patrick Holzer, Ivica Turkalj·Feb 22, 2024
We analyze the frequency spectrum of Quantum Neural Networks (QNNs) using Minkowski sums, which yields a compact algebraic description and permits explicit computation. Using this description, we prove several maximality results for broad classes of ...
Quantum Theory and Application of Contextual Optimal Transport
Nicola Mariella, A. Akhriev, F. Tacchino +9 more·Feb 22, 2024
Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements $(\boldsymbol{\mu}, \boldsymbol{\nu})$ are coupled to covariates, a challenging conditional distribution learning setting arises. Existing appr...
A Quick Introduction to Quantum Machine Learning for Non-Practitioners
Ethan N. Evans, Dominic M. Byrne, Matthew Cook·Feb 22, 2024
This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles g...
Quantum Annealing and GNN for Solving TSP with QUBO
Haoqi He·Feb 21, 2024
This paper explores the application of Quadratic Unconstrained Binary Optimization (QUBO) models in solving the Travelling Salesman Problem (TSP) through Quantum Annealing algorithms and Graph Neural Networks. Quantum Annealing (QA), a quantum-inspir...
Neural-network quantum states for many-body physics
Matija Medvidović, Javier Robledo Moreno·Feb 16, 2024
Variational quantum calculations have borrowed many tools and algorithms from the machine learning community in the recent years. Leveraging great expressive power and efficient gradient-based optimization, researchers have shown that trial states in...
A Comparative Analysis of Hybrid-Quantum Classical Neural Networks
K. Zaman, Tasnim Ahmed, M. Hanif +2 more·Feb 16, 2024
Hybrid Quantum-Classical Machine Learning (ML) is an emerging field, amalgamating the strengths of both classical neural networks and quantum variational circuits on the current noisy intermediate-scale quantum devices. This paper performs an extensi...
Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks
K. Zaman, Tasnim Ahmed, Muhammad Kashif +3 more·Feb 16, 2024
In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural networks (HQNNs) represent a promising solution that combines the strengths of classical machine learning with quantum computing capabilities. Compared to classical ...
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Eric R. Anschuetz, Xun Gao·Feb 13, 2024
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in ...
QUAPPROX: A Framework for Benchmarking the Approximability of Variational Quantum Circuit
Jinyang Li, Ang Li, Weiwen Jiang·Feb 13, 2024
Most of the existing quantum neural network models, such as variational quantum circuits (VQCs), are limited in their ability to explore the non-linear relationships in input data. This gradually becomes the main obstacle for it to tackle realistic a...
Variational post-selection for ground states and thermal states simulation
Shi-Xin Zhang, Jiaqi Miao, Chang-Yu Hsieh·Feb 12, 2024
Variational quantum algorithms, as one of the most promising routes in the noisy intermediate-scale quantum era, offer various potential applications while also confronting severe challenges due to near-term quantum hardware restrictions. In this wor...
Challenges and opportunities in the supervised learning of quantum circuit expectation values.
S. Cantori, S. Pilati·Feb 7, 2024
Recently, deep neural networks have been proven capable of predicting output expectation values of certain random quantum circuits via a supervised learning approach. Here we investigate the potential of this possible approach to the emulation of qua...
Unleashing the expressive power of pulse-based quantum neural networks
Han-Xiao Tao, Jiaqi Hu, Re-Bing Wu·Feb 5, 2024
Quantum machine learning (QML) based on Noisy Intermediate-Scale Quantum (NISQ) devices hinges on the optimal utilization of limited quantum resources. The broadly used gate-based QML models are user-friendly for software engineers, but their express...
Correlated optical convolutional neural network with “quantum speedup”
Yifan Sun, Qian Li, Ling‐Jun Kong +1 more·Jan 31, 2024
Compared with electrical neural networks, optical neural networks (ONNs) have the potentials to break the limit of the bandwidth and reduce the consumption of energy, and therefore draw much attention in recent years. By far, several types of ONNs ha...
Quantum Time Dynamics Mediated by the Yang-Baxter Equation and Artificial Neural Networks.
Sahil Gulania, Yuri Alexeev, Stephen K. Gray +2 more·Jan 30, 2024
Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang-Baxter equation (YBE). Unlike traditional error mi...