Papers
QuantumGS: Quantum Encoding Framework for Gaussian Splatting
Grzegorz Wilczyński, Rafał Tobiasz, Paweł Gora +2 more·Feb 4, 2026
Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled real-time rendering of complex scenes. However, standard 3DGS relies on spherical harmonics, which often struggle to accurately capture high-frequency view-d...
Optimal Control Design Guided by Adam Algorithm and LSTM-Predicted Open Quantum System Dynamics
JunDong Zhong, ZhaoMing Wang·Feb 4, 2026
The realization of high-fidelity quantum control is crucial for quantum information processing, particularly in noisy environments where control strategies must simultaneously achieve precise manipulation and effective noise suppression. Conventional...
Low resource entanglement classification from neural network interpretability
A. García-Velo, R. Puebla, Y. Ban +2 more·Feb 4, 2026
Entanglement is a central resource in quantum information and quantum technologies, yet its characterization remains challenging due to both theoretical complexity and measurement requirements. Machine learning has emerged as a promising alternative,...
Benchmarking Quantum and Classical Algorithms for the 1D Burgers Equation: QTN, HSE, and PINN
Vanshaj Kerni, Abdelrahman E. Ahmed, Syed Ali Asghar·Feb 4, 2026
We present a comparative benchmark of Quantum Tensor Networks (QTN), the Hydrodynamic Schrödinger Equation (HSE), and Physics-Informed Neural Networks (PINN) for simulating the 1D Burgers' equation. Evaluating these emerging paradigms against classic...
A computational account of dreaming: learning and memory consolidation
Qi Zhang·Feb 4, 2026
A number of studies have concluded that dreaming is mostly caused by randomly arriving internal signals because "dream contents are random impulses", and argued that dream sleep is unlikely to play an important part in our intellectual capacity. On t...
Stochastic Spiking Neuron Based SNN Can be Inherently Bayesian
Huannan Zheng, Jingli Liu, Kezhou Yang·Feb 3, 2026
Uncertainty in biological neural systems appears to be computationally beneficial rather than detrimental. However, in neuromorphic computing systems, device variability often limits performance, including accuracy and efficiency. In this work, we pr...
Validating a Koopman-Quantum Hybrid Paradigm for Diagnostic Denoising of Fusion Devices
Tie-Jun Wang, Run-Qing Zhang, Ling Qian +4 more·Feb 3, 2026
The potential of Quantum Machine Learning (QML) in data-intensive science is strictly bottlenecked the difficulty of interfacing high-dimensional, chaotic classical data into resource-limited, noisy quantum processors. To bridge this gap, we introduc...
Physics-inspired transformer quantum states via latent imaginary-time evolution
Kimihiro Yamazaki, Itsushi Sakata, Takuya Konishi +1 more·Feb 3, 2026
Neural quantum states (NQS) are powerful ansätze in the variational Monte Carlo framework, yet their architectures are often treated as black boxes. We propose a physically transparent framework in which NQS are treated as neural approximations to la...
Approaching the Thermodynamic Limit with Neural-Network Quantum States
Luciano Loris Viteritti, Riccardo Rende, Subir Sachdev +1 more·Feb 2, 2026
Accessing the thermodynamic-limit properties of strongly correlated quantum matter requires simulations on very large lattices, a regime that remains challenging for numerical methods, especially in frustrated two-dimensional systems. We introduce th...
First-Principles Optical Descriptors and Hybrid Classical-Quantum Classification of Er-Doped CaF$_2$
David Angel Alba Bonilla, Kerem Yurtseven, Krishan Sharma +4 more·Jan 31, 2026
We present a physics-informed classical-quantum machine learning framework for discriminating pristine CaF$_2$ from Er-doped CaF$_2$ using first-principles optical descriptors. Finite Ca$_8$F$_{16}$ and Ca$_7$ErF$_{16}$ clusters were constructed from...
Addressing the ground state of the deuteron by physics-informed neural networks
Lorenzo Brevi, Antonio Mandarino, Carlo Barbieri +1 more·Jan 30, 2026
Machine learning techniques have proven to be effective in addressing the structure of atomic nuclei. Physics$-$Informed Neural Networks (PINNs) are a promising machine learning technique suitable for solving integro-differential problems such as the...
Towards Sample Efficient Entanglement Classification for 3 and 4 Qubit Systems: A Tailored CNN-BiLSTM Approach
Qian Sun, Yuedong Sun, Yu Hu +3 more·Jan 30, 2026
Accurate classification of multipartite entanglement in high-dimensional quantum systems is crucial for advancing quantum communication and information processing. However, conventional methods are resource-intensive, and even many machine-learning-b...
Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +3 more·Jan 29, 2026
Hybrid quantum-classical learning models increasingly integrate neural networks with variational quantum circuits (VQCs) to exploit complementary inductive biases. However, many existing approaches rely on tightly coupled architectures or task-specif...
A Deterministic Framework for Neural Network Quantum States in Quantum Chemistry
Zheng Che·Jan 29, 2026
Stochastic optimization of Neural Network Quantum States (NQS) in discrete Fock spaces is limited by sampling variance and slow mixing. We present a deterministic framework that optimizes a neural backflow ansatz within dynamically adaptive configura...
Parametric Quantum State Tomography with HyperRBMs
Simon Tonner, Viet T. Tran, Richard Kueng·Jan 28, 2026
Quantum state tomography (QST) is essential for validating quantum devices but suffers from exponential scaling in system size. Neural-network quantum states, such as Restricted Boltzmann Machines (RBMs), can efficiently parameterize individual many-...
Neural Quantum States in Mixed Precision
Massimo Solinas, Agnes Valenti, Nawaf Bou-Rabee +1 more·Jan 28, 2026
Scientific computing has long relied on double precision (64-bit floating point) arithmetic to guarantee accuracy in simulations of real-world phenomena. However, the growing availability of hardware accelerators such as Graphics Processing Units (GP...
Quantum Light Detection with Enhanced Photonic Neural Network
Stanisław Świerczewski, Dogyun Ko, Amir Rahmani +7 more·Jan 27, 2026
Advances in quantum technologies are accelerating the demand for optical quantum state sensors that combine high precision, versatility, and scalability within a unified hardware platform. Quantum reservoir computing offers a powerful route toward th...
Data-Driven Qubit Characterization and Optimal Control using Deep Learning
Paul Surrey, Julian D. Teske, Tobias Hangleiter +2 more·Jan 26, 2026
Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a r...
Bayesian Optimization for Quantum Error-Correcting Code Discovery
Yihua Chengyu, Richard Meister, Conor Carty +2 more·Jan 26, 2026
Quantum error-correcting codes protect fragile quantum information by encoding it redundantly, but identifying codes that perform well in practice with minimal overhead remains difficult due to the combinatorial search space and the high cost of logi...
Quantum Recurrent Unit: A Parameter-Efficient Quantum Neural Network Architecture for NISQ Devices
Tzong-Daw Wu, Hsi-Sheng Goan·Jan 26, 2026
The rapid growth of modern machine learning (ML) models presents fundamental challenges in parameter efficiency and computational resource requirements. This study introduces the Quantum Recurrent Unit (QRU), a novel quantum neural network (NN) archi...