Papers
Low Latency GNN Accelerator for Quantum Error Correction
Alessio Cicero, Luigi Altamura, Moritz Lange +2 more·Mar 23, 2026
Quantum computers have the potential to solve certain complex problems in a much more efficient way than classical computers. Nevertheless, current quantum computer implementations are limited by high physical error rates. This issue is addressed by ...
Model selection in hybrid quantum neural networks with applications to quantum transformer architectures
Harsh Wadhwa, Rahul Bhowmick, Naipunnya Raj +3 more·Mar 23, 2026
Quantum machine learning models generally lack principled design guidelines, often requiring full resource-intensive training across numerous choices of encodings, quantum circuit designs and initialization strategies to find effective configuration....
Neural Belief-Matching Decoding for Topological Quantum Error Correction Codes
Luca Menti, Francisco Lázaro·Mar 23, 2026
Quantum error correction (QEC) is critical for scalable fault-tolerant quantum computing. Topological codes, such as the toric code, offer hardware-efficient architectures but their Tanner graphs contain many girth-4 cycles that degrade the performan...
Neural network approach to mitigating intra-gate crosstalk in superconducting CZ gates
Yiming Yu, Yexiong Zeng, Ye-Hong Chen +2 more·Mar 23, 2026
The potential of quantum computing is fundamentally constrained by the inherent susceptibility of qubits to noise and crosstalk, particularly during multi-qubit gate operations. Existing strategies, such as hardware isolation and dynamical decoupling...
Distilling the knowledge with quantum neural networks
Yuxuan Yan, Sitian Qian, Qi Zhao +1 more·Mar 23, 2026
Quantum Neural Networks (QNNs) are a promising class of quantum machine learning models with potential quantum advantages when implemented on scalable, error-corrected quantum computers. However, as system sizes increase, deploying QNNs becomes chall...
Quantum inference on a classically trained quantum extreme learning machine
Emanuele Brusaschi, Marco Clementi, Marco Liscidini +3 more·Mar 20, 2026
Quantum extreme learning machines (QELMs) are unconventional computing architectures that bear remarkable promise in both classical and quantum machine-learning tasks, such as the estimate of quantum state properties. However, the probabilistic natur...
Measurement-Induced Quantum Neural Network
Paul Argyle, Djamil Lakhdar-Hamina, Sarah H. Miller +1 more·Mar 19, 2026
We introduce a measurement-induced quantum neural network (MINN), an adaptive monitored-circuit architecture in which mid-circuit measurement outcomes determine the entangling gates in subsequent layers. In contrast to standard monitored circuits whe...
End-to-End QGAN-Based Image Synthesis via Neural Noise Encoding and Intensity Calibration
Xue Yang, Rigui Zhou, Shizheng Jia +5 more·Mar 19, 2026
Quantum Generative Adversarial Networks (QGANs) offer a promising path for learning data distributions on near-term quantum devices. However, existing QGANs for image synthesis avoid direct full-image generation, relying on classical post-processing ...
Barren Plateaus Beyond Observable Concentration
Zi-Shen Li, Bujiao Wu, Xiao-Wei Li +1 more·Mar 19, 2026
Parameterized quantum circuits (PQCs) are central to quantum machine learning and near-term quantum simulation, but their scalability is often hindered by barren plateaus (BPs), where gradients decay exponentially with system size. Prior explanations...
Learning Entanglement Quasiprobability from Noisy and Incomplete Data
Yu-Zhuo Li, Li-Chao Peng, Ke-Mi Xu·Mar 19, 2026
Negativities in quasiprobability distributions, a foundational concept originating in quantum optics, serve as a fundamental signature of quantum nonclassicality, with entanglement quasiprobabilities offering a necessary and sufficient criterion for ...
Removing nodal and support-mismatch pathologies in Variational Monte Carlo via blurred sampling
Zhou-Quan Wan, Roeland Wiersema, Shiwei Zhang·Mar 18, 2026
Variational Monte Carlo (VMC) is a powerful and fast-growing method for optimizing and evolving parameterized many-body wave functions, especially with modern neural-network quantum states. In practice, however, the stochastic estimators that form th...
A Continuous-Variable Quantum Fourier Layer: Applications to Filtering and PDE Solving
Paolo Marcandelli, Stefano Mariani, Martina Siena +1 more·Mar 18, 2026
Fourier representations play a central role in operator learning methods for partial differential equations and are increasingly being explored in quantum machine learning architectures. The classical fast Fourier transform (FFT), particularly in its...
Quantum reservoir computing with classical and nonclassical states in an integrated optical circuit
S. Świerczewski, W. Verstraelen, P. Deuar +3 more·Mar 17, 2026
Quantum reservoir computing (QRC) is a hardware-implementation-friendly quantum neural network scheme with minimal physical system requirements and a proven advantage over classical counterparts. We use an extension of the positive-P phase space meth...
Taming the expressiveness of neural-network wave functions for robust convergence to quantum many-body states
Dezhe Z. Jin·Mar 16, 2026
Neural networks are emerging as a powerful tool for determining the quantum states of interacting many-body fermionic systems. The standard approach trains a neural-network ansatz by minimizing the mean local energy estimated from Monte Carlo samples...
Physics-informed neural networks for solving saddle-point equations in strong-field physics with tailored fields
Jiakang Chen, Sufia Hashim, Carla Figueira de Morisson Faria·Mar 16, 2026
We develop an unsupervised physics-informed neural network to solve saddle-point equations (SPEs) governing direct above-threshold ionization (ATI) within the strong-field approximation. This setting provides a well-understood testbed in which the sa...
Quantum-Inspired Unitary Pooling for Multispectral Satellite Image Classification
Georgios Maragkopoulos, Aikaterini Mandilara, Ralntion Komini +1 more·Mar 16, 2026
Multispectral satellite imagery poses significant challenges for deep learning models due to the high dimensionality of spectral data and the presence of structured correlations across channels. Recent work in quantum machine learning suggests that u...
Neural network backflow for ab-initio solid calculations
An-Jun Liu, Bryan K. Clark·Mar 16, 2026
Accurately simulating extended periodic systems is a central challenge in condensed matter physics. Neural quantum states (NQS) offer expressive wavefunctions for this task but face issues with scalability. In this work, we successfully extend the ne...
A Deep-Learning-Boosted Framework for Quantum Sensing with Nitrogen-Vacancy Centers in Diamond
Changyu Yao, Haochen Shen, Zhongyuan Liu +13 more·Mar 16, 2026
Nitrogen-vacancy (NV) centers in diamond are a versatile quantum sensing platform for high sensitivity measurements of magnetic fields, temperature and strain with nanoscale spatial resolution. A common bottleneck is the analysis of optically detecte...
Learning Quantum Operator Dynamics from Short-Time Data
Jinyang Li, Satoshi Iso, Shunji Matsuura +2 more·Mar 16, 2026
Real-time dynamics of quantum observables provide direct access to excitation spectra and correlation functions in quantum many-body systems, but currently available quantum devices are limited to short evolution times due to decoherence. We propose ...
Variance reduction for forces and pressure in variational Monte Carlo
David Linteau, Saverio Moroni, Giuseppe Carleo +1 more·Mar 15, 2026
We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in electronic systems, although we show that the underlying ideas apply mo...