arXiv Machine Learning

A Hybrid Quantum-Classical Approach for Melt Pool Prediction in Laser Powder Bed Fusion

arXiv:2606. 23719v1 Announce Type: cross Abstract: Laser powder bed fusion (LPBF) is a promising additive manufacturing technique that suffers from quality assurance concerns.

arXiv Machine Learning

Aug 4

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

arXiv:2608. 01194v1 Announce Type: cross Abstract: Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues.

By L\'eo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin, Pavel Sekatski, Viktoria Patapovich, Asel Sagingalieva, Alexey Melnikov

benchmarks

arXiv Machine Learning

Jul 10

Image classification via a quantum-inspired strategy involving a mixture of experts

arXiv:2607. 07754v1 Announce Type: new Abstract: Pattern recognition problems arise in a variety of physical image processing situations, and convolutional neural networks are a popular scheme for the required feature extraction and classification tasks.

By Kumari Jyoti, Rohith Babu, Apoorva D. Patel

diffusion computer-vision benchmarks

arXiv Machine Learning

Jun 9

Quantum feature-map learning with reduced resource overhead

arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.

By Jonas J\"ager, Philipp Els\"asser, Elham Torabian

efficiency benchmarks

arXiv AI

Aug 12

Regression and Classification with Single-Qubit Quantum Neural Networks

arXiv:2412. 09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other.

By Leandro C. Souza, Bruno C. Guingo, Gilson Giraldi, Renato Portugal

arXiv AI

Aug 12

Quantum Incremental Learning with Mixed State Prototypes

arXiv:2608. 10464v1 Announce Type: new Abstract: Incremental learning models are required to learn new classes sequentially without catastrophic forgetting, while operating under parameter and memory constraints.

By Yu Wu, Qianli Zhou, Xinyang Deng, Wen Jiang, Kang Hao Cheong, Witold Pedrycz

arXiv Machine Learning

Sep 10

Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning

arXiv:2609.06016v1 Announce Type: new Abstract: Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this...

By Suzhen Yuan, Qilin Xie, Lifeng Shen, Shuyin Xia, Jermiah D. Deng, Guoying Wang

arXiv AI

Jul 2

When AI meets quantum information: A comprehensive review

arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.

By Min Chen, Yu Gan, Xin Jin, Yuqing Li, Junqi Wang, Zeguan Wu, Yunfei Wang, Bingzhi Zhang, Priyam Srivastava, Tianlong Chen, Ankit Kulshrestha, Yuan Liu, Juan Jos\'e Mendoza-Arenas, Kaushik P. Seshadreesan, Sarvagya Upadhyay, Xueyue Zhang, Quntao Zhuang, Junyu Liu

arXiv Machine Learning

Jul 15

Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning

arXiv:2506. 21324v3 Announce Type: replace-cross Abstract: Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths.

By Jiechen Chen, Bipin Rajendran, Osvaldo Simeone

arXiv AI

Sep 10

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

arXiv:2411.10406v4 Announce Type: replace-cross Abstract: In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, sm...

By Masoud Mohseni, Artur Scherer, K. Grace Johnson, Oded Wertheim, Matthew Otten, Namit Anand, Navid Anjum Aadit, Yuri Alexeev, Gilad Ben-Shach, Kirk M. Bresniker, Kerem Y. Camsari, Barbara Chapman, Soumitra Chatterjee, Shuvro Chowdhury, Gebremedhin A. Dagnew, Tom Dvir, Aniello Esposito, Farah Fahim, Michael Ferguson, Marco Fiorentino, Archit Gajjar, Katerina Gratsea, Gaurav Gyawali, Christian Heiter, Ali H. Z. Kavaki, Abdullah Khalid, Xiangzhou Kong, Bohdan Kulchytskyy, Elica Kyoseva, Ruoyu Li, P. Aaron Lott, Igor L. Markov, Robert F. McDermott, Lucas Morais, Giacomo Pedretti, Pooja Rao, Eleanor Rieffel, Allyson Silva, John Sorebo, Panagiotis Spentzouris, Ziv Steiner, Boyan Torosov, Davide Venturelli, Robert J. Visser, Zak Webb, Xin Zhan, Yonatan Cohen, Pooya Ronagh, Alan Ho, Raymond G. Beausoleil, John M. Martinis

arXiv Machine Learning

Jul 30

Shot-based quantum encoding: a data-loading paradigm for quantum neural networks

arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.

By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov

rag benchmarks

arXiv AI

Aug 6

Image Classification Using CNN-QNN Hybrid Model with Optimized Correlated Features

arXiv:2608. 04379v1 Announce Type: cross Abstract: We propose a method to optimize the correlation among convolutional neural network (CNN) features that are used as inputs to quantum neural network (QNN) to enhance image classification accuracy.

By Minseo Seong, Youngwook Kim

computer-vision robotics

arXiv Machine Learning

Jul 3

Ravines in quantum cost landscapes: opportunities for improved VQA predictions

arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).

By Felix J. Beckmann, Jo\~ao F. Bravo

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