Papers
Excited Pfaffians: Generalized Neural Wave Functions Across Structure and State
Nicholas Gao, Till Grutschus, Frank Noé +1 more·Mar 15, 2026
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient numerical accuracy in state overlaps requires increasing the number of...
How to find expressible and trainable parameterized quantum circuits?
Peter Röseler, Dennis Willsch, Kristel Michielsen·Mar 15, 2026
Whether parameterized quantum circuits (PQCs) can be systematically constructed to be both trainable and expressive remains an open question. Highly expressive PQCs often exhibit barren plateaus, while several trainable alternatives admit efficient c...
Disentangling Tensor Network States with Deep Neural Network
Chaohui Fan, Bo Zhan, Yuntian Gu +5 more·Mar 15, 2026
We introduce Neural Tensor Network States ($ν$TNS), a variational many-body wave-function ansatz that integrates deep neural networks with tensor-network architectures. In the $ν$TNS framework, a neural network serves as a disentangler of the wave-fu...
Fast Single Nitrogen-Vacancy Center Ramsey Characterization using a Physics-Informed Neural Network
Chao Shang, Gregory D. Fuchs·Mar 14, 2026
Precise characterization of the local spin environment of single diamond nitrogen-vacancy (NV) centers is crucial for advancing quantum sensing, quantum networking, and the optimization of quantum materials. However, single NV center fluorescence mea...
Distance learning from projective measurements as an information-geometric probe of many-body physics
Oleksii Malyshev, Simon M. Linsel, Fabian Grusdt +3 more·Mar 13, 2026
The ability of modern quantum simulators--both digital and analogue--to generate large ensembles of single-shot projective "snapshots" has opened a data-rich avenue for the study of quantum many-body systems. Unsupervised machine learning analysis of...
Deep-Learning-Designed AlGaAs Interface Linking Trapped Ions to Telecom Quantum Networks
I. P. De Simeone, G. Maltese, V. Cambier +5 more·Mar 13, 2026
The realization of a scalable quantum internet requires efficient light-matter interfaces that map stationary qubits onto photonic carriers for long-distance transmission. A central challenge is the generation of entangled photons simultaneously comp...
V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction
Yingdi Jin, Xinming Qin, Ruichen Liu +3 more·Mar 13, 2026
Density functional theory (DFT) is a cornerstone of computational chemistry and materials science, but its computational cost limits its use in large-scale and high-throughput applications. While machine learning has accelerated energy prediction for...
Quantum Reservoir Autoencoder for Blind Decryption: Two-Phase Protocol and Noise Resilience
Hikaru Wakaura, Taiki Tanimae·Mar 12, 2026
We instantiate the quantum reservoir autoencoder (QRA) with a noise-induced reservoir employing reset noise channels and address two open problems: noise-resilient reversibility and blind decryption. For a single-ciphertext protocol with 10 data qubi...
DysonNet: Constant-Time Local Updates for Neural Quantum States
Lucas Winter, Andreas Nunnenkamp·Mar 11, 2026
Neural quantum states (NQS) provide a flexible variational framework for many-body wavefunctions, but suffer from high computational cost and limited interpretability. We introduce DysonNet, a broad class of NQS that couples strictly local nonlineari...
Variational Adaptive Gaussian Decomposition: Scalable Quadrature-Free Time-Sliced Thawed Gaussian Dynamics
Rahul Sharma, Amartya Bose·Mar 11, 2026
Time-slicing has emerged as a strategy for incorporating semiclassical propagation into real-time path integral formulation and recovering full quantum dynamics. A central step is the decomposition of a time-evolved wave function into a superposition...
Machine learning the arrow of time in solid-state spins
Xiang-Qian Meng, Zhide Lu, Ya-Nan Lu +9 more·Mar 11, 2026
Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversi...
A Hybrid Quantum-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Yixiong Chen·Mar 10, 2026
Accurate forecasting of financial market volatility is crucial for risk management, option pricing, and portfolio optimization. Traditional econometric models and classical machine learning methods face challenges in handling the inherent non-linear ...
Noise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning
A. M. A. S. D. Alagiyawanna, Asoka Karunananda·Mar 10, 2026
Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with machine learning techniques. The features of photonic tech...
Mitigating Frequency Learning Bias in Quantum Models via Multi-Stage Residual Learning
Ammar Daskin·Mar 10, 2026
Quantum machine learning models based on parameterized circuits can be viewed as Fourier series approximators. However, they often struggle to learn functions with multiple frequency components, particularly high-frequency or non-dominant ones; a phe...
Asymptotic Expansions for Neural Network Approximations of Quantum Channels
Rômulo Damasclin Chaves dos Santos·Mar 9, 2026
This paper establishes the Quantum Voronovskaya--Damasclin (QVD) Theorem, providing a complete asymptotic characterization of Quantum Neural Network Operators in the approximation of arbitrary quantum channels. The result extends the classical Vorono...
Characterization and upgrade of a quantum graph neural network for charged particle tracking
Matteo Argenton, Laura Cappelli, Concezio Bozzi·Mar 9, 2026
In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in reconstructing char...
Simulating non-Markovian open quantum dynamics by exploiting physics-informed neural network
Long Cao, Liwei Ge, Daochi Zhang +4 more·Mar 9, 2026
This work integrates the physics-informed neural network (PINN) approach into the neural quantum state framework to simulate open quantum system dynamics, to circumvent the computationally expensive time-dependent variational principle required in co...
Lindbladian Learning with Neural Differential Equations
Timothy Heightman, Roman Aseguinolaza Gallo, Edward Jiang +3 more·Mar 8, 2026
Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the system is open, this task becomes considerably harder than in the purely...
A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
Rodrigo Chaves, Kunal Kumar, Bruno Chagas +4 more·Mar 6, 2026
This paper investigates whether hybrid quantum-classical machine learning can deliver practical improvements in financial fraud detection performance for card-based and other payment transactions. Building on a Guided Quantum Compressor architecture,...
Implementation of Quantum Implicit Neural Representation in Deterministic and Probabilistic Autoencoders for Image Reconstruction/Generation Tasks
Saadet Müzehher Eren·Mar 6, 2026
We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and generation tasks. Our purpose is to demonstrate that the QINR in VAEs and AEs can transform information f...