Papers
Dual-Qubit Hierarchical Fuzzy Neural Network for Image Classification: Enabling Relational Learning via Quantum Entanglement
Wenwei Zhang, Jintao Wang, Tianyu Ye +1 more·Dec 15, 2025
Classical deep neural network models struggle to represent data uncertainty and capture dependencies between features simultaneously, especially under fuzzy or noisy conditions. Although a quantum-assisted hierarchical fuzzy neural network (QA-HFNN) ...
Investigation of a Bit-Sequence Reconciliation Protocol Based on Neural TPM Networks in Secure Quantum Communications
Matvey Yorkhov, Vladimir Faerman, Anton Konev·Dec 15, 2025
The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to stud...
A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional
Martin J. Uttendorfer, Daniel Barragan-Yani, Matthias Sperl +1 more·Dec 15, 2025
We introduce a novel approach that exploits the intersection of quantum computing, machine learning and reduced density matrix functional theory to leverage the potential of quantum computing to improve simulations of interacting quantum particles. O...
Neural quantum states for entanglement depth certification from randomized Pauli measurements
Marcin Płodzień·Dec 15, 2025
Entanglement depth quantifies how many qubits share genuine multipartite entanglement, but certification typically relies on tailored witnesses or full tomography, both of which scale poorly with system size. We recast entanglement-depth and non-$k$-...
A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction
Xiuying Zhang, Qinsheng Zhu, Xiaodong Xing·Dec 15, 2025
We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consist...
Practical Hybrid Quantum Language Models with Observable Readout on Real Hardware
Stefan Balauca, Ada-Astrid Balauca, Adrian Iftene·Dec 14, 2025
Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quan...
Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis
Yeray Cordero, Paula García-Molina, Fernando Vilariño·Dec 14, 2025
Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction, yet classical networks suffer from a well-known spectral bias that limits their ability to capture high-frequency detai...
A Comparative Study of Encoding Strategies for Quantum Convolutional Neural Networks
Xingyun Feng·Dec 14, 2025
Quantum convolutional neural networks (QCNNs) offer a promising architecture for near-term quantum machine learning by combining hierarchical feature extraction with modest parameter growth. However, any QCNN operating on classical data must rely on ...
Learning Dynamics in Memristor-Based Equilibrium Propagation
Michael Döll, Andreas Müller, Bernd Ulmann·Dec 13, 2025
Memristor-based in-memory computing has emerged as a promising paradigm to overcome the constraints of the von Neumann bottleneck and the memory wall by enabling fully parallelisable and energy-efficient vector-matrix multiplications. We investigate ...
Learning Minimal Representations of Fermionic Ground States
Felix Frohnert, Emiel Koridon, Stefano Polla·Dec 12, 2025
We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network architecture on data from $L$-site Fermi-Hubbard models, we identify m...
Basis dependence of Neural Quantum States for the Transverse Field Ising Model
Ronald Santiago Cortes, Aravindh S. Shankar, Marcello Dalmonte +2 more·Dec 12, 2025
Neural Quantum States (NQS) are powerful tools used to represent complex quantum many-body states in an increasingly wide range of applications. However, despite their popularity, at present only a rudimentary understanding of their limitations exist...
FRQI Pairs method for image classification using Quantum Recurrent Neural Network
Rafał Potempa, Michał Kordasz, Sundas Naqeeb Khan +4 more·Dec 12, 2025
This study aims to introduce the FRQI Pairs method to a wider audience, a novel approach to image classification using Quantum Recurrent Neural Networks (QRNN) with Flexible Representation for Quantum Images (FRQI). The study highlights an innovative...
Electronic crystals and quasicrystals in semiconductor quantum wells: an AI-powered discovery
Filippo Gaggioli, Pierre-Antoine Graham, Liang Fu·Dec 11, 2025
The homogeneous electron gas is a cornerstone of quantum condensed matter physics, providing the foundation for developing density functional theory and understanding electronic phases in semiconductors. However, theoretical understanding of strongly...
Generative Adversarial Variational Quantum Kolmogorov-Arnold Network
Hikaru Wakaura·Dec 11, 2025
Kolmogorov Arnold Networks is a novel multilayer neuromorphic network that can exhibit higher accuracy than a neural network. It can learn and predict more accurately than neural networks with a smaller number of parameters, and many research groups ...
Graph-Based Bayesian Optimization for Quantum Circuit Architecture Search with Uncertainty Calibrated Surrogates
Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique +1 more·Dec 10, 2025
Quantum circuit design is a key bottleneck for practical quantum machine learning on complex, real-world data. We present an automated framework that discovers and refines variational quantum circuits (VQCs) using graph-based Bayesian optimization wi...
LiePrune: Lie Group and Quantum Geometric Dual Representation for One-Shot Structured Pruning of Quantum Neural Networks
Haijian Shao, Bowen Yang, Wei Liu +2 more·Dec 10, 2025
Quantum neural networks (QNNs) and parameterized quantum circuits (PQCs) are key building blocks for near-term quantum machine learning. However, their scalability is constrained by excessive parameters, barren plateaus, and hardware limitations. We ...
Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks
Nickholas Gutierrez, Rodrigo Araiza Bravo, Susanne Yelin·Dec 9, 2025
Spin squeezing is a powerful resource for quantum metrology, and recent hardware platforms based on interacting qubits provide multiple possible architectures to generate and reverse squeezing during a sensing protocol. In this work, we compare the s...
Optimizing the dynamical preparation of quantum spin lakes on the ruby lattice
DinhDuy Vu, Dominik S. Kufel, Jack Kemp +3 more·Dec 9, 2025
Quantum spin liquids are elusive long-range entangled states. Motivated by experiments in Rydberg quantum simulators, recent excitement has centered on the possibility of dynamically preparing a state with quantum spin liquid correlation even when th...
Persistent coherent quantum dynamics in 2D long-range magnets via magnon binding
Vighnesh Dattatraya Naik, Markus Heyl·Dec 9, 2025
The dynamics of 2D long-range quantum magnets represents a current frontier in experimental physics such as in Rydberg atomic systems or trapped ions. In this work we address theoretical challenges in understanding these dynamics by combining large-s...
SAQ: Stabilizer-Aware Quantum Error Correction Decoder
David Zenati, Eliya Nachmani·Dec 9, 2025
Quantum Error Correction (QEC) decoding faces a fundamental accuracy-efficiency tradeoff. Classical methods like Minimum Weight Perfect Matching (MWPM) exhibit variable performance across noise models and suffer from polynomial complexity, while tens...