Papers
Live trends in quantum computing research, updated daily from arXiv.
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Transformer-Based Neural Networks Backflow for Strongly Correlated Electronic Structure
Huan Ma, Bowen Kan, Honghui Shang +1 more·Sep 30, 2025
Solving the electronic Schrödinger equation for strongly correlated systems remains one of the grand challenges in quantum chemistry. Here we demonstrate that Transformer architectures can be adapted to capture the complex grammar of electronic corre...
Magnetometry with Broadband Microwave Fields in Nitrogen-Vacancy Centers in Diamond
Arezoo Afshar, Andrew Proppe, Noah Lupu-Gladstein +3 more·Sep 29, 2025
Nitrogen vacancy (NV) centers in diamond are optically addressable and versatile light-matter interfaces with practical application in magnetic field sensing, offering the ability to operate at room temperature and reach sensitivities below pT/$\sqrt...
Quantum Dynamics with Time-Dependent Neural Quantum States
Alejandro Romero-Ros, Javier Rozalén Sarmiento, Arnau Rios·Sep 29, 2025
We present proof-of-principle time-dependent neural quantum state (NQS) simulations to illustrate the ability of this approach to effectively capture key aspects of quantum dynamics in the continuum. NQS leverage the parameterization of the wave func...
SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination
Karl C. Linne, Sho Uemura, Yue Ji +5 more·Sep 29, 2025
Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control...
Probabilistic Graybox Characterization of Quantum Devices with Bayesian Neural Networks
Poramet Pathumsoot, Michal Hajdušek, Rodney Van Meter·Sep 29, 2025
While the Graybox characterization method allows for implicit noise models and is platform-agnostic, the method lacks uncertainty quantification. Characterization of quantum devices is a crucial process that enables researchers to gain insight from e...
Multi-channel convolutional neural quantum embedding
Yujin Kim, Changjae Im, Taehyun Kim +2 more·Sep 26, 2025
Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational quantum circuits involves embedding the data into a quantum Hilbert s...
Decoding quantum low density parity check codes with diffusion
Zejun Liu, Anqi Gong, Bryan K. Clark·Sep 26, 2025
An efficient decoder is essential for quantum error correction, and data-driven neural decoders have emerged as promising, flexible solutions. Here, we introduce a diffusion model framework to infer logical errors from syndrome measurements in quantu...
Supervised Machine Learning for Predicting Open Quantum System Dynamics and Detecting Non-Markovian Memory Effects
Ali Abu-Nada, Subhashish Banerjee·Sep 26, 2025
We present a \emph{novel} and scalable supervised machine learning framework to predict open-quantum system dynamics and detect non-Markovian memory using only local ancilla measurements. A system qubit is coherently coupled to an ancilla via a symme...
Machine Learning for Quantum State Tomography: Robust Covariance Matrix Estimation for Squeezed Vacuum States with Thermal Noise
Juan Camilo Rodrıguez, Hsien-Yi Hsieh, Hua-Li Chen +3 more·Sep 26, 2025
We present a supervised machine learning-based method using convolutional neural networks to estimate the covariance matrix of Gaussian quantum states in the presence of thermal noise. Unlike computationally intensive density matrix reconstructions, ...
PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
Yiming Huang, Yajie Hao, Jing Zhou +3 more·Sep 25, 2025
Variational quantum algorithms (VQAs) are leading strategies to reach practical utilities of near-term quantum devices. However, the no-cloning theorem in quantum mechanics precludes standard backpropagation, leading to prohibitive quantum resource c...
AI-enhanced Quantum Simulation of Schwinger Model
Ao-Ning Wang, Min-Quan He, Z. D. Wang·Sep 24, 2025
The Schwinger Model from Quantum Electrodynamics (QED) has long served as a valuable simplified model for exploring key physical phenomena in Quantum Chromodynamics (QCD)-a field rich with fundamental insights but is substantially more complex. While...
Digital Signal Processing from Classical Coherent Systems to Continuous-Variable QKD: A Review of Cross-Domain Techniques, Applications, and Challenges
Davi Juvêncio Gomes de Sousa, Caroline da Silva Morais Alves, Valéria Loureiro da Silva +1 more·Sep 24, 2025
This systematic review investigates the application of digital signal processing (DSP) techniques -- originally developed for coherent optical communication systems to continuous-variable quantum key distribution (CV-QKD). The convergence of these do...
Universal quantum computation in topological quantum neural networks and amplituhedron representation
Chris Fields, James F. Glazebrook, Antonino Marcianò +1 more·Sep 24, 2025
We study the relationship between computation and scattering both operationally (hence phenomenologically) and formally. We show how topological quantum neural networks (TQNNs) enable universal quantum computation, using the Reshetikhin-Turaev and Tu...
Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
Marc-Antoine Roux, Joffrey Rivard, Victor Yon +18 more·Sep 23, 2025
Spin qubits need to operate within a very precise voltage space around charge state transitions to achieve high-fidelity gates. However, the stability diagrams that allow the identification of the desired charge states are long to acquire. Moreover, ...
Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints
Abhijit Sen, Illya V. Lukin, Kurt Jacobs +3 more·Sep 23, 2025
The prediction of quantum dynamical responses lies at the heart of modern physics. Yet, modeling these time-dependent behaviors remains a formidable challenge because quantum systems evolve in high-dimensional Hilbert spaces, often rendering traditio...
Machine learning approach to single-shot multiparameter estimation for the non-linear Schrödinger equation
Louis Rossignol, Tangui Aladjidi, Myrann Baker-Rasooli +1 more·Sep 23, 2025
The nonlinear Schrödinger equation (NLSE) is a fundamental model for wave dynamics in nonlinear media ranging from optical fibers to Bose-Einstein condensates. Accurately estimating its parameters, which are often strongly correlated, from a single m...
Training the classification capability of large-scale quantum cellular automata
Mario Boneberg, Simon Kochsiek, Gabriele Perfetto +1 more·Sep 22, 2025
In the vicinity of a phase transition ergodicity can be broken. Here, different initial many-body configurations evolve towards one of several fixed points, which are macroscopically distinguishable through an order parameter. This mechanism enables ...
Accelerated characterization of two-level systems in superconducting qubits via machine learning
Avinash Pathapati, Olli Mansikkamäki, Alexander Tyner +1 more·Sep 22, 2025
We introduce a data-driven approach for extracting two-level system (TLS) parameters-frequency $ω_{TLS}$, coupling strength $g$, dissipation time $T_{TLS, 1}$, and the pure dephasing time $T^φ_{TLS, 2}$, labelled as a 4-component vector $\vec{q}$, di...
Knowledge Distillation for Variational Quantum Convolutional Neural Networks on Heterogeneous Data
Kai Yu, Binbin Cai, Song Lin·Sep 20, 2025
Distributed quantum machine learning faces significant challenges due to heterogeneous client data and variations in local model structures, which hinder global model aggregation. To address these challenges, we propose a knowledge distillation frame...
Quantum Enhanced Anomaly Detection for ADS-B Data using Hybrid Deep Learning
Rani Naaman, Felipe Gohring de Magalhaes, Jean-Yves Ouattara +1 more·Sep 19, 2025
The emerging field of Quantum Machine Learning (QML) has shown promising advantages in accelerating processing speed and effectively handling the high dimensionality associated with complex datasets. Quantum Computing (QC) enables more efficient data...