Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Continuous-time parametrization of neural quantum states for quantum dynamics
Dingzu Wang, Wenxuan Zhang, Xiansong Xu +1 more·Jul 11, 2025
Neural quantum states are a promising framework for simulating many-body quantum dynamics, as they can represent states with volume-law entanglement. As time evolves, the neural network parameters are typically optimized at discrete time steps to app...
A Neural-Guided Variational Quantum Algorithm for Efficient Sign Structure Learning in Hybrid Architectures
Mengzhen Ren, Yu-Cheng Chen, Yangsen Ye +3 more·Jul 10, 2025
Variational quantum algorithms hold great promise for unlocking the power of near-term quantum processors, yet high measurement costs, barren plateaus, and challenging optimization landscapes frequently hinder them. Here, we introduce sVQNHE, a neura...
Quantum and Hybrid Machine‐Learning Models for Materials‐Science Tasks
Leyang Wang, Yilun Gong, Zongrui Pei·Jul 10, 2025
Quantum computing has become increasingly practical in solving real‐world problems due to advances in hardware and algorithms. In this paper, we aim to design, apply, and evaluate quantum machine learning and hybrid quantum‐classical models in a few ...
Introduction to quantum error correction with stabilizer codes
Zachary P. Bradshaw, Jeffrey J. Dale, Ethan N. Evans·Jul 8, 2025
We give an introduction to the theory of quantum error correction using stabilizer codes that is geared towards the working computer scientists and mathematicians with an interest in exploring this area. To this end, we begin with an introduction to ...
Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification
Shaswata Mahernob Sarkar, Sheikh Iftekhar Ahmed, Jishnu Mahmud +2 more·Jul 2, 2025
Quantum Machine Learning (QML) has seen significant advancements, driven by recent improvements in Noisy Intermediate-Scale Quantum (NISQ) devices. Leveraging quantum principles such as entanglement and superposition, quantum convolutional neural net...
Improving GANs by leveraging the quantum noise from real hardware
Hongni Jin, Kenneth M. Merz·Jul 2, 2025
We propose a novel approach to generative adversarial networks (GANs) in which the standard i.i.d. Gaussian latent prior is replaced or hybridized with a quantum-correlated prior derived from measurements of a 16-qubit entangling circuit. Each latent...
Quantum Circuit Structure Optimization for Quantum Reinforcement Learning
Seokshin Son, Joongheon Kim·Jul 1, 2025
Reinforcement learning (RL) enables agents to learn optimal policies through environmental interaction. However, RL suffers from reduced learning efficiency due to the curse of dimensionality in high-dimensional spaces. Quantum reinforcement learning...
Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling
B. Rababah, Bilal Farooq·Jul 1, 2025
Quantum computing has opened new opportunities to tackle complex machine learning tasks, for instance, highdimensional data representations commonly required in intelligent transportation systems. We explore quantum machine learning to model complex ...
Quantum Physics-Informed Neural Networks for Maxwell's Equations: Circuit Design, "Black Hole" Barren Plateaus Mitigation, and GPU Acceleration
Ziv Chen, Gal G. Shaviner, Hemanth Chandravamsi +3 more·Jun 29, 2025
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving partial differential equations (PDEs) by embedding the governing physics into the loss function associated with a deep neural network. In this work, a Quantum P...
Experimental quantum reservoir computing with a circuit quantum electrodynamics system
Baptiste Carles, Julien Dudas, Léo Balembois +2 more·Jun 27, 2025
Quantum reservoir computing is a machine learning framework that offers ease of training compared to other quantum neural networks, as it does not rely on gradient-based optimization. Learning is performed in a single step on the output features meas...
Quanvolutional Neural Networks for Pneumonia Detection: An Efficient Quantum-Assisted Feature Extraction Paradigm
Gazi Tanbhir, Md. Farhan Shahriyar, Abdullah Md Raihan Chy·Jun 27, 2025
Pneumonia poses a significant global health challenge, demanding accurate and timely diagnosis. While deep learning, particularly Convolutional Neural Networks (CNNs), has shown promise in medical image analysis for pneumonia detection, CNNs often su...
Towards a Comparative Framework for Compositional AI Models
Tiffany Duneau·Jun 27, 2025
The DisCoCirc framework for natural language processing allows the construction of compositional models of text, by combining units for individual words together according to the grammatical structure of the text. The compositional nature of a model ...
Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone·Jun 26, 2025
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems built on spiking neurons excel at processing time series data efficient...
Practical insights on the effect of different encodings, ansätze and measurements in quantum and hybrid convolutional neural networks
Jesús Lozano-Cruz, Albert Nieto-Morales, Oriol Balló-Gimbernat +3 more·Jun 25, 2025
This study investigates the design choices of parameterized quantum circuits (PQCs) within quantum and hybrid convolutional neural network (HQNN and QCNN) architectures, applied to the task of satellite image classification using the EuroSAT dataset....
Computed tomography of propagating microwave photons
Qi-Ming Chen, Aarne Keranen, Aashish Sah +1 more·Jun 25, 2025
Propagating photons serve as essential links for distributing quantum information and entanglement across distant nodes. Knowledge of their Wigner functions not only enables their deployment as active information carriers but also provides error diag...
Iterative Quantum Feature Maps
Nasa Matsumoto, Quoc Hoan Tran, Koki Chinzei +2 more·Jun 24, 2025
Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have demonstrated rigorous end-to-end quantum speedups for specific famili...
Learning quantum tomography from incomplete measurements
Mateusz Krawczyk, Pavel Baláž, Katarzyna Roszak +1 more·Jun 24, 2025
We revisit quantum tomography in an informationally incomplete scenario and propose improved state reconstruction methods using deep neural networks. In the first approach, the trained network predicts an optimal linear or quadratic reconstructor wit...
Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos +2 more·Jun 24, 2025
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), ...
ReBoot: Encrypted Training of Deep Neural Networks with CKKS Bootstrapping
Alberto Pirillo, Luca Colombo·Jun 24, 2025
Growing concerns over data privacy underscore the need for deep learning methods capable of processing sensitive information without compromising confidentiality. Among privacy-enhancing technologies, Homomorphic Encryption (HE) stands out by offerin...
Quantum-Classical Hybrid Quantized Neural Network
Wenxin Li, Chuan Wang, Hongdong Zhu +4 more·Jun 23, 2025
In this work, we introduce a novel Quadratic Binary Optimization (QBO) framework for training a quantized neural network. The framework enables the use of arbitrary activation and loss functions through spline interpolation, while Forward Interval Pr...