Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Application of quantum machine learning using variational quantum classifier in accelerator physics
He-Xing Yin, Zhi-Yuan Hu, H. Zeng +2 more·Jun 7, 2025
Quantum machine learning algorithms aim to take advantage of quantum computing to improve classical machine learning algorithms. In this paper, we have applied a quantum machine learning algorithm, the variational quantum classifier for the first tim...
Quantum sequel of neural network training
Hao Zhang, Alex Kamenev·Jun 5, 2025
Training of neural networks (NNs) has emerged as a major consumer of both computational and energy resources. Quantum computers were coined as a root to facilitate training, but no experimental evidence has been presented so far. Here we demonstrate ...
Hybrid between biologically- and quantum-inspired many-body states
Miha Srdinvsek, Xavier Waintal·Jun 5, 2025
Deep neural networks can represent very different sorts of functions, including complex quantum many-body states. Tensor networks can also represent these states, have more structure and are easier to optimize. However, they can be prohibitively cost...
RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations
Swagat Kumar, Jan-Nico Zaech, C. M. Wilmott +1 more·Jun 4, 2025
Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers. However, choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task. Quantum Archite...
Learning thermodynamic master equations for open quantum systems
Peter Sentz, Stanley Nicholson, Yujin Cho +3 more·Jun 2, 2025
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of way...
Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments
Umberto Gonccalves de Sousa·Jun 2, 2025
This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for"Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integ...
Quantum Machine Learning for Predicting Anastomotic Leak: A Clinical Study
V. Nov'ak, Ivan Zelinka, Lenka Pvribylov'a +2 more·Jun 2, 2025
Anastomotic leak (AL) is a life-threatening complication following colorectal surgery, and its accurate prediction remains a significant clinical challenge. This study explores the potential of Quantum Neural Networks (QNNs) for AL prediction, presen...
Importance sampling for data-driven decoding of quantum error-correcting codes
Evan Peters·May 28, 2025
Data-driven decoding (DDD) - learning to decode syndromes of (quantum) error-correcting codes by learning from data - can be a difficult problem due to several atypical and poorly understood properties of the training data. We introduce a theory of e...
Neural Network Assisted Fermionic Compression Encoding: A Lossy-QSCI Framework for Resource-Efficient Ground-State Simulations
Yu-Cheng Chen, Ronin Wu, M. H. Cheng +1 more·May 23, 2025
Quantum computing promises to revolutionize many-body simulations for quantum chemistry, but its potential is constrained by limited qubits and noise in current devices. In this work, we introduce the Lossy Quantum Selected Configuration Interaction ...
Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor
Hai-Feng Zhang, Zhao-Yun Chen, Peng Wang +17 more·May 22, 2025
Quantum machine learning (QML) models, like their classical counterparts, are vulnerable to adversarial attacks, hindering their secure deployment. Here, we report the first systematic experimental robustness benchmark for 20-qubit quantum neural net...
Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data
Yun-Cheng Tsai, Samuel Yen-Chi Chen·May 22, 2025
Blockchain transaction data exhibits high dimensionality, noise, and intricate feature entanglement, presenting significant challenges for traditional clustering algorithms. In this study, we conduct a comparative analysis of three clustering approac...
On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features
Mehrad Sahebi, Alice Barthe, Yudai Suzuki +2 more·May 21, 2025
In the quest for quantum advantage, a central question is under what conditions can classical algorithms achieve a performance comparable to quantum algorithms--a concept known as dequantization. Random Fourier features (RFFs) have demonstrated poten...
Transformer-Based Neural Quantum Digital Twins for Many-Body Quantum Simulation and Optimal Annealing Schedule Design
Jianlong Lu, Hanqiu Peng, Ying Chen·May 21, 2025
We introduce Transformer-based Neural Quantum Digital Twins (Tx-NQDTs) to simulate full adiabatic dynamics of many-body quantum systems, including ground and low-lying excited states, at low computational cost. Tx-NQDTs employ a graph-informed Transf...
Solving MNIST with a globally trained Mixture of Quantum Experts
Paolo Alessandro Xavier Tognini, L. Banchi, Giacomo De Palma·May 20, 2025
We propose a new quantum neural network for image classification, which is able to classify the parity of the MNIST dataset with full resolution with a test accuracy of up to 97.5% without any classical pre-processing or post-processing. Our architec...
Efficient Generation of Parameterised Quantum Circuits from Large Texts
C. Krawchuk, Nikhil Khatri, Neil John Ortega +1 more·May 19, 2025
Quantum approaches to natural language processing (NLP) are redefining how linguistic information is represented and processed. While traditional hybrid quantum-classical models rely heavily on classical neural networks, recent advancements propose a...
Joint Optimization of Routing and Purification to Meet Fidelity Targets in Quantum Networks
Gongyu Ni, Holger Claussen, Lester Ho·May 18, 2025
Quantum networks rely on high-fidelity entanglement links, but achieving target fidelity often increases latency and Bell pair consumption due to purification. This paper proposes a cost-based scheduler that jointly optimizes path selection and purif...
Quantum-Enhanced Channel Mixing in RWKV Models for Time Series Forecasting
Chi-Sheng Chen, En-Jui Kuo·May 18, 2025
Recent advancements in neural sequence modeling have led to architectures such as RWKV, which combine recurrent-style time mixing with feedforward channel mixing to enable efficient long-context processing. In this work, we propose QuantumRWKV, a hyb...
Learning to Program Quantum Measurements for Machine Learning
S. Y. Chen, H. Tseng, Hsin-Yi Lin +1 more·May 18, 2025
The rapid advancements in quantum computing (QC) and machine learning (ML) have sparked significant interest, driving extensive exploration of quantum machine learning (QML) algorithms to address a wide range of complex challenges. The development of...
Quantum-Evolutionary Neural Networks for Multi-Agent Federated Learning
Aarav Lala, Kalyan Cherukuri·May 16, 2025
As artificial intelligence continues to drive innovation in complex, decentralized environments, the need for scalable, adaptive, and privacy-preserving decision-making systems has become critical. This paper introduces a novel framework combining qu...
Research of the Variational Shadow Quantum Circuit Based on the Whale Optimization Algorithm in Image Classification
Shuang Wu, Xueliang Song, Yumin Dong +1 more·May 15, 2025
In order to explore the possibility of cross-fertilization between quantum computing and neural networks as well as to improve the classification performance of quantum neural networks, this paper proposes an improved Variable Split Shadow Quantum Ci...