Papers
Live trends in quantum computing research, updated daily from arXiv.
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Qubit Platforms
Hardware platform mentions in abstracts — Photonic leads
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...
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...
Quantum AI Algorithm Development for Enhanced Cybersecurity: A Hybrid Approach to Malware Detection
Tanya Joshi, Krishnendu Guha·Sep 4, 2025
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine lear...
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...
NeuroQD: A Learning-Based Simulation Framework For Quantum Dot Devices
Shize Che, Junyu Zhou, Seongwoo Oh +6 more·Sep 2, 2025
Electron spin qubits in quantum dot devices are promising for scalable quantum computing. However, architectural support is currently hindered by the lack of realistic and performant simulation methods for real devices. Physics-based tools are accura...
Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
Sayan Manna, M. Mohan·Sep 2, 2025
Quantum algorithms operate on quantum states through unitary transformations in high dimensional complex Hilbert space. In this work, we propose a machine learning approach to create the quantum circuit using a single-layer complex-valued neural netw...
Quantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder
J. Orduz, P. Rivas, E. Baker·Aug 30, 2025
Quantum machine learning deals with leveraging quantum theory with classic machine learning algorithms. Current research efforts study the advantages of using quantum mechanics or quantum information theory to accelerate learning time or convergence....
Application of Quantum Convolutional Neural Networks for MRI-Based Brain Tumor Detection and Classification
Sugih Pratama Nugraha, Ariiq Islam Alfajri, T. Sumaryada +4 more·Aug 28, 2025
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) for brain tumor classification using MRI images, leveraging quantum computing for enhanced computational efficiency. A dataset of 3,264 MRI images, including glioma,...
Towards Quantum Machine Learning for Malicious Code Analysis
Jesus Lopez, Saeefa Rubaiyet Nowmi, Viviana Cadena +1 more·Aug 26, 2025
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its applic...
Can Classical Initialization Help Variational Quantum Circuits Escape the Barren Plateau?
Yifeng Peng, Xinyi Li, Zhemin Zhang +3 more·Aug 25, 2025
Variational quantum algorithms (VQAs) have emerged as a leading paradigm in near-term quantum computing, yet their performance can be hindered by the so-called barren plateau problem, where gradients vanish exponentially with system size or circuit d...
Quantum Neural Ordinary and Partial Differential Equations
Yu Cao, Shi Jin, Nana Liu·Aug 24, 2025
We introduce a unified framework -- Quantum Neural Ordinary and Partial Differential Equations (QNODEs and QNPDEs) -- which extends the continuous-time formalism of classical neural ordinary and partial differential equations into quantum machine lea...
Quantum State Fidelity for Functional Neural Network Construction
Sk Chan, W. Smith, Kyla Gabriel·Aug 23, 2025
Neuroscientists face challenges in analyzing high-dimensional neural recording data of dense functional networks. Without ground-truth reference data, finding the best algorithm for recovering neurologically relevant networks remains an open question...
Improving Quantum Recurrent Neural Networks with Amplitude Encoding
Jack Morgan, Hamed Mohammadbagherpoor, Eric Ghysels·Aug 22, 2025
Quantum machine learning holds promise for advancing time series forecasting. The Quantum Recurrent Neural Network (QRNN), inspired by classical RNNs, encodes temporal data into quantum states that are periodically input into a quantum circuit. While...
End-to-End Analysis of Charge Stability Diagrams with Transformers
Rahul Marchand, Lucas Schorling, Cornelius Carlsson +8 more·Aug 21, 2025
Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to analyze charge stability diagrams in semiconductor quantum dot arrays, a k...
Graybox characterization and calibration with finite-shot estimation on superconducting-qubit experiments
Poramet Pathumsoot, A. Chantasri, Michal Hajduvsek +1 more·Aug 18, 2025
Characterization and calibration of quantum devices are necessary steps to achieve fault-tolerant quantum computing. As quantum devices become more sophisticated, it is increasingly essential to rely not only on physics-based models, but also on pred...
SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization
Ratun Rahman, Atit Pokharel, Md Raihan Uddin +1 more·Aug 17, 2025
Quantum federated learning (QFL) is an emerging field that has the potential to revolutionize computation by taking advantage of quantum physics concepts in a distributed machine learning (ML) environment. However, the majority of available quantum s...