Papers
Essentially No Energy Barrier Between Independent Fermionic Neural Quantum State Minima
David D. Dai, Marin Soljačić·Jan 11, 2026
Neural quantum states (NQS) have proven highly effective in representing quantum many-body wavefunctions, but their loss landscape remains poorly understood and debated. Here, we demonstrate that the NQS loss landscape is more benign and similar to c...
Artificial Entanglement in the Fine-Tuning of Large Language Models
Min Chen, Zihan Wang, Canyu Chen +3 more·Jan 11, 2026
Large language models (LLMs) can be adapted to new tasks using parameter-efficient fine-tuning (PEFT) methods that modify only a small number of trainable parameters, often through low-rank updates. In this work, we adopt a quantum-information-inspir...
Noise-Resistant Feature-Aware Attack Detection Using Quantum Machine Learning
Chao Ding, Shi Wang, Jingtao Sun +3 more·Jan 11, 2026
Continuous-variable quantum key distribution (CV-QKD) is a quantum communication technology that offers an unconditional security guarantee. However, the practical deployment of CV-QKD systems remains vulnerable to various quantum attacks. In this pa...
Feature Entanglement-based Quantum Multimodal Fusion Neural Network
Yu Wu, Qianli Zhou, Jie Geng +2 more·Jan 9, 2026
Multimodal learning aims to enhance perceptual and decision-making capabilities by integrating information from diverse sources. However, classical deep learning approaches face a critical trade-off between the high accuracy of black-box feature-leve...
Driver-Intention Prediction with Deep Learning: Real-Time Brain-to-Vehicle Communication
Niloufar Alavi, Swati Shah, Rezvan Alamian +1 more·Jan 8, 2026
Brain-computer interfaces (BCIs) allow direct communication between the brain and electronics without the need for speech or physical movement. Such interfaces can be particularly beneficial in applications requiring rapid response times, such as dri...
Assessing the Impact of Low Resolution Control Electronics on Quantum Neural Network Performance
Rupayan Bhattacharjee, Rohit Sarma Sarkar, Sergi Abadal +2 more·Jan 8, 2026
Scaling quantum computers requires tight integration of cryogenic control electronics with quantum processors, where Digital-to-Analog Converters (DACs) face severe power and area constraints. We investigate quantum neural network (QNN) training and ...
The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
Dominik Freinberger, Philipp Moser·Jan 8, 2026
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures ha...
Solving nonlinear PDEs with Quantum Neural Networks: A variational approach to the Bratu Equation
Nikolaos Cheimarios·Jan 7, 2026
We present a variational quantum algorithm (VQA) to solve the nonlinear one-dimensional Bratu equation. By formulating the boundary value problem within a variational framework and encoding the solution in a parameterized quantum neural network (QNN)...
Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction
Rehan Ahmad, Muhammad Kashif, Nouhaila Innan +1 more·Jan 7, 2026
In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii...
Enhancing Small Dataset Classification Using Projected Quantum Kernels with Convolutional Neural Networks
A. M. A. S. D. Alagiyawanna, Asoka Karunananda, A. Mahasinghe +1 more·Jan 6, 2026
Convolutional Neural Networks (CNNs) have shown promising results in efficiency and accuracy in image classification. However, their efficacy often relies on large, labeled datasets, posing challenges for applications with limited data availability. ...
A Unified Frequency Principle for Quantum and Classical Machine Learning
Rundi Lu, Ruiqi Zhang, Weikang Li +3 more·Jan 6, 2026
Quantum neural networks constitute a key class of near-term quantum learning models, yet their training dynamics remain not fully understood. Here, we present a unified theoretical framework for the frequency principle (F-principle) that characterize...
Quantum-Enhanced Neural Contextual Bandit Algorithms
Yuqi Huang, Vincent Y. F Tan, Sharu Theresa Jose·Jan 6, 2026
Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling to quantum neural networks (QNNs) due to issues such as massive over-...
Quantum-enhanced long short-term memory with attention for spatial permeability prediction in oilfield reservoirs
Muzhen Zhang, Yujie Cheng, Zhanxiang Lei·Jan 6, 2026
Spatial prediction of reservoir parameters, especially permeability, is crucial for oil and gas exploration and development. However, the wide range and high variability of permeability prevent existing methods from providing reliable predictions. Fo...
Deep learning parameter estimation and quantum control of single molecule
Juan M. Scarpetta, Omar Calderón-Losada, Morten Hjorth-Jensen +1 more·Jan 5, 2026
Coherent control, a central concept in physics and chemistry, has sparked significant interest due to its ability to fine-tune interference effects in atoms and individual molecules for applications ranging from light-harvesting complexes to molecula...
Implicitly Restarted Lanczos Enables Chemically-Accurate Shallow Neural Quantum States
Wei Liu, Wenjie Dou·Jan 4, 2026
The variational optimization of high-dimensional neural network models, such as those used in neural quantum states (NQS), presents a significant challenge in machine intelligence. Conventional first-order stochastic methods (e.g., Adam) are plagued ...
Neural Minimum Weight Perfect Matching for Quantum Error Codes
Yotam Peled, David Zenati, Eliya Nachmani·Jan 1, 2026
Realizing the full potential of quantum computation requires Quantum Error Correction (QEC). QEC reduces error rates by encoding logical information across redundant physical qubits, enabling errors to be detected and corrected. A common decoder used...
Probabilistic Computers for Neural Quantum States
Shuvro Chowdhury, Jasper Pieterse, Navid Anjum Aadit +2 more·Dec 31, 2025
Neural quantum states efficiently represent many-body wavefunctions with neural networks, but the cost of Monte Carlo sampling limits their scaling to large system sizes. Here we address this challenge by combining sparse Boltzmann machine architectu...
Machine Learning-Aided Optimal Control of a Qubit Subjected to External Noise
Riccardo Cantone, Shreyasi Mukherjee, Luigi Giannelli +2 more·Dec 30, 2025
We apply a machine-learning-enhanced greybox framework to a quantum optimal control protocol for open quantum systems. Combining a whitebox physical model with a neural-network blackbox trained on synthetic data, the method captures non-Markovian noi...
One-Shot Structured Pruning of Quantum Neural Networks via $q$-Group Engineering and Quantum Geometric Metrics
Haijian Shao, Wei Liu, Xing Deng +1 more·Dec 30, 2025
Quantum neural networks (QNNs) suffer from severe gate-level redundancy, which hinders their deployment on noisy intermediate-scale quantum (NISQ) devices. In this work, we propose q-iPrune, a one-shot structured pruning framework grounded in the alg...
Quantum Error Mitigation with Attention Graph Transformers for Burgers Equation Solvers on NISQ Hardware
Seyed Mohamad Ali Tousi, Adib Bazgir, Yuwen Zhang +1 more·Dec 29, 2025
We present a hybrid quantum-classical framework augmented with learned error mitigation for solving the viscous Burgers equation on noisy intermediate-scale quantum (NISQ) hardware. Using the Cole-Hopf transformation, the nonlinear Burgers equation i...