Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks
Marco Mordacci, Michele Amoretti·Sep 19, 2025
In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective ...
Neural Architecture Search Algorithms for Quantum Autoencoders
Ankit Kulshrestha, Xiaoyuan Liu, Hayato Ushijima-Mwesigwa +1 more·Sep 18, 2025
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the ta...
TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Yifeng Peng, Xinyi Li, Samuel Yen-Chi Chen +4 more·Sep 18, 2025
Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bo...
Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
Yan Chen Jiang, Lu Ma, Chuan Wang +1 more·Sep 17, 2025
The coherent Ising machine (CIM) is a quantum-inspired computing platform that leverages optical parametric oscillation dynamics to solve combinatorial optimization problems by searching for the ground state of an Ising Hamiltonian. Conventional CIM ...
Solving Differential Equation with Quantum-Circuit Enhanced Physics-Informed Neural Networks
Rachana Soni·Sep 17, 2025
I present a simple hybrid framework that combines physics informed neural networks (PINNs) with features generated from small quantum circuits. As a proof of concept, a first-order equation is solved by feeding quantum measurement probabilities into ...
Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment
Zheng-an Wang, Yanbo J. Wang, Jiachi Zhang +11 more·Sep 17, 2025
Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critical issue in inclusive finance w...
Machine Learning for Quantum Noise Reduction
Karan Kendre·Sep 17, 2025
Quantum noise fundamentally limits the utility of near-term quantum devices, making error mitigation essential for practical quantum computation. While traditional quantum error correction codes require substantial qubit overhead and complex syndrome...
QLook:Quantum-Driven Viewport Prediction for Virtual Reality
Niusha Sabri Kadijani, Yoga Suhas Kuruba Manjunath, Xiaodan Bi +1 more·Sep 16, 2025
We propose QLook, a quantum-driven predictive framework to improve viewport prediction accuracy in immersive virtual reality (VR) environments. The framework utilizes quantum neural networks (QNNs) to model the user movement data, which has multiple ...
Efficient lattice field theory simulation using adaptive normalizing flow on a resistive memory-based neural differential equation solver
Meng Xu, Jichang Yang, Ning Lin +6 more·Sep 16, 2025
Lattice field theory (LFT) simulations underpin advances in classical statistical mechanics and quantum field theory, providing a unified computational framework across particle, nuclear, and condensed matter physics. However, the application of thes...
HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification
Shahjalal, Jahid Karim Fahim, Pintu Chandra Paul +3 more·Sep 16, 2025
Classification of medical images plays a vital role in medical image analysis; however, it remains challenging due to the limited availability of labeled data, class imbalances, and the complexity of medical patterns. To overcome these challenges, we...
Designing Shadow Tomography Protocols by Natural Language Processing
Yadong Wu, Pengfei Zhang, Ce Wang +2 more·Sep 16, 2025
Quantum circuits form a foundational framework in quantum science, enabling the description, analysis, and implementation of quantum computations. However, designing efficient circuits, typically constructed from single- and two-qubit gates, remains ...
Large Language Model Scaling Laws for Neural Quantum States in Quantum Chemistry
Oliver Knitter, Dan Zhao, Stefan Leichenauer +1 more·Sep 16, 2025
Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources. Motivated by the fact that neural quantum states (NQS) has increasingly adopted LLM-b...
Efficient Privacy-Preserving Training of Quantum Neural Networks by Using Mixed States to Represent Input Data Ensembles
Gaoyuan Wang, Jonathan Warrell, Mark Gerstein·Sep 15, 2025
Quantum neural networks (QNNs) are gaining increasing interest due to their potential to detect complex patterns in data by leveraging uniquely quantum phenomena. This makes them particularly promising for biomedical applications. In these applicatio...
Neural-Quantum-States Impurity Solver for Quantum Embedding Problems
Yinzhanghao Zhou, Tsung-Han Lee, Ao Chen +2 more·Sep 15, 2025
Neural quantum states (NQS) have emerged as a promising approach to solve second-quantized Hamiltonians, because of their scalability and flexibility. In this work, we design and benchmark an NQS impurity solver for the quantum embedding (QE) methods...
Entanglement and optimization within autoregressive neural quantum states
Andrew Jreissaty, Hang Zhang, Jairo C. Quijano +2 more·Sep 15, 2025
Neural quantum states (NQSs) are powerful variational ansätze capable of representing highly entangled quantum many-body wavefunctions. While the average entanglement properties of ensembles of restricted Boltzmann machines are well understood, the e...
Accurate ground states of $SU(2)$ lattice gauge theory in 2+1D and 3+1D
Thomas Spriggs, Eliska Greplova, Juan Carrasquilla +1 more·Sep 15, 2025
We present a neural network wavefunction framework for solving non-Abelian lattice gauge theories in a continuous group representation. Using a combination of $SU(2)$ equivariant neural networks alongside an $SU(2)$ invariant, physics-inspired ansatz...
High-capacity associative memory in a quantum-optical spin glass
Brendan P. Marsh, David Atri Schuller, Yunpeng Ji +5 more·Sep 15, 2025
The Hopfield model describes a neural network that stores memories using all-to-all-coupled spins. Memory patterns are recalled under equilibrium dynamics. Storing too many patterns breaks the associative recall process because frustration causes an ...
Characterizing Scaling Trends of Post-Compilation Circuit Resources for NISQ-era QML Models
Rupayan Bhattacharjee, Pau Escofet, Santiago Rodrigo +3 more·Sep 15, 2025
This work investigates the scaling characteristics of post-compilation circuit resources for Quantum Machine Learning (QML) models on connectivity-constrained NISQ processors. We analyze Quantum Kernel Methods and Quantum Neural Networks across proce...
Quantum Noise Tomography with Physics-Informed Neural Networks
Antonin Sulc·Sep 15, 2025
Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we intr...
Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
Arthur M. Faria, Mehdi Djellabi, Igor O. Sokolov +1 more·Sep 14, 2025
We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework. QGATs leverage parameterized quantum circuits to encode node features ...