Latest Quantum Machine Learning Research Papers
The newest Quantum Machine Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Quantum Machine Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.
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- Eliminating vendor lock-in in quantum machine learning via framework-agnostic neural networksPoornima Kumaresan, Shwetha Singaravelu, Lakshmi Rajendran, Santhosh Sivasubramani · Frontiers in Quantum Scienc... · Jul 22, 2026
Introduction Quantum machine learning (QML) sits at the intersection of quantum computing and artificial intelligence and offers the potential to solve problems that remain intractable for classical methods. However, the current landscape o…
- Quantum Federated Lasso With Anonymous Consensus Built Upon Quantum BlockchainLiwei Lin, Yue Wu, Chuan Huang, J M Chen et al. · Software Practice and Exper... · Jul 18, 2026
ABSTRACT Objective With the rapid advancement of quantum computing technologies, Quantum Federated Learning (QFL) has emerged as a promising framework for privacy‐preserving quantum machine learning (QML). QFL provides a promising approach …
- SAFE machine learning with variational quantum classifiersYuehua Chen, Paolo Giudici, Vasily Kolesnikov, Paolo Recchia et al. · Statistics · Jul 17, 2026
We propose a variational quantum classifier operating on high-dimensional deep representations via amplitude encoding, stabilized by a learnable classical pre-encoding layer. By combining normalized amplitude embeddings with bounded quantum…
- PsiAudit: An open-source toolkit for auditing symmetry-organised complexity in equivariant quantum neural networksHassan Ugail, Newton Howard · PLoS ONE · Jul 17, 2026
Parameterised quantum circuits are commonly assessed using measures such as expressibility, gradient behaviour, and entanglement. While useful, these measures do not indicate whether a circuit respects the symmetry it was designed to respec…
- Quantum machine learning for predicting properties of van der Waals bilayersChandra Chowdhury, Livia Giordano · The Journal of Chemical Phy... · Jul 17, 2026
Layering two-dimensional (2D) materials into van der Waals bilayers provides an effective method to achieve innovative quantum states and tunable electronic properties. Exploring the extensive configurational space resulting from various la…
- Quantum Machine Learning with Adaptive Quantum NetworksChristopher Altman · OpenAlex · Jul 17, 2026
- Quantum kernel-based delta (Δ)-learning for correcting a semiempirical method in the prediction of reaction barrier heightsArmaan Kautish, Ashwin Sivakumar, Viki Kumar Prasad · The Journal of Chemical Phy... · Jul 16, 2026
A quantum machine learning based Δ-learning framework is presented for the prediction of reaction barrier heights of organic molecules. Corrections learned by quantum kernel-based models are applied to mitigate the underlying errors of the …
- Proof-of-Concepts of Quantum-Enhanced Intelligent Transportation SystemsShiva Pokhrel, Hai L. Vu · Zenodo (CERN European Organ... · Jul 14, 2026
This article presents a comprehensive technical exposition of four proof-of-concept (PoC) implementations that systematically evaluate the applicability, efficacy, and performance characteristics of quantum machine learning (QML) methodolog…
- Quantum Machine Learning for Intelligent Transportation: A SurveyShiva Pokhrel, Hai L. Vu · Zenodo (CERN European Organ... · Jul 14, 2026
Intelligent Transportation Systems (ITS) face escalating demands for ultra-low latency, quantum-resilient security, efficient processing of heterogeneous high-volume data, and strict resource constraints—pressures that increasingly exceed t…
- Quantum Machine Learning for Intelligent Transportation: A SurveyShiva Pokhrel, Hai L. Vu · Zenodo (CERN European Organ... · Jul 14, 2026
Intelligent Transportation Systems (ITS) face escalating demands for ultra-low latency, quantum-resilient security, efficient processing of heterogeneous high-volume data, and strict resource constraints—pressures that increasingly exceed t…
- TOWARD QUANTUM-ENHANCED INTRUSION DETECTION: UNIFIED EVALUATION OF LEARNING MODELSV R Srividhya, Gali Akshata, Mamata Balesh Kamagoudar, Neha Shakari, Sampreeth N Ganganagoudar · Zenodo (CERN European Organ... · Jul 13, 2026
Intrusion Detection Systems (IDS) rely on intelligent learning techniques to identify complex and evolving attack patterns within network traffic. This paper presents a comparative study of Support Vector Machines (SVM), Quantum Support Vec…
- Towards Trustworthy Quantum Machine Learning in Clinical Diagnostics: A Multi-Method Explainability Study of a Hybrid Quantum-Classical Neural Network for Liver Disease DetectionFatima Masood · OpenAlex · Jul 12, 2026
- Variational Gibbs state preparation on trapped-ion devicesReece Robertson, Mirko Consiglio, Josey Stevens, Emery Doucet et al. · npj Quantum Information · Jul 11, 2026
We implement a variational quantum algorithm for Gibbs state preparation of a transverse-field Ising model on IonQ’s quantum computers. To this end, we train the variational parameters via classical simulation and perform state tomography o…
- Advancing genomics and integration of multi-omics for precision oncology using quantum machine learningJi‐Yong Sung, Jae‐Ho Cheong · npj Digital Medicine · Jul 11, 2026
Abstract Cancer multi-omics faces challenges in handling the scale, complexity, and heterogeneity of multi-omics data, limiting progress in variant interpretation, tumor classification, and modeling cancer evolution. Quantum computing offer…
- Decoding Protein Sequences as a 'Quantum Language'… Demonstration of a QNLP-Based Classification Pipelineinquantio · Zenodo (CERN European Organ... · Jul 10, 2026
A study published on bioRxiv demonstrates for the first time the technique of treating protein sequences like sentences in natural language processing (NLP) and parsing them into parameterized quantum circuits using the Quantum Natural Lang…
- Flexible genetic algorithm for quantum support vector machinesMinh D. Nguyen, Vu Tuan Hai, Le Bin Ho, Nguyen Lan Tran · Machine Learning Science an... · Jul 10, 2026
Abstract Quantum Support Vector Machines (QSVM) is one of the most promising frameworks in quantum machine learning, yet their performance depends on the design of the feature map. Conventional approaches rely on fixed quantum circuits, whi…
- Decoding Protein Sequences as a 'Quantum Language'… Demonstration of a QNLP-Based Classification Pipelineinquantio · Zenodo (CERN European Organ... · Jul 10, 2026
A study published on bioRxiv demonstrates for the first time the technique of treating protein sequences like sentences in natural language processing (NLP) and parsing them into parameterized quantum circuits using the Quantum Natural Lang…
- Research on the application potential and synergistic effects of quantum computing in the field of artificial intelligence: Integrative analysis centered on quantum machine learning (VQC/QNN)Wen-Chuan Ke2 Kuang-Chung Chen1 · Zenodo (CERN European Organ... · Jul 9, 2026
The rapid development of deep learning and large language models (LMs) has led to a "computing inflation" phenomenon in the demand for computing power, energy consumption, and training time for artificial intelligence (AI). As classical com…
- Research on the application potential and synergistic effects of quantum computing in the field of artificial intelligence: Integrative analysis centered on quantum machine learning (VQC/QNN)Wen-Chuan Ke2 Kuang-Chung Chen1 · Zenodo (CERN European Organ... · Jul 9, 2026
The rapid development of deep learning and large language models (LMs) has led to a "computing inflation" phenomenon in the demand for computing power, energy consumption, and training time for artificial intelligence (AI). As classical com…
- Benchmarking Classical and Quantum Machine Learning for Potency Prediction across Ten Therapeutically Relevant TargetsShiv Garg, Sara Garg · ACS Medicinal Chemistry Let... · Jul 7, 2026
- ProCDNet: prostate cancer detection network using quantum machine learning with enhanced addax optimizationM. R. Prathap, K.S. Vairavel, C. Santhosh Kumar, Abdullah Alwabli · Scientific Reports · Jul 7, 2026
Prostate cancer is one of the most prevalent malignancies worldwide, necessitating the development of advanced and efficient detection models. This research presents the Prostate Cancer Detection Network (ProCDNet), a novel framework that i…
- Exploring the Frontiers of Quantum Machine Learning: State of the Art Challenges and ApplicationsMikel Prieto · OpenAlex · Jul 6, 2026
- Quantum Machine Learning: Bridging Quantum Computing & Machine LearningWei Zhang, Tianming Liu, Yingfeng Wang, Xiang Li et al. · OpenAlex · Jul 4, 2026
Quantum Machine Learning (QML) is an emerging interdisciplinary field at the intersection of quantum computing and machine learning, delivering new era for advancing learning beyond the limitations of conventional computational systems. By …
- Quantum machine learning for object detection and segmentation: A comprehensive survey and future research directionsFalak Niaz, Ida Bagus Krishna Yoga Utama, Yeong Min Jang · ICT Express · Jul 1, 2026
- Multitask learning for earth observation data classification with hybrid quantum networkFan Fan, Yilei Shi, Tobias Guggemos, Xiao Xiang Zhu · Quantum Machine Intelligence · Jun 26, 2026
Abstract Quantum machine learning (QML) has gained increasing attention as a potential framework to address certain data analysis challenges in the future. Earth observation (EO) has entered the era of Big Data, where increasingly sophistic…
- Complexity as Curriculum: Structural Parallels and Disanalogies Across Algorithmic Information Theory, Metaheuristic Optimization, and Machine LearningSaluca Agentic AI Research Team · Zenodo (CERN European Organ... · Jun 21, 2026
This paper examines whether a coherent "complexity-as-curriculum" principle connects three bodies of work: (1) the diversification-to-intensification schedule in metaheuristic algorithms such as PSO and harmony search, (2) information-theor…
- Complexity as Curriculum: Structural Parallels and Disanalogies Across Algorithmic Information Theory, Metaheuristic Optimization, and Machine LearningSaluca Agentic AI Research Team · Zenodo (CERN European Organ... · Jun 21, 2026
This paper examines whether a coherent "complexity-as-curriculum" principle connects three bodies of work: (1) the diversification-to-intensification schedule in metaheuristic algorithms such as PSO and harmony search, (2) information-theor…
- Complexity, Curriculum, and Convergence: Structural Parallels Across Algorithmic Learning SystemsSaluca Agentic AI Research Team · Zenodo (CERN European Organ... · Jun 20, 2026
Modern learning systems—spanning classical metaheuristics, distributed stochastic gradient descent, quantum machine learning, and network complexity theory—independently confront a shared structural tension: how to balance broad exploration…
- Complexity, Curriculum, and Convergence: Shared Structural Tensions in Algorithmic Learning SystemsSaluca Agentic AI Research Team · Zenodo (CERN European Organ... · Jun 20, 2026
Modern learning systems—spanning classical machine learning, quantum machine learning, and metaheuristic optimization—each grapple with a common structural tension: how to balance broad exploration of a solution space against focused exploi…
- Complexity, Curriculum, and Convergence: Structural Parallels Across Algorithmic Learning SystemsSaluca Agentic AI Research Team · Zenodo (CERN European Organ... · Jun 20, 2026
Modern learning systems—spanning classical metaheuristics, distributed stochastic gradient descent, quantum machine learning, and network complexity theory—independently confront a shared structural tension: how to balance broad exploration…