Papers
Quantum-Circuit Framework for Two-Stage Stochastic Programming via QAOA Integrated with a Quantum Generative Neural Network
Taihei Kuroiwa, Daiki Yamazaki, Keita Takahashi +3 more·Dec 27, 2025
Two-stage stochastic programming often discretizes uncertainty into scenarios, but scenario enumeration makes expected recourse evaluation scale at least linearly in the scenario count. We propose qGAN-QAOA, a unified quantum-circuit workflow in whic...
AI-Accelerated Qubit Readout at the Single-Photon Level for Scalable Atomic Quantum Processors
Yaoting Zhou, Weisen Wang, Zhuangzhuang Tian +6 more·Dec 24, 2025
Quantum state readout with minimal resources is crucial for scalable quantum information processing. As a leading platform, neutral atom arrays rely on atomic fluorescence imaging for qubit readout, requiring short exposure, low photon count schemes ...
Regression of Functions by Quantum Neural Networks Circuits
Fernando M. de Paula Neto, Lucas dos Reis Silva, Paulo S. G. de Mattos Neto +1 more·Dec 23, 2025
The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely ...
QuSquare: Scalable Quality-Oriented Benchmark Suite for Pre-Fault-Tolerant Quantum Devices
David Aguirre, Rubén Peña, Mikel Sanz·Dec 22, 2025
As quantum technologies continue to advance, the proliferation of hardware architectures with diverse capabilities and limitations has underscored the importance of benchmarking as a tool to compare performance across platforms. Achieving fair, scala...
Image Denoising via Quantum Reservoir Computing
Soumyadip Das, Luke Antoncich, Jingbo B. Wang·Dec 21, 2025
Quantum Reservoir Computing (QRC) leverages the natural dynamics of quantum systems for information processing, without requiring a fault-tolerant quantum computer. In this work, we apply QRC within a hybrid quantum classical framework for image deno...
Exploring polymer classification with a hybrid single-photon quantum approach
Alexandrina Stoyanova, Bogdan Penkovsky·Dec 19, 2025
Polymers exhibit complex architectures and diverse properties that place them at the center of contemporary research in chemistry and materials science. As conventional computational techniques, even multi-scale ones, struggle to capture this complex...
Exploring the Effect of Basis Rotation on NQS Performance
Sven Benjamin Kožić, Vinko Zlatić, Fabio Franchini +1 more·Dec 19, 2025
Neural Quantum States (NQS) use neural networks to represent wavefunctions of quantum many-body systems, but their performance depends on the choice of basis, yet the underlying mechanism remains poorly understood. We use a fully solvable one-dimensi...
Optimizing Quantum Data Embeddings for Ligand-Based Virtual Screening
Junggu Choi, Tak Hur, Seokhoon Jeong +5 more·Dec 18, 2025
Effective molecular representations are essential for ligand-based virtual screening. We investigate how quantum data embedding strategies can improve this task by developing and evaluating a family of quantum-classical hybrid embedding approaches. T...
Q-RUN: Quantum-Inspired Data Re-uploading Networks
Wenbo Qiao, Shuaixian Wang, Peng Zhang +2 more·Dec 18, 2025
Data re-uploading quantum circuits (DRQC) are a key approach to implementing quantum neural networks and have been shown to outperform classical neural networks in fitting high-frequency functions. However, their practical application is limited by t...
Optical coprocessor based on spontaneous Brillouin scattering
I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov +1 more·Dec 17, 2025
Analog coprocessors for neural networks are an intensively developing field. They provide approximate results of computations for relatively low energy cost and at high speed. We show that a set of ring resonators with Brillouin interaction between p...
Low-Latency FPGA Control System for Real-Time Neural Network Processing in CCD-Based Trapped-Ion Qubit Measurement
Binglei Lou, Gautham Duddi Krishnaswaroop, Filip Wojcicki +5 more·Dec 17, 2025
Accurate and low-latency qubit state measurement is critical for trapped-ion quantum computing. While deep neural networks (DNNs) have been integrated to enhance detection fidelity, their latency performance on specific hardware platforms remains und...
Photonics-Enhanced Graph Convolutional Networks
Yuan Wang, Oleksandr Kyriienko·Dec 17, 2025
Photonics can offer a hardware-native route for machine learning (ML). However, efficient deployment of photonics-enhanced ML requires hybrid workflows that integrate optical processing with conventional CPU/GPU based neural network architectures. He...
Geometric Latent Space Tomography with Metric-Preserving Autoencoders
S. M. Yousuf Iqbal Tomal, Abdullah Al Shafin·Dec 16, 2025
Quantum state tomography faces exponential scaling with system size, while recent neural network approaches achieve polynomial scaling at the cost of losing the geometric structure of quantum state space. We introduce geometric latent space tomograph...
Fair sampling of ground-state configurations using hybrid quantum-classical MCMC algorithms
Yuichiro Nakano, Keisuke Fujii·Dec 16, 2025
We study the fair sampling properties of hybrid quantum-classical Markov chain Monte Carlo (MCMC) algorithms for combinatorial optimization problems with degenerate ground states. While quantum optimization heuristics such as quantum annealing and th...
Physics-Informed Neural Networks with Adaptive Constraints for Multi-Qubit Quantum Tomography
Changchun Feng, Laifa Tao, Lin Chen·Dec 16, 2025
Quantum state tomography (QST) faces exponential measurement requirements and noise sensitivity in multi-qubit systems, bottlenecking practical quantum technologies. We present a physics-informed neural network (PINN) framework integrating quantum me...
A Graph-Based Forensic Framework for Inferring Hardware Noise of Cloud Quantum Backend
Subrata Das, Archisman Ghosh, Swaroop Ghosh·Dec 16, 2025
Cloud quantum platforms give users access to many backends with different qubit technologies, coupling layouts, and noise levels. The execution of a circuit, however, depends on internal allocation and routing policies that are not observable to the ...
Quantum Machine Learning for Climate Modelling
Mierk Schwabe, Lorenzo Pastori, Valentina Sarandrea +1 more·Dec 16, 2025
Quantum machine learning (QML) is making rapid progress, and QML-based models hold the promise of quantum advantages such as potentially higher expressivity and generalizability than their classical counterparts. Here, we present work on using a quan...
Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization
Shaolun Ruan, Feng Liang, Rohan Ramakrishna +5 more·Dec 16, 2025
Quantum Neural Networks (QNNs) represent a promising fusion of quantum computing and neural network architectures, offering speed-ups and efficient processing of high-dimensional, entangled data. A crucial component of QNNs is the encoder, which maps...
A nonlinear quantum neural network framework for entanglement engineering
Adriano Macarone-Palmieri, Alberto Ferrara, Rosario Lo Franco·Dec 16, 2025
Multipartite entanglement is a crucial resource for quantum technologies; however, its scalable generation in noisy quantum devices remains a significant challenge. Here, we propose a low-depth quantum neural network architecture with linear scaling,...
Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Patrick Egenlauf, Iva Březinová, Sabine Andergassen +1 more·Dec 15, 2025
Out-of-equilibrium quantum many-body systems exhibit rapid correlation buildup that underlies many emerging phenomena. Exact wave-function methods to describe this scale exponentially with particle number; simpler mean-field approaches neglect essent...