Papers
Matter-induced plaquette terms in a $\mathbb{Z}_2$ lattice gauge theory
Matjaž Kebrič, Fabian Döschl, Umberto Borla +4 more·Feb 13, 2026
Lattice gauge theories (LGTs) provide a powerful framework for studying confinement, topological order, and exotic quantum matter. In particular, the paradigmatic phenomenon of confinement, where dynamical matter is coupled to gauge fields and forms ...
Reverse Delegated Training and Private Inference via Perfectly-Secure Quantum Homomorphic Encryption
Sergio A. Ortega, Miguel A. Martin-Delgado·Feb 13, 2026
Quantum machine learning in cloud environments requires protecting sensitive data while enabling remote computation. Here we demonstrate the first realistic implementations of a perfectly-secure quantum homomorphic encryption (QHE) scheme applied to ...
QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesis
Subhangi Kumari, Rakesh Achutha, Vignesh Sivaraman·Feb 13, 2026
Synthesizing realistic tabular data is challenging due to heterogeneous feature types and high dimensionality. We introduce QTabGAN, a hybrid quantum-classical generative adversarial framework for tabular data synthesis. QTabGAN is especially designe...
WSBD: Freezing-Based Optimizer for Quantum Neural Networks
Christopher Kverne, Mayur Akewar, Yuqian Huo +2 more·Feb 11, 2026
The training of Quantum Neural Networks (QNNs) is hindered by the high computational cost of gradient estimation and the barren plateau problem, where optimization landscapes become intractably flat. To address these challenges, we introduce Weighted...
Unlearnable phases of matter
Tarun Advaith Kumar, Yijian Zou, Amir-Reza Negari +2 more·Feb 11, 2026
We identify fundamental limitations in machine learning by demonstrating that non-trivial mixed-state phases of matter are computationally hard to learn. Focusing on unsupervised learning of distributions, we show that autoregressive neural networks ...
Nonreciprocal many-body physics
Michel Fruchart, Vincenzo Vitelli·Feb 11, 2026
Reciprocity is a fundamental symmetry present in many natural phenomena and engineered systems. Distinct situations where this symmetry is broken are typically grouped under the umbrella term "nonreciprocity", colloquially defined by: the action of A...
Error-mitigated quantum state tomography using neural networks
Yixuan Hu, Mengru Ma, Jiangwei Shang·Feb 10, 2026
The reliable characterization of quantum states is a fundamental task in quantum information science. For this purpose, quantum state tomography provides a standard framework for reconstructing quantum states from measurement data, yet it is often de...
SAQNN: Spectral Adaptive Quantum Neural Network as a Universal Approximator
Jialiang Tang, Jialin Zhang, Xiaoming Sun·Feb 10, 2026
Quantum machine learning (QML), as an interdisciplinary field bridging quantum computing and machine learning, has garnered significant attention in recent years. Currently, the field as a whole faces challenges due to incomplete theoretical foundati...
A Trainable-Embedding Quantum Physics-Informed Framework for Multi-Species Reaction-Diffusion Systems
Ban Q. Tran, Nahid Binandeh Dehaghani, A. Pedro Aguiar +2 more·Feb 10, 2026
Physics-informed neural networks (PINNs) and hybrid quantum-classical extensions provide a promising framework for solving partial differential equations (PDEs) by embedding physical laws directly into the learning process. In this work, we study emb...
Weak forms offer strong regularisations: how to make physics-informed (quantum) machine learning more robust
Annie E. Paine, Smit Chaudhary, Antonio A. Gentile·Feb 9, 2026
Physics-informed (PI) methodologies have surged to become a pillar route to solve Differential Equations (DEs), sustained by the growth of machine learning methods in scientific contexts. The main proposition of PI is to minimise variationally a loss...
Empirical Study of Observable Sets in Multiclass Quantum Classification
Paul San Sebastian, Mikel Cañizo, Roman Orus·Feb 9, 2026
Variational quantum algorithms have gained attention as early applications of quantum computers for learning tasks. In the context of supervised learning, most of the works that tackle classification problems with parameterized quantum circuits const...
Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling
Bulent Soykan, Sean Mondesire, Ghaith Rabadi +1 more·Feb 8, 2026
The Unrelated Parallel Machine Scheduling Problem (UPMSP) with release dates, setups, and eligibility constraints presents a significant multi-objective challenge. Traditional methods struggle to balance minimizing Total Weighted Tardiness (TWT) and ...
HoloGraph: All-Optical Graph Learning via Light Diffraction
Yingjie Li, Shanglin Zhou, Caiwen Ding +1 more·Feb 7, 2026
As a representative of next-generation device/circuit technology beyond CMOS, physics-based neural networks such as Diffractive Optical Neural Networks (DONNs) have demonstrated promising advantages in computational speed and energy efficiency. Howev...
All-Optical Segmentation via Diffractive Neural Networks for Autonomous Driving
Yingjie Li, Daniel Robinson, Weilu Gao +1 more·Feb 7, 2026
Semantic segmentation and lane detection are crucial tasks in autonomous driving systems. Conventional approaches predominantly rely on deep neural networks (DNNs), which incur high energy costs due to extensive analog-to-digital conversions and larg...
Physical Analog Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units
Manuel Escudero, Mohamadreza Zolfagharinejad, Sjoerd van den Belt +2 more·Feb 7, 2026
Kolmogorov-Arnold Networks (KANs) shift neural computation from linear layers to learnable nonlinear edge functions, but implementing these nonlinearities efficiently in hardware remains an open challenge. Here we introduce a physical analog KAN arch...
BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks
Simon Bührer, Andreas Plesner, Aczel Till +1 more·Feb 7, 2026
The energy and latency costs of deep neural network inference are increasingly driven by deployment rather than training, motivating hardware-specialized alternatives to arithmetic-heavy models. Field-Programmable Gate Arrays (FPGAs) provide an attra...
Consensus Protocols for Entanglement-Aware Scheduling in Distributed Quantum Neural Networks
Kuan-Cheng Chen, Samuel Yen-Chi Chen, Mahdi Chehimi +2 more·Feb 6, 2026
The realization of distributed quantum neural networks (DQNNs) over quantum internet infrastructures faces fundamental challenges arising from the fragile nature of entanglement and the demanding synchronization requirements of distributed learning. ...
HyQuRP: Hybrid quantum-classical neural network with rotational and permutational equivariance for 3D point clouds
Semin Park, Chae-Yeun Park·Feb 6, 2026
We introduce HyQuRP, a hybrid quantum-classical neural network equivariant to rotational and permutational symmetries. While existing equivariant quantum machine learning models often rely on ad hoc constructions, HyQuRP is built upon the formal foun...
Quenching Speculation in Quantum Markets via Entangled Neural Traders
Kieran Hymas, Hiu Ming Lau, Kareem Raslan +5 more·Feb 6, 2026
Speculative trading can drive pronounced market instabilities, yet existing regulatory and macroprudential tools intervene only after such dynamics emerge. Quantum technologies offer a fundamentally new means of shaping economic behavior by introduci...
Private and interpretable clinical prediction with quantum-inspired tensor train models
José Ramón Pareja Monturiol, Juliette Sinnott, Roger G. Melko +1 more·Feb 5, 2026
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerabl...