Papers
Quantitative Universal Approximation for Noisy Quantum Neural Networks
Lukas Gonon, Antoine Jacquier, Marcel Mordarski·Apr 2, 2026
We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks. We focus on applications to Quantitative Finance, where target functions are often given as expectations. We further provide a...
Programmable recirculating bricks mesh architecture for quantum photonics
Jacek Gosciniak·Apr 1, 2026
General-purpose programmable photonic processors offer a flexible foundation for integrating various functionalities within a single chip. A two-dimensional hexagonal waveguide mesh of Mach Zehnder interferometers has been shown to have great potenti...
Hybrid Quantum-Classical AI for Industrial Defect Classification in Welding Images
Akshaya Srinivasan, Xiaoyin Cheng, Jianming Yi +4 more·Mar 30, 2026
Hybrid quantum-classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum-classical approaches for classifying defects in aluminium TIG weld...
Neural Quantum States in Non-Stabilizer Regimes: Benchmarks with Atomic Nuclei
James W. T. Keeble, Alessandro Lovato, Caroline E. P. Robin·Mar 30, 2026
As neural networks are known to efficiently represent classes of tensor-network states as well as volume-law-entangled states, identifying which properties determine the representational capabilities of neural quantum states (NQS) remains an open que...
A Resource-Aligned Hybrid Quantum-Classical Framework for Multimodal Face Anti-Spoofing
Wanqi Sun, Jungang Xu, Chenghua Duan·Mar 29, 2026
Embedding high-dimensional data into resource-limited quantum devices remains a significant challenge for practical quantum machine learning. In multimodal face anti-spoofing, while linear compression methods such as principal component analysis can ...
Benchmarking Encoding Families in Quantum Neural Networks Under Fixed Circuit Area for Frequency Spectrum and Trainability
Martyna Czuba, Patrick Holzer, Hein Zay Yar Oo·Mar 29, 2026
Quantum Neural Networks (QNNs) offer a promising framework for integrating quantum computing principles into machine learning, yet their practical capabilities and limitations remain insufficiently studied. In this work, we systematically investigate...
Non-Unitary Quantum Machine Learning: Fisher Efficiency Transitions from Distributed Quantum Expressivity
Apoorv Kumar Masta, Srinjoy Ganguly, Shalini Devendrababu +3 more·Mar 28, 2026
Quantum machine learning has faced growing scrutiny over its practical advantages compared to classical approaches, particularly following dequantization results and large scale benchmarking studies that have challenged earlier optimistic claims. Thi...
NNQA: Neural-Native Quantum Arithmetic for End-to-End Polynomial Synthesis
Ziqing Guo, Jie Li, Yong Chen +1 more·Mar 28, 2026
Hybrid classical quantum learning is often bottlenecked by communication overhead and approximation error from generic variational ansatzes. In this study, we introduce Neural Native Quantum Arithmetic (NNQA), which compiles classically learned nonli...
Pattern Formation in Quantum Hierarchical Cellular Neural Networks
W. A. Zúñiga-Galindo, B. A. Zambrano-Luna, Chayapuntika Indoung·Mar 28, 2026
We present a new class of quantum neural networks (QNNs) whose states are solutions of $p$-adic Schrödinger equations with a non-local potential that controls the interaction between the neurons. These equations are obtained as Wick rotations of the ...
Expressibility of neural quantum states: a Walsh-complexity perspective
Taige Wang·Mar 27, 2026
Neural quantum states are powerful variational wavefunctions, but it remains unclear which many-body states can be represented efficiently by modern additive architectures. We introduce Walsh complexity, a basis-dependent measure of how broadly a wav...
Neural Operator Quantum State: A Foundation Model for Quantum Dynamics
Zihao Qi, Christopher Earls, Yang Peng·Mar 26, 2026
Capturing the dynamics of quantum many-body systems under time-dependent driving protocols is a central challenge for numerical simulations. Existing methods such as tensor networks and time-dependent neural quantum states, however, must be re-run fo...
Characterization of Off-wafer Pulse Communication in BrainScaleS Neuromorphic System
Bernhard Vogginger, Vasilis Thanasoulis, Johannes Partzsch +1 more·Mar 25, 2026
Neuromorphic VLSI systems take inspiration from biology to enable efficient emulation of large-scale spiking neural networks and to explore new computational paradigms. To establish large neuromorphic systems, a sophisticated routing infrastructure i...
Auto-regressive Neural Quantum State Sampling for Selected Configuration Interaction
Shane Thompson, Daniel Gunlycke·Mar 25, 2026
Accurate ground-state energy calculations remain a central challenge in quantum chemistry due to the exponential scaling of the many-body Hilbert space. Variational Monte Carlo and variational quantum eigensolvers offer promising ansatz optimization ...
Decoder Dependence in Surface-Code Threshold Estimation with Native Gottesman-Kitaev-Preskill Digitization and Parallelized Sampling
Dennis Delali Kwesi Wayo, Chinonso Onah, Leonardo Goliatt +1 more·Mar 25, 2026
We quantify decoder dependence in surface-code threshold studies under two matched regimes: Pauli noise and native GKP-style Gaussian displacement digitization. Using LiDMaS+ v1.1.0, we benchmark MWPM, Union-Find (UF), Belief Propagation (BP), and ne...
A versatile neural-network toolbox for testing Bell locality in networks
Antoine Girardin, Mohammad Massi Rashidi, Géraldine Haack +2 more·Mar 25, 2026
Determining whether an observed distribution of events generated in a quantum network is Bell local, i.e., if it admits an alternative realization in terms of independent local variables, is extremely challenging. Building upon arXiv:1907.10552, we d...
Spectral methods: crucial for machine learning, natural for quantum computers?
Vasilis Belis, Joseph Bowles, Rishabh Gupta +2 more·Mar 25, 2026
This article presents an argument for why quantum computers could unlock new methods for machine learning. We argue that spectral methods, in particular those that learn, regularise, or otherwise manipulate the Fourier spectrum of a machine learning ...
Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
Jucai Zhai, Muhammad Abdullah, Boyang Chen +6 more·Mar 25, 2026
In scientific computing, the formulation of numerical discretisations of partial differential equations (PDEs) as untrained convolutional layers within Convolutional Neural Networks (CNNs), referred to by some as Neural Physics, has demonstrated good...
Quantum photonic neural networks in time
Ivanna M. Boras Vazquez, Jacob Ewaniuk, Nir Rotenberg·Mar 25, 2026
We introduce the architecture and timing algorithm to realize a time-bin-encoded quantum photonic neural network (QPNN): a reconfigurable nonlinear photonic circuit inspired by the brain and trained to process quantum information. Unlike the typical ...
Information-Theoretic Scaling Laws of Neural Quantum States
Yiming Lu, Sriram Bharadwaj, Dikshant Rathore +1 more·Mar 24, 2026
We establish an information-theoretic scaling law for generic autoregressive neural quantum states, determined by the middle-cut mutual information of the wavefunction amplitude. By formalizing the virtual bond as an effective information channel acr...
Probing the Spacetime Structure of Entanglement in Monitored Quantum Circuits with Graph Neural Networks
Javad Vahedi, Stefan Kettemann·Mar 23, 2026
Global entanglement in quantum many-body systems is inherently nonlocal, raising the question of whether it can be inferred from local observations. We investigate this problem in monitored quantum circuits, where projective measurements generate cla...