Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
Probing and Enhancing the Robustness of GNN-based QEC Decoders with Reinforcement Learning
Ryota Ikeda·Aug 5, 2025
Graph Neural Networks (GNNs) have emerged as a powerful, data-driven approach for Quantum Error Correction (QEC) decoding, capable of learning complex noise characteristics directly from syndrome data. However, the robustness of these decoders agains...
TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing
Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen +1 more·Aug 1, 2025
Variational Quantum Computing (VQC) faces fundamental scalability barriers, primarily due to barren plateaus and sensitivity to quantum noise. To address these challenges, we introduce TensorHyper-VQC, a novel tensor-train (TT)-guided hypernetwork fr...
Dimension reduction with structure-aware quantum circuits for hybrid machine learning
A. Daskin·Jul 31, 2025
Schmidt decomposition of a vector can be understood as writing the singular value decomposition (SVD) in vector form. A vector can be written as a linear combination of tensor product of two dimensional vectors by recursively applying Schmidt decompo...
Search for $t\bar tt\bar tW$ Production at $\sqrt{s} = 13$ TeV Using a Modified Graph Neural Network at the LHC
Syed Haider Ali, A. Ahmad, Muhammad Saiel +1 more·Jul 31, 2025
The simultaneous production of four top quarks in association with a ($W$) boson at $(\sqrt{s} = 13)$ TeV is an rare SM process with a next-to-leading-order (NLO) cross-section of $(6.6^{+2.4}_{-2.6} {ab})$\cite{saiel}. Identifying this process in th...
Neural network for excess noise estimation in continuous-variable quantum key distribution under composable finite-size security
Lucas Q. Galvão, Davi Juvêncio G. de Sousa, Micael Andrade Dias +1 more·Jul 30, 2025
Parameter estimation is a critical step in continuous-variable quantum key distribution (CV-QKD), especially in the finite-size regime where worst-case confidence intervals can significantly reduce the achievable secret-key rate. We provide a finite-...
Hybrid Quantum Classical Surrogate for Real Time Inverse Finite Element Modeling in Digital Twins
A. Alavi, Sanduni Jayasinghe, M. Mahmoodian +3 more·Jul 30, 2025
Large-scale civil structures, such as bridges, pipelines, and offshore platforms, are vital to modern infrastructure, where unexpected failures can cause significant economic and safety repercussions. Although finite element (FE) modeling is widely u...
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4 more·Jul 29, 2025
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks. This is especially relevant for financial time series, since we only ...
Pitfalls when tackling the exponential concentration of parameterized quantum models
Reyhaneh Aghaei Saem, Behrang Tafreshi, Zoe Holmes +1 more·Jul 29, 2025
Identifying scalable circuit architectures remains a central challenge in variational quantum computing and quantum machine learning. Many approaches have been proposed to mitigate or avoid the barren plateau phenomenon or, more broadly, exponential ...
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
S. A. C. Ord'onez, Luis Fernando Torres Torres, Mario Bifulco +3 more·Jul 28, 2025
Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipeline combining class-balanced k-means distillation with pretrained Vision...
A Novel Post-Quantum Secure Digital Signature Scheme Based on Neural Network
Satish Kumar, Md Arzoo Jamal·Jul 28, 2025
Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital documents. In the post-quantum era, classical public key-based signature schemes become vulnerable to brute-force and key-recovery attac...
Benchmarking a Tunable Quantum Neural Network on Trapped-Ion and Superconducting Hardware
Djamil Lakhdar-Hamina, Xingxin Liu, Richard Barney +4 more·Jul 28, 2025
We implement a quantum generalization of a neural network on trapped-ion and IBM superconducting quantum computers to classify MNIST images, a common benchmark in computer vision. The network feedforward involves qubit rotations whose angles depend o...
FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report
Matteo Cardaioli, Luca Marangoni, Giada Martini +5 more·Jul 25, 2025
The increasing complexity and volume of financial transactions pose significant challenges to traditional fraud detection systems. This technical report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning...
Quantum-Efficient Convolution through Sparse Matrix Encoding and Low-Depth Inner Product Circuits
Mohammad Rasoul Roshanshah, Payman Kazemikhah, Hossein Aghababa·Jul 25, 2025
Convolution operations are foundational to classical image processing and modern deep learning architectures, yet their extension into the quantum domain has remained algorithmically and physically costly due to inefficient data encoding and prohibit...
Graph Neural Network-Based Predictor for Optimal Quantum Hardware Selection
A. Tudisco, Deborah Volpe, Giacomo Orlandi +1 more·Jul 25, 2025
The growing variety of quantum hardware technologies, each with unique peculiarities such as connectivity and native gate sets, creates challenges when selecting the best platform for executing a specific quantum circuit. This selection process usual...
Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
R. V. Bhat, Rahul Bhowmick, Avinash Singh +1 more·Jul 22, 2025
The preparation of quantum Gibbs states is a fundamental challenge in quantum computing, essential for applications ranging from modeling open quantum systems to quantum machine learning. Building on the Meta-Variational Quantum Eigensolver framework...
Reconfigurable qubit states and quantum trajectories in a synthetic artificial neuron network with a process to direct information generation from co-integrated burst-mode spiking under non-Markovianity
Osama M. Nayfeh, C. Horne·Jul 22, 2025
The research and development of hardware neuron technologies are accelerating at a very fast pace to provide for increased efficiency in performing artificial intelligence and autonomy functions beyond that possible with emulation on digital computer...
A scalable quantum-neural hybrid variational algorithm for ground state estimation
Minwoo Kim, Kyoung Keun Park, Uihwan Jeong +2 more·Jul 15, 2025
We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss...
On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models
Silvie Illésová, Tomasz Rybotycki, Piotr Gawron +1 more·Jul 14, 2025
We present a systematic study of how quantum circuit design, specifically the depth of the variational ansatz and the choice of quantum feature mapping, affects the performance of hybrid quantum-classical neural networks on a causal classification ta...
Functional Neural Wavefunction Optimization
Victor Armegioiu, Juan Carrasquilla, Siddhartha Mishra +4 more·Jul 14, 2025
We propose a framework for the design and analysis of optimization algorithms in variational quantum Monte Carlo, drawing on geometric insights into the corresponding function space. The framework translates infinite-dimensional optimization dynamics...
Quantum Convolution for Structure-Based Virtual Screening
Pei-Kun Yang·Jul 13, 2025
Structure-based virtual screening (SBVS) is a key computational strategy for identifying potential drug candidates by estimating the binding free energies (delta G_bind) of protein-ligand complexes. The immense size of chemical libraries, combined wi...