Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
The PID Controller Strikes Back: Classical Controller Helps Mitigate Barren Plateaus in Noisy Variational Quantum Circuits
Zhehao Yi, Rahul Bhadani·Nov 18, 2025
Variational quantum algorithms (VQAs) combine the advantages of classical optimization and quantum computation, making them one of the most promising approaches in the Noisy Intermediate-Scale Quantum (NISQ) era. However, when optimized using gradien...
A Global Spacetime Optimization Approach to the Real-Space Time-Dependent Schrödinger Equation
Enze Hou, Yuzhi Liu, Linxuan Zhang +3 more·Nov 17, 2025
The time-dependent Schrödinger equation (TDSE) in real space is fundamental to understanding the dynamics of many-electron quantum systems, with applications ranging from quantum chemistry to condensed matter physics and materials science. However, s...
Quantum Orthogonal Separable Physics-Informed Neural Networks
Pietro Zanotta, Ljubomir Budinski, Caglar Aytekin +1 more·Nov 16, 2025
This paper introduces Quantum Orthogonal Separable Physics-Informed Neural Networks (QO-SPINNs), a novel architecture for solving Partial Differential Equations, integrating quantum computing principles to address the computational bottlenecks of cla...
Autonomously Designed Pulses for Precise, Site-Selective Control of Atomic Qubits
Sanghyo Park, Seuk Lee, Keunyoung Lee +2 more·Nov 16, 2025
Quantum computers based on cold-atom arrays offer long-lived qubits with programmable connectivity, yet their progress toward fault-tolerant operation is limited by the relatively low fidelity of site-selective local control. We introduce an artifici...
Machine Learning Framework for Efficient Prediction of Quantum Wasserstein Distance
Changchun Feng, Xinyu Qiu, Laifa Tao +1 more·Nov 16, 2025
The quantum Wasserstein distance (W-distance) is a fundamental metric for quantifying the distinguishability of quantum operations, with critical applications in quantum error correction. However, computing the W-distance remains computationally chal...
Leveraging Quantum-Based Architectures for Robust Diagnostics
Shabnam Sodagari, Tommy Long·Nov 15, 2025
The objective of this study is to diagnose and differentiate kidney stones, cysts, and tumors using Computed Tomography (CT) images of the kidney. This study leverages a hybrid quantum-classical framework in this regard. We combine a pretrained ResNe...