Papers
Live trends in quantum computing research, updated daily from arXiv.
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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 ...
Neural Decoders for Universal Quantum Algorithms
J. Pablo Bonilla Ataides, Andi Gu, Susanne F. Yelin +1 more·Sep 14, 2025
Fault-tolerant quantum computing demands decoders that are fast, accurate, and adaptable to circuit structure and realistic noise. While machine learning (ML) decoders have demonstrated impressive performance for quantum memory, their use in algorith...
Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits
Michael Kölle, Leonhard Klingert, Julian Schönberger +3 more·Sep 14, 2025
Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quantum physics, it can surpass the limitations of classical algorithms. H...
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
Amanuel Anteneh·Sep 12, 2025
We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty in parameter estimates, which is a key advantage of using Bayesian inf...
A Symmetry-Integrated Approach to Surface Code Decoding
Hoshitaro Ohnishi, Hideo Mukai·Sep 12, 2025
Quantum error correction, which utilizes logical qubits that are encoded as redundant multiple physical qubits to find and correct errors in physical qubits, is indispensable for practical quantum computing. Surface code is considered to be a promisi...
Thermodynamic coprocessor for linear operations with input-size-independent calculation time based on open quantum system
I. V. Vovchenko, A. A. Zyablovsky, A. A. Pukhov +1 more·Sep 11, 2025
Linear operations, e.g., vector-matrix and vector-vector multiplications, are core operations of modern neural networks. To diminish computational time, these operations are implemented by parallel computations using different coprocessors. In this w...
Quantum-Enhanced Forecasting for Deep Reinforcement Learning in Algorithmic Trading
Jun-Hao Chen, Yu-Chien Huang, Yun-Cheng Tsai +1 more·Sep 11, 2025
The convergence of quantum-inspired neural networks and deep reinforcement learning offers a promising avenue for financial trading. We implemented a trading agent for USD/TWD by integrating Quantum Long Short-Term Memory (QLSTM) for short-term trend...
Machine learning the effects of many quantum measurements
W. Hou, Samuel J. Garratt, N. Eassa +4 more·Sep 10, 2025
Measurements are essential for the processing and protection of information in quantum computers. They can also induce long-range entanglement between unmeasured qubits. However, when post-measurement states depend on many non-deterministic measureme...
D2D Power Allocation via Quantum Graph Neural Network
Le Tung Giang, Nguyen Xuan Tung, W. Hwang·Sep 10, 2025
Increasing wireless network complexity demands scalable resource management. Classical GNNs excel at graph learning but incur high computational costs in large-scale settings. We present a fully quantum Graph Neural Network (QGNN) that implements mes...
Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
Borja Aizpurua, Sukhbinder Singh, Román Orús·Sep 8, 2025
We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz for representing large linear layers within hybrid classical-quantum mo...
A brain-inspired paradigm for scalable quantum vision
Chenghua Duan, Xiuxing Li, Wending Zhao +6 more·Sep 7, 2025
One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability...
From Membership-Privacy Leakage to Quantum Machine Unlearning
Jun-Jian Su, Runze He, Guanghui Li +4 more·Sep 7, 2025
Quantum Machine Learning (QML) has the potential to achieve quantum advantage for specific tasks by combining quantum computation with classical Machine Learning (ML). In classical ML, a significant challenge is membership privacy leakage, whereby an...
Learning Neural Decoding with Parallelism and Self-Coordination for Quantum Error Correction
Kai Zhang, Situ Wang, Linghang Kong +3 more·Sep 4, 2025
Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation. Neural network decoders like AlphaQubit have demonstrated significant potential, achieving higher accuracy than traditional human-designed decoding algori...
LATTE: A Decoding Architecture for Quantum Computing with Temporal and Spatial Scalability
Kai Zhang, Jubo Xu, Fang Zhang +3 more·Sep 4, 2025
Quantum error correction allows inherently noisy quantum devices to emulate an ideal quantum computer with reasonable resource overhead. As a crucial component, decoding architectures have received significant attention recently. In this paper, we in...