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
QuIRK: Quantum-Inspired Re-uploading KAN
Vinayak Sharma, Ashish Padhy, Lord Sen +4 more·Oct 9, 2025
Kolmogorov-Arnold Networks or KANs have shown the ability to outperform classical Deep Neural Networks, while using far fewer trainable parameters for regression problems on scientific domains. Even more powerful has been their interpretability due t...
Near-limit quantum control beyond analytic tractability in many-body spin systems
Jixing Zhang, Bo Peng, Yang Wang +11 more·Oct 9, 2025
As quantum control approaches hardware-imposed performance limits, weak effects omitted by reduced models become consequential. Assumptions required for analytic tractability then cease to guide control design and instead constrain further improvemen...
Spin quantum computing, spin quantum cognition
Betony Adams, Francesco Petruccione·Oct 8, 2025
Over two decades ago, Bruce Kane proposed that spin-half phosphorus nuclei embedded in a spin-zero silicon substrate could serve as a viable platform for spin-based quantum computing. These nuclear spins exhibit remarkably long coherence times, makin...
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2 more·Oct 8, 2025
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines remains unclear. In this work, we take a step towards bridging this gap b...
Fisher Information, Training and Bias in Fourier Regression Models
Lorenzo Pastori, Veronika Eyring, Mierk Schwabe·Oct 8, 2025
Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training ...
Adapting Quantum Machine Learning for Energy Dissociation of Bonds
Swathi Chandrasekhar, Shiva Raj Pokhrel, Navneet Singh·Oct 8, 2025
Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE...
Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms
N. Meli, Shuteng Wang, Marcel Seelbach Benkner +5 more·Oct 8, 2025
Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high potential to transform how visual signals are processed and interpreted w...
Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
Aueaphum Aueawatthanaphisut, Nyi Wunna Tun·Oct 7, 2025
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environ...
From Classical Rationality to Contextual Reasoning: Quantum Logic as a New Frontier for Human-Centric AI in Finance
Fabio Bagarello, Francesco Gargano, Polina Khrennikova·Oct 7, 2025
We consider state of the art applications of artificial intelligence (AI) in modelling human financial expectations and explore the potential of quantum logic to drive future advancements in this field. This analysis highlights the application of mac...
A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices
Yuqi Zhang, Yuxin Yang, Feixiong Chen +7 more·Oct 7, 2025
Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework...
Quantum Reservoir Computing for Credit Card Default Prediction on a Neutral Atom Platform
Giacomo Vitali, Chiara Vercellino, Paolo Viviani +9 more·Oct 6, 2025
In this paper, we define and benchmark a hybrid quantum-classical machine learning pipeline by performing a binary classification task applied to a real-world financial use case. Specifically, we implement a Quantum Reservoir Computing (QRC) layer wi...
Subsystem many-hypercube codes: High-rate concatenated codes with low-weight syndrome measurements
Ryota Nakai, Hayato Goto·Oct 6, 2025
Quantum error-correcting codes (QECCs) require high encoding rate in addition to high threshold unless a sufficiently large number of physical qubits are available. The many-hypercube (MHC) codes defined as the concatenation of the [[6,4,2]] quantum ...
Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes
Xiangjun Mi, Frank Mueller·Oct 5, 2025
Quantum error correction (QEC) is essential for scalable quantum computing, yet decoding errors via conventional algorithms result in limited accuracy (i.e., suppression of logical errors) and high overheads, both of which can be alleviated by infere...
Quantum feature-map learning with reduced resource overhead
Jonas Jäger, Philipp Elsässer, Elham Torabian·Oct 3, 2025
Current quantum computers require algorithms that use limited resources economically. In quantum machine learning, success hinges on quantum feature maps, which embed classical data into the state space of qubits. We introduce Quantum Feature-Map Lea...
Fast surrogate modelling of EIT in atomic quantum systems using LSTM neural networks
Isabel S. Burdon Hita, Óscar Iglesias-González, Gabriel M. Carral +1 more·Oct 2, 2025
Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter estimation...
Improving neural network performance for solving quantum sign structure
Xiaowei Ou, Tianshu Huang, Vidvuds Ozolins·Oct 2, 2025
Neural quantum states have emerged as a widely used approach to the numerical study of the ground states of non-stoquastic Hamiltonians. However, existing approaches often rely on a priori knowledge of the sign structure or require a separately pre-t...
HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
Junggu Choi, Junho Lee, Kyle L. Jung +1 more·Oct 2, 2025
In this study, we propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins. To assess the effectivenes...
On the Relativity of Quantumness as Implied by Relativity of Arithmetic and Probability
Marek Czachor·Oct 1, 2025
A hierarchical structure of isomorphic arithmetics is defined by a bijection $g_\mathbb{R}:\mathbb{R}\to \mathbb{R}$. It entails a hierarchy of probabilistic models, with probabilities $p_k=g^k(p)$, where $g$ is the restriction of $g_\mathbb{R}$ to t...
Quantum Probabilistic Label Refining: Enhancing Label Quality for Robust Image Classification
Fang Qi, Lu Peng, Zhengming Ding·Oct 1, 2025
Learning with softmax cross-entropy on one-hot labels often leads to overconfident predictions and poor robustness under noise or perturbations. Label smoothing mitigates this by redistributing some confidence uniformly, but treats all samples equall...
Nondestructive characterization of laser-cooled atoms using machine learning
G. De Sousa, M. Doris, D. D'Amato +3 more·Sep 30, 2025
We develop machine learning techniques for estimating physical properties of laser-cooled potassium-39 atoms in a magneto-optical trap using only the scattered light -- i.e., fluorescence -- that is intrinsic to the cooling process. In-situ snap-shot...