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.

Recent papers

• Grow, Compress, and Perturb: A Hybrid Quantum-Classical Pathway to Scalable Quantum Chemistry on Quantum Computers
  Hugo D. Nogueira, Siwar Badreddine, Olivier Adjoua, Diata Traore et al. · arXiv · Oct 29, 2026

  While Variational Quantum Eigensolver (VQE) variants are among leading candidates for electronic structure calculations on quantum computers, gradient-based operator selection limits their scalability. To solve this, we introduce the Hamilt…

• Finite element modelling of subthalamic deep brain stimulation in Parkinson's disease with quantum machine learning optimisation: clinical horizons and revenue projections
  Cartik Sharma · OpenAlex · Oct 26, 2026
• ENVIRONMENTAL ROBOTICS | RESEARCH PREPRINT Massachusetts lakeshore undersea robotics: finite quantum-machine-learning monitoring, NTU reference trajectories and conditional market scenarios
  Cartik Sharma · OpenAlex · Oct 25, 2026
• Quantum Machine Learning in Medical Imaging: A Primer for Radiologists
  Ruben Ortiz Cordero, Gaven K. Smart, Harshada Gothankar, Emilio Supsupin et al. · arXiv · Oct 23, 2026

  Deep learning has transformed radiology, but the computational cost of training and deploying modern models is outpacing what classical hardware can support. Quantum computing has been proposed as one response to this bottleneck: an n-qubit…

• Classical Pre-Training of Large Shallow Parametrized Quantum Circuits through Pauli Propagation and deployment on Quantum Hardware
  Saverio Monaco, Jamal Slim, Ran Xue, Dirk Krücker et al. · arXiv · Oct 23, 2026

  Scaling parametrized quantum circuits to a larger number of qubits is a central challenge in Quantum Machine Learning: gradient estimation via the parameter-shift rule quickly becomes prohibitively expensive, while gradient-free optimizers …

• Topology-Dependent Compilation Asymmetry in Equivariant Quantum Neural Networks: A Compile-Once-and-Relabel Solution
  Hassan Ugail, Newton Howard · Quantum Science and Technology · Oct 23, 2026

  Abstract Equivariant quantum neural networks are designed to preserve symmetries in quantum machine-learning data, but this property is usually verified only before hardware compilation. We show that a transpiler can preserve the expected u…

• Qubits Are Not Enough: Deflation and Measurement Limits in Variational Quantum Modal Analysis
  Bikalpa Gautam, Akil Raj Subedi · arXiv · Oct 22, 2026

  Amplitude encoding stores an N-degree-of-freedom structural mode using ceiling(log2 N) qubits. This article develops an evaluation framework for variational quantum eigensolvers in finite-element structural dynamics. A Cholesky transformati…

• Quantum Fokker-Planck solvers as trainable generative models
  Tariq Mahassen, Marcin Jastrzebski, Oleksandr Kyriienko · arXiv · Oct 22, 2026

  We develop and analyze generative models based on quantum solvers of stochastic differential equations. Unlike the common approach based on layered variational quantum circuits with parameterized rotations, we parameterize the potential of …

• Resource Analysis of Quantum Optimization Algorithms via Variational Methods
  Vitor Dallabona, Lucas Friedrich, Jonas Maziero · arXiv · Oct 21, 2026

  Variational Quantum Algorithms (VQAs) are promising approaches for solving combinatorial optimization problems on near-term quantum devices. However, the role played by quantum resources such as entanglement and coherence during the optimiz…

• Mamba Quantum Eigensolver (MQE): A Selective State Space Model for Scalable Ground State Simulations in Quantum Chemistry
  G. X. A. Petronilo, M. R. Araújo, Erico S. Teixeira, Clebson Cruz · arXiv · Oct 21, 2026

  Predicting molecular electronic structure using Quantum Computing approaches is a challenge in Computational Chemistry. Traditional Variational Quantum Eigensolvers (VQEs) are typically hindered by optimization challenges. The alternative G…

• Dynamical Mean-Field Theory for Real Materials using Variational Quantum Compressed Time Evolution on a Quantum Computer
  Stefan Wolf, Johannes Selisko, Timo Eckstein, Ludwig Nützel et al. · arXiv · Oct 20, 2026

  We design a hybrid quantum-classical computational workflow, combining density functional theory and dynamical mean-field theory, to investigate the electronic properties of CaCuO2, a parent structure of high-temperature cuprate superconduc…

• Minimizing classical resources in variational measurement-based quantum computation for generative modeling
  Arunava Majumder, Hendrik Poulsen Nautrup, Hans J. Briegel · Physical Review A · Oct 20, 2026

  Measurement-based quantum computation (MBQC) is a framework for quantum information processing in which a computational task is carried out through one-qubit measurements on a highly entangled resource state. Due to the indeterminacy of the…

• Supervised Learning with Quantum Hamiltonian Embeddings
  Riccardo Molteni, Minjun Jeon, Paolo Braccia, Abhinav Deshpande et al. · arXiv · Oct 19, 2026

  Machine learning is widely regarded as a promising application in which quantum computing could offer a computational advantage. However, general quantum machine learning models with provable guarantees on performance, trainability, and eff…

• Physics-anchored residual quantum neural networks for cloud-cover parameterization
  Zhuo Wang, Ce Yu, Bin Yang · arXiv · Oct 19, 2026

  Compact quantum neural networks have been explored for cloud-cover parameterization, but their predictive value depends on how physical knowledge is incorporated and which classical models provide the reference. We investigate an offline re…

• Quantum machine learning: A comprehensive survey of models, tools, and challenges
  Taha M. Mahmoud, Marwa R. Hassan, Shenson Joseph, Issam Boukabou et al. · Journal of King Saud Univer... · Oct 19, 2026

  Abstract Quantum Machine Learning (QML) is an emerging interdisciplinary field that merges quantum computing with classical machine learning (ML) to enhance computational efficiency, scalability, and accuracy in data-driven tasks. By levera…

• A Scalable Quantum-Intelligent Blockchain Framework for Secure Body Area Networks: Design of a Deep Quantum Neural Architecture for Privacy-Preserving Healthcare Communications
  Vandana Tukaram Bhalerao, Prof. Dr. Parimala Mani · International Journal of Dr... · Oct 19, 2026

  The development of the application of making use of Wireless Body Area Networks (WBANs) in medically created systems has presented fresh security concerns, in particular with the advent of quantum computing threatening the conventional cryp…

• Task-aligned spectral inductive biases in parameterized quantum circuits
  Felix Frohnert, Adrián Pérez-Salinas, Sebastian Lange, Marcus Schwaebisch et al. · arXiv · Oct 16, 2026

  Parameterized quantum circuits are widely investigated in quantum machine learning, yet how their architecture can provide task-aligned inductive biases remains poorly understood. We study data reuploading quantum circuits with two distinct…

• Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
  Valeria Repetto, Stefano Melchionna, Romina D'Aurizio · arXiv · Oct 16, 2026

  Quantum kernels are among the most promising methods in quantum machine learning, providing a natural way to embed data into high-dimensional Hilbert spaces via quantum circuits and capturing non-linear relationships in data. Their learning…

• ConQUESTA: Topology-Aware Post-Training Structural Pruning of Quantum Neural Networks
  Saim Rehman, Muhammad Shafique · arXiv · Oct 16, 2026

  Deploying quantum neural networks (QNNs) on sparse hardware requires preserving task accuracy while reducing compiler-induced two-qubit gates, SWAPs, and depth. We present ConQUESTA (\underline{C}onstrained \underline{Qu}antum \underline{E}…

• Learning angles for quantum approximate optimization
  Johannes Jakubik, Isabelle Wittmann, Zaheed Gaffoor, Gciniwe Simphiwe Baloyi et al. · arXiv · Oct 16, 2026

  The Quantum Approximate Optimization Algorithm (QAOA) and its variants are leading algorithms to help solve challenging combinatorial optimization problems. QAOA maps these problems to Ising Hamiltonians such that their ground state is the …

• Topological Quantum Machine Learning on a Majorana Zero-Mode Processor for Non-Rigid MRI-to-Ultrasound Surface Registration and Fusion: Finite-Mathematics Formulations and Statistical Verifiability
  Cartik Sharma · OpenAlex · Oct 16, 2026
• Quantum-Enhanced Anomaly Detection for Supply Chain Disruption Risk in Renewable Energy Component Procurement: An Empirical Comparison of Variational Quantum Circuits and Classical Machine Learning Baselines
  Syada Nusrat Nahar, Umme Jahara Simki, Mirza Golam Karim Chowdhury, Md Adnan Chowdhury Ifti et al. · Zenodo (CERN European Organ... · Oct 15, 2026

  Abstract Renewable energy supply chains — dependent on geographically concentrated raw materials such as lithium, cobalt, rare earths, and polysilicon — are increasingly exposed to procurement disruption risk from geopolitical shocks, commo…

• Sampled-Based Guided Quantum Walk: Non-variational quantum algorithm for combinatorial optimization
  Ugo Nzongani, Dylan Laplace Mermoud, Giuseppe Di Molfetta, Andrea Simonetto · Quantum · Oct 13, 2026

  We introduce SamBa-GQW, a novel quantum algorithm for solving binary combinatorial optimization problems of arbitrary degree with no use of any classical optimizer. The algorithm is based on a continuous-time quantum walk on the solution sp…

• The impact of architecture and cost function on dissipative quantum neural networks
  Tobias C. Sutter, Christopher Popp, Beatrix C. Hiesmayr · Quantum Machine Intelligence · Oct 13, 2026

  Abstract Combining machine learning and quantum computation is a potential path towards powerful applications on quantum devices. Among such approaches, quantum neural networks have emerged as a promising framework. In this work, we present…

• An Annealing-based Quantum Neural Network with Discrete Wavelet Transforms for Enhanced Air Pollution Nowcasting
  Otto Menegasso Pires, André Saimon, Daniela Herrmann, Prashant Kumar et al. · arXiv · Oct 9, 2026

  Air pollution poses a significant threat to public health, exacerbated by the rapid industrialization and urbanization of modern society. While traditional deep learning offers predictive capabilities, recent advancements in quantum machine…

• Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion
  Jingyan Xu, Yuanhong Wu, Samuel Yen-Chi Chen, Sean Patten et al. · arXiv · Oct 9, 2026

  Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best design and discard the remaining candidates. This paper addresses whether the structural dive…

• Dynamical Lie Algebras for Fixed-Gate Quantum Ansätze
  Orlane Zang, Daniel Juteau, Tony Quertier, Grégoire Barrué · arXiv · Oct 9, 2026

  Parameterized Quantum Circuits have emerged as a central framework for variational quantum algorithms in the NISQ regime, with major applications in quantum simulation, combinatorial optimization, and, more recently, quantum machine learnin…

• QTN-RQNN: Quantum Tensor Network-Based Recurrent Quantum Neural Network
  Amit S. Patel, Himanshu R. Patel, Bikash K. Behera, Pranav Mehta et al. · arXiv · Oct 9, 2026

  Resource constraints and imperfect quantum operations remain major challenges for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices, particularly for fault-diagnosis tasks involving imbalanced and heterogeneo…

• Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
  Jack Waller, Xing Liang, Dimitrios Makris, Rajagopal Nilavalan · arXiv · Oct 8, 2026

  Quantum machine learning, and quantum neural networks (QNNs) in particular, are advancing fields with growing potential. Although systematic comparisons of QNN configurations have been explored primarily for classification tasks, comparativ…

• Quantum convolutional neural networks for jet images classification
  Hala Elhag, Tobias Hartung, Karl Jansen, Lento Nagano et al. · Quantum Machine Intelligence · Oct 8, 2026

  Abstract Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that …