Papers
Live trends in quantum computing research, updated daily from arXiv.
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12,429 papers in 12 months (-19% vs prior quarter)
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
Operator Lanczos Approach enabling Neural Quantum States as Real-Frequency Impurity Solvers
Jonas B. Rigo, Markus Schmitt·Dec 9, 2025
To understand the intricate exchange between electrons of different bands in strongly correlated materials, it is essential to treat multi-orbital models accurately. For this purpose, dynamical mean-field theory (DMFT) provides an established framewo...
Pulse Shape Discrimination for Germanium Detectors using Variational Quantum Circuits
F. Napolitano·Dec 9, 2025
Pulse shape discrimination (PSD) is a critical component in background rejection for neutrinoless double-beta decay and dark matter searches using Broad Energy Germanium (BEGe) detectors. To date, advanced discrimination has relied on Deep Learning a...
LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout
M. A. Farooq, G. Di Guglielmo, A. Rajagopala +3 more·Dec 8, 2025
Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior...
Trapped Fermions Through Kolmogorov-Arnold Wavefunctions
Paulo F. Bedaque, Jacob Cigliano, Hersh Kumar +2 more·Dec 8, 2025
We investigate a variational Monte Carlo framework for trapped one-dimensional mixture of spin-$\frac{1}{2}$ fermions using Kolmogorov-Arnold networks (KANs) to construct universal neural-network wavefunction ansätze. The method can, in principle, ac...
A scalable and real-time neural decoder for topological quantum codes
Andrew W. Senior, Thomas Edlich, Francisco J. H. Heras +21 more·Dec 8, 2025
Fault-tolerant quantum computing will require error rates far below those achievable with physical qubits. Quantum error correction (QEC) bridges this gap, but depends on decoders being simultaneously fast, accurate, and scalable. This combination of...
Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
Chi-Sheng Chen, Xinyu Zhang, En-Jui Kuo +3 more·Dec 7, 2025
Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shif...
PERM EQ x GRAPH EQ: Equivariant Neural Networks for Quantum Molecular Learning
Saumya Biswas, Jiten Oswal·Dec 5, 2025
In hierarchal order of molecular geometry, we compare the performances of Geometric Quantum Machine Learning models. Two molecular datasets are considered: the simplistic linear shaped LiH-molecule and the trigonal pyramidal molecule NH3. Both accura...
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
Bruno Jacob, Amanda A. Howard, Panos Stinis·Dec 4, 2025
Quantum algorithms for partial differential equations (PDEs) face severe practical constraints on near-term hardware: limited qubit counts restrict spatial resolution to coarse grids, while circuit depth limitations prevent accurate long-time integra...
Hardware-inspired Continuous Variables Quantum Optical Neural Networks
Todor Krasimirov-Ivanov, Alba Cervera-Lierta, Paolo Stornati +1 more·Dec 4, 2025
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mapping...