Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Fragmentation is Efficiently Learnable by Quantum Neural Networks
Mikhail Mints, Eric R. Anschuetz·Nov 30, 2025
Hilbert space fragmentation is a phenomenon in which the Hilbert space of a quantum system is dynamically decoupled into exponentially many Krylov subspaces. We can define the Schur transform as a unitary operation mapping some set of preferred bases...
Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification
Ece Yurtseven·Nov 29, 2025
Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Net...
Nonstabilizerness Estimation using Graph Neural Networks
Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis +2 more·Nov 28, 2025
This article proposes a Graph Neural Network (GNN) approach to estimate nonstabilizerness in quantum circuits, measured by the stabilizer Rényi entropy (SRE). Nonstabilizerness is a fundamental resource for quantum advantage, and efficient SRE estima...
RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks
Tobias Meuser, Jannis Weil, Aninda Lahiri +1 more·Nov 27, 2025
Quantum networks are becoming increasingly important because of advancements in quantum computing and quantum sensing, such as recent developments in distributed quantum computing and federated quantum machine learning. Routing entanglement in quantu...
Resource assessment of classical and quantum hardware for post-quench dynamics
Joseph Vovrosh, Tiago Mendes-Santos, Hadriel Mamann +6 more·Nov 25, 2025
We estimate the run-time and energy consumption of simulating non-equilibrium dynamics on neutral atom quantum computers in analog mode, directly comparing their performance to state-of-the-art classical methods, namely Matrix Product States and Neur...
Neural surrogates for designing gravitational wave detectors
Carlos Ruiz-Gonzalez, Sören Arlt, Sebastian Lehner +5 more·Nov 24, 2025
Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to automate experimental design, often via combinatorial search and optim...
Simulating dynamics of the two-dimensional transverse-field Ising model: a comparative study of large-scale classical numerics
Joseph Vovrosh, Sergi Julià-Farré, Wladislaw Krinitsin +11 more·Nov 24, 2025
The quantum dynamics of many-qubit systems is an outstanding problem that has recently driven significant advances in both numerical methods and programmable quantum processing units. In this work, we employ a comprehensive toolbox of state-of-the-ar...
Performance Guarantees for Quantum Neural Estimation of Entropies
Sreejith Sreekumar, Ziv Goldfeld, Mark M. Wilde·Nov 24, 2025
Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neural estimators (QNEs), which utilize a hybrid classical-quantum architecture, have recently emerged as an ap...
Neural Architecture Search for Quantum Autoencoders
Hibah Agha, Samuel Yen-Chi Chen, Huan-Hsin Tseng +1 more·Nov 24, 2025
In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum compu...
Feature Ranking in Credit-Risk with Qudit-Based Networks
Georgios Maragkopoulos, Lazaros Chavatzoglou, Aikaterini Mandilara +1 more·Nov 24, 2025
In finance, predictive models must balance accuracy and interpretability, particularly in credit risk assessment, where model decisions carry material consequences. We present a quantum neural network (QNN) based on a single qudit, in which both data...
Neural network approximation of regularized density functionals
Mihály A. Csirik, Andre Laestadius, Mathias Oster·Nov 23, 2025
Density functional theory is one of the most efficient and widely used computational methods of quantum mechanics, especially in fields such as solid state physics and quantum chemistry. From the theoretical perspecive, its central object is the univ...
Improved error correction with leakage reduction units built into qubit measurement in a superconducting quantum processor
Yuejie Xin, Sean L. M. van der Meer, Marc Serra-Peralta +5 more·Nov 21, 2025
Leakage to non-computational states is a source of correlated errors in both time and space that limits the effectiveness of quantum error correction (QEC) with superconducting circuits. We present and experimentally demonstrate a high-fidelity, leak...
Intrinsic preservation of plasticity in continual quantum learning
Yu-Qin Chen, Shi-Xin Zhang·Nov 21, 2025
Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning. However, standard deep learning suffers from a fundamental issue: loss of plasticity, in which networks gradually lose their ability to learn fr...
Quantum Data Learning of Topological-to-Ferromagnetic Phase Transitions in the 2+1D Toric Code Loop Gas Model
Shamminuj Aktar, Rishabh Bhardwaj, Andreas Bärtschi +2 more·Nov 20, 2025
Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states, bypassing the need for any identification of the classical observable of the theory. A central challenge in many-body physics is that the ...
Optimizing Quantum Key Distribution Network Performance using Graph Neural Networks
Akshit Pramod Anchan, Ameiy Acharya, Leki Chom Thungon·Nov 20, 2025
This paper proposes an optimization of Quantum Key Distribution (QKD) Networks using Graph Neural Networks (GNN) framework. Today, the development of quantum computers threatens the security systems of classical cryptography. Moreover, as QKD network...
Approximation rates of quantum neural networks for periodic functions via Jackson's inequality
Ariel Neufeld, Philipp Schmocker, Viet Khoa Tran·Nov 20, 2025
Quantum neural networks (QNNs) are an analog of classical neural networks in the world of quantum computing, which are represented by a unitary matrix with trainable parameters. Inspired by the universal approximation property of classical neural net...
QSentry: Backdoor Detection for Quantum Neural Networks via Measurement Clustering
Shuolei Wang, Zimeng Xiao, Jinjing Shi +3 more·Nov 19, 2025
Quantum neural networks (QNNs) are an important model for implementing quantum machine learning (QML), while they demonstrate a high degree of vulnerability to backdoor attacks similar to classical networks. To address this issue, a quantum backdoor ...
Fidelity-Preserving Quantum Encoding for Quantum Neural Networks
Yuhu Lu, Jinjing Shi·Nov 19, 2025
Efficiently encoding classical visual data into quantum states is essential for realizing practical quantum neural networks (QNNs). However, existing encoding schemes often discard spatial and semantic information when adapting high-dimensional image...
Vehicle Routing Problems via Quantum Graph Attention Network Deep Reinforcement Learning
Le Tung Giang, Vu Hoang Viet, Nguyen Xuan Tung +2 more·Nov 19, 2025
The vehicle routing problem (VRP) is a fundamental NP-hard task in intelligent transportation systems with broad applications in logistics and distribution. Deep reinforcement learning (DRL) with Graph Neural Networks (GNNs) has shown promise, yet cl...
Intelligent Inverse Design of Multi-Layer Metasurface Cavities for Dual Resonance Enhancement of Nanodiamond Single Photon Emitters
Omar A. M. Abdelraouf·Nov 19, 2025
Single-photon emitters (SPEs) based on nitrogen-vacancy centers in nanodiamonds (neutral NV0 (wavelength 575 nm) and negative NV- (wavelength 637 nm)) represent promising platforms for quantum nanophotonics applications, yet their emission efficienci...