The paper systematically compares four classical machine learning models—SVM, ANN, CNN, and LSTM—with their quantum equivalents—QSVM, QNN, QCNN, and QLSTM—on simulated proton‑proton collision data from CERN Open Data. Classical models, especially CNN and LSTM, slightly outperform the quantum models under current hardware and dataset constraints, but quantum models achieve comparable accuracy with far fewer trainable parameters; for example, the QCNN matches a deep classical CNN using only four qubits and a depth‑three circuit. The study also shows that the regression task is non‑trivial for shallow polynomial fits, underscoring the relevance of the architectural comparison.
whyItMatters":"The work provides a realistic benchmark of classical versus quantum machine learning performance on high‑energy physics data, highlighting parameter‑efficiency advantages of quantum models for near‑term devices."
By Tariq Mahmood, Zain ul Abidin, Itzel Luviano Soto, Alfredo Raya
The paper compares classical and hybrid quantum machine learning models for a trigger-like binary classification task using CMS open data. Eight classical models (SVM, ANN, CNN, LSTM) and eight quantum counterparts are evaluated under identical preprocessing, data splits, and decision thresholds, with performance measured by accuracy, ROC‑AUC, F1‑score, precision, and recall. The best classical model is an artificial neural network (93.53 % accuracy, 0.9819 ROC‑AUC), while the best quantum model is a quantum convolutional network (90.89 % accuracy, 0.9731 ROC‑AUC), indicating that within an eight‑qubit budget the quantum models do not surpass the classical ones.
By Tariq Mahmood, Muhammad Awais Rafique, Talab Hussain, Juan Pablo Perez Aguilar, Alfredo Raya, Muhammad Ahsan
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
arXiv:2602. 15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments.
By Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar, Victoria Nguyen, Sebastian Schmitt, Marco Pizzichemi, Loris Martinazzoli, Eluned Anne Smith
arXiv:2605. 06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states.
By Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang, Chen-Yu Liu, En-Jui Kuo, Yun-Yuan Wang, Prayag Tiwari, Andrea Ceschini, Chi-Sheng Chen, Yu-Chao Hsu, Chun-Hua Lin, Tai-Yue Li, Antonello Rosato, Massimo Panella, Simon See, Saif Al-Kuwari, Kuan-Cheng Chen, Nan-Yow Chen, Hsi-Sheng Goan
arXiv:2607. 29145v1 Announce Type: cross Abstract: Quantum software engineering is an emerging research field focusing on efficiently embedding the quantum programming paradigm into existing software ecosystems.
By Piotr Malkowski, Domenik Eichhorn, Joshua Ammermann, Rinor Kelmendi, Nick Poser, Patrick Hopf, Ina Schaefer
The paper introduces MQSS-Selector, a reinforcement‑learning guided pass selection system for an MLIR compilation pipeline aimed at unified High Performance Computing‑Quantum Computing (HPCQC) infrastructures. It addresses the challenges of Noisy Intermediate‑Scale Quantum (NISQ) devices by integrating device selection, compiler‑pass optimization, and job queue scheduling into a single learning‑based framework. The selector can simultaneously optimize multiple objectives—fidelity, compilation time, and scheduling latency—while adapting to circuit characteristics and device conditions.
By Andre Youssefi (Leibniz Supercomputing Centre), Erc\"ument Kaya (Leibniz Supercomputing Centre, Technical University of Munich), Minh Chung (Leibniz Supercomputing Centre), Jorge Echavarria (Munich Quantum Valley), Laura B. Schulz (Argonne National Laboratory), Martin Schulz (Leibniz Supercomputing Centre, Technical University of Munich)
arXiv:2606. 14822v1 Announce Type: cross Abstract: Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures.
By L\'eo Monbroussou
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:2607. 07634v1 Announce Type: cross Abstract: Time series analysis plays a vital role across a wide range of scientific and engineering domains but poses substantial computational challenges.
By Leonardo Nogueira Falabella, Vasily Sazonov
arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
By Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy
arXiv:2607. 02413v1 Announce Type: cross Abstract: Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-informed analysis techniques for cold-atom experiments.
By M. Doris, S. Guo, S. M. Koh, L. Ritter, A. R. Fritsch, S. Mukherjee, I. B. Spielman, J. P. Zwolak