Hybrid Quantum-Classical ML

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8 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Apr 29, 2026cs.CV

HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentation

Semantic segmentation in remote sensing is commonly addressed using classical deep learning architectures such as U-Net, which require a large number of parameters to model complex spatial relationships. Quantum machine learning (QML) provides an alternative representation paradigm by mapping classical features into quantum states, but its direct application to high-dimensional images remains challenging under near-term quantum hardware constraints. In this work, we propose HQ-UNet, a hybrid quantum-classical U-Net architecture that integrates a compact parameterized quantum circuit at the bottleneck of a classical U-Net. The proposed design uses a non-pooling quantum convolutional module to enrich highly compressed encoder features before decoding, while keeping the quantum component shallow and parameter-efficient. Experiments on the LandCover.ai dataset show that HQ-UNet achieves a mean IoU of 0.8050 and an overall accuracy of 94.76%, outperforming the classical U-Net baseline. These results suggest that compact quantum bottlenecks can enhance feature representation for remote sensing image segmentation under near-term quantum constraints. This highlights the potential of hybrid quantum-classical designs as a promising direction for parameter-efficient dense prediction in Earth observation.

Md Aminur Hossain, Ayush V. Patel, Ikshwaku Vanani +1

Space Applications Centre, Indian Space Research Organisation (ISRO), India · New-Brunswick Honors College, Rutgers University, New Jersey · Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay

Remote Sensing Image SegmentationHybrid Quantum-Classical MLQuantum Neural Networks
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Apr 24, 2026cs.CV

On the Complementarity of Quantum and Classical Features: Adaptive Hybrid Quantum-Classical Feature Fusion for Breast Cancer Classification

The integration of quantum machine learning with classical deep learning offers promising avenues for medical image analysis by mapping data into high-dimensional Hilbert spaces. However, effectively unifying these distinct paradigms remains challenging due to common optimization asymmetries. In this paper, a novel hybrid quantum-classical architecture for breast cancer diagnosis based on a dual-branch feature-extraction pipeline is proposed. Our framework extracts and unifies complementary representations from classical models and quantum circuits, exploring both trainable and deterministic (non-trainable) quantum paradigms. To integrate these embeddings, three progressive feature fusion strategies are introduced: Static Hybrid Fusion (SHF) for offline extraction, Dynamic Hybrid Fusion (DHF) for end-to-end co-adaptation, and a novel Temperature-Scaled Hybrid Fusion (TSHF). The TSHF strategy incorporates a learnable scalar, inspired by multimodal learning, that dynamically balances hybrid gradient dynamics and resolves optimization bottlenecks. Empirical validation on the BreastMNIST dataset confirms our hypothesis that unifying diverse feature representations creates a richer data context. The TSHF strategy, specifically when pairing a ResNet backbone with a trainable quantum circuit, achieved a peak accuracy of 87.82%, F1-score of 91.77%, and an AUC-ROC of 89.08%, outperforming purely classical baselines. These results demonstrate that the proposed hybrid framework improves classification accuracy and threshold reliability, providing a stable, high-performance architecture for the clinical deployment of quantum-enhanced diagnostic tools.

Yasmin Rodrigues Sobrinho, João Renato Ribeiro Manesco, João Paulo Papa

Department of Computing, São Paulo State University, Ave. Engenheiro Luiz Edmundo Carrijo Coube, 14-01 - Vargem Limpa, Bauru, 17033-360, São Paulo, Brazil

Hybrid Quantum-Classical MLMedical Image Classification
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Apr 24, 2026quant-ph

A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma

GBM is a highly aggressive primary malignancy in adults, necessitating personalized therapeutic strategies due to its inherent molecular heterogeneity. MGMT promoter methylation is a pivotal prognostic biomarker for anticipating response to temozolomide-based chemotherapy. Although various AI frameworks have been developed for non-invasive MGMT prediction, spatial heterogeneity of methylation status and the high-dimensional and correlated nature of MRI data frequently constrain discriminative feature learning and generalizability of classical models. To circumvent these limitations, a specialized IA-QCNN architecture is proposed, based on the principles of quantum mechanics, including superposition and entanglement, and enabling more efficient representation learning in high-dimensional Hilbert space. The framework establishes a methodological bridge between GBM radiogenomics and quantum deep learning by integrating energy-based slice selection, importance-aware weighting, ring-topology quantum convolution, and folding-based pooling layers. When the model predicts MGMT promoter methylation status using both mpMRI and T1Gd images, experimental results demonstrate that the IA-QCNN achieves high accuracy despite its low number of trainable parameters while effectively minimizing the overfitting problem observed in classical models. Quantitative analyses reveal that the T1Gd modality possesses higher discriminative power than mpMRI, establishing a clinically significant sequence preference. Furthermore, the model exhibits exceptional robustness in hybrid noise environments, effectively utilizing noise as a regularization mechanism to enhance predictive performance. Consequently, the specialized IA-QCNN architecture provides a robust and computationally efficient alternative to classical approaches in the analysis of heterogeneous radiogenomic data.

Emine Akpinar, Murat Oduncuoglu

Department of Physics, Yildiz Technical University, Istanbul, Turkey

Quantum Convolutional Neural NetworksHybrid Quantum-Classical MLClinical PredictionRadiomicsQuantum Neural Networks
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Apr 18, 2026quant-ph

Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach

Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facing limitations in complex thermal pattern classification tasks. This paper presents a novel Hybrid Quantum Neural Network (HQNN) architecture that integrates quantum computing principles with classical convolutional neural networks for enhanced breast cancer classification. Our approach employs parameterized quantum circuits with multi-head attention mechanisms for quantum-aware feature encoding, coupled with classical convolutional layers for comprehensive pattern recognition. The quantum component utilizes a 4qubit variational circuit with strongly entangling layers, while the classical component incorporates advanced attention mechanisms for feature fusion. Experimental validation on breast cancer thermographic data demonstrates substantial performance improvements over state-of-the-art classical architectures, with the quantum-enhanced approach exhibiting superior convergence dynamics and enhanced feature representation capabilities. Our findings provide evidence for quantum advantage in medical image classification through classical simulation, establishing a framework for quantum-classical hybrid systems in healthcare applications. The methodology addresses key challenges in quantum machine learning deployment while maintaining computational feasibility on near-term quantum devices.

Riza Alaudin Syah, Irwan Alnarus Kautsar, Gunawan Witjaksono +1

Faculty of Computing Universiti Teknologi Malaysia Johor Bahru, Malaysia · Informatics Department Faculty of Science and Technology Universitas Muhammadiyah Sidoarjo Sidoarjo, Indonesia · Department of Information System Universitas Siber Jakarta Jakarta, Indonesia

Hybrid Quantum-Classical MLMedical Image ClassificationQuantum Neural Networks
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Apr 17, 2026cs.LG

Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics

Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater to the learning requirements, we envision using a federated learning framework with a quantum-enhanced model. Specifically, we design a hybrid quantum-classical long-shot-term-memory model (QLSTM) for local training at distributed nodes. It combines the representative power of quantum models in understanding complex relationships within the feature space, and an LSTM-based model to learn necessary correlations across data points. Given the computing limitations and unprecedented cost of current stand-alone noisy-intermediate quantum (NISQ) devices, we propose to use a federated learning setup, where the learning load can be distributed to local servers as per design and data availability. We demonstrate the benefits of such a design on a classification task for the Supersymmetry(SUSY) dataset, having 5M rows. Our experiments indicate that the performance of this design is not only better that some of the existing work using variational quantum circuit (VQC) based quantum machine learning (QML) techniques, but is also comparable (Δ∼±1%Δ\sim \pm 1\%) to that of classical deep-learning benchmarks. An important observation from this study is that the designed framework has <<300 parameters and only needs 20K data points to give a comparable performance. Which also turns out to be a 100×\times improvement than the compared baseline models. This shows an improved learning capability of the proposed framework with minimal data and resource requirements, due to the joint model with an LSTM based architecture and a quantum enhanced VQC.

Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1

qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE

Hybrid Quantum-Classical MLHigh-Energy PhysicsQuantum Neural NetworksFederated LearningData-Efficient Learning
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Apr 17, 2026cs.LG

PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs

We present PINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow. The framework enables systematic evaluation of PINN performance across benchmark problems including 1D hyperbolic conservation laws, incompressible flows, and electromagnetic wave propagation. It supports a range of architectural and training enhancements, including Fourier feature embeddings, random weight factorization, strict boundary condition enforcement, adaptive loss balancing, curriculum training, and second-order optimization strategies, with extensibility to additional methods. We provide a comprehensive benchmark study quantifying the impact of these methods on convergence, accuracy, and computational cost, and analyze distributed data parallel scaling in terms of runtime and memory efficiency. In addition, we extend the framework to hybrid quantum-classical PINNs and derive a formal estimate for circuit-evaluation complexity under parameter-shift differentiation. Results highlight the sensitivity of PINNs to architectural and training choices, confirm their high computational cost relative to classical solvers, and identify regimes where hybrid quantum models offer improved parameter efficiency. PINNACLE provides a foundation for benchmarking physics-informed learning methods and guiding future developments through quantitative assessment of their trade-offs.

Shimon Pisnoy, Hemanth Chandravamsi, Ziv Chen +4

†Faculty of Mechanical Engineering, Technion Israel Institute of Technology, Haifa, Israel. · ‡Corresponding author. · §The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering, Technion Israel Institute of Technology, Haifa, Israel. +2

Neural PDE SolversPDE Surrogate ModelingHybrid Quantum-Classical MLDistributed TrainingPhysics-Informed ML
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Mar 10, 2026cs.LG

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data. Recently, quantum computing has emerged as a promising paradigm for solving complex high-dimensional sampling problems. To harness this, we propose a novel hybrid framework combining the temporal representation power of classical neural networks with the distribution-learning capabilities of quantum models. Specifically, we integrate a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM). The LSTM extracts dynamic features, while the QCBM acts as a learnable generative prior modeling complex market distributions to guide forecasting. Evaluated on 5-minute high-frequency data from the SSE Composite and CSI 300 indices, our model significantly outperforms a classical LSTM baseline across MSE, RMSE, and QLIKE metrics. Furthermore, by introducing a stochastic Drop-Prior" mechanism during training, the LSTM implicitly distills structured information from the quantum prior. This establishes a pragmatic paradigm of quantum-assisted training with classical-efficient inference", whereby the model retains its quantum-enhanced accuracy even when the quantum module is entirely disabled during deployment. This demonstrates a practical pathway for leveraging quantum computing to enhance classical models without real-time quantum inference latency.

Yixiong Chen

Quantitative FinanceQuantum Machine LearningHybrid Quantum-Classical MLFinancial Time Series
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Jan 8, 2026cs.CV

Quantum Implicit Neural Representations for Novel View Synthesis

Quantum implicit neural representations have recently emerged as compact function approximators for continuous visual signals. In parallel, Neural Radiance Fields (NeRFs) have become a dominant framework for novel-view synthesis by recovering continuous volumetric scene representations from posed 2D images, but often require substantial model capacity and intensive optimisation. We introduce 3D Quantum Implicit Scene Representation (3D-QISR), the first hybrid quantum-classical radiance-field model for novel-view synthesis from 2D observations. 3D-QISR replaces the classical NeRF backbone with parameterised quantum circuits that encode spatial and view-dependent information through quantum state transformations, enabling compact quantum-enhanced implicit representations while preserving compatibility with standard volumetric rendering. We propose two architectures: Full 3D-QISR, which maximises the representational role of a unified quantum state, and Dual-Branch 3D-QISR, which separates spatial and view-dependent quantum embeddings to introduce a task-aligned inductive bias, reduce state-preparation complexity, and improve scalability and hardware compatibility. Experiments on moderate-resolution novel-view synthesis benchmarks show that 3D-QISR executed on simulated quantum hardware matches or outperforms classical baselines while using fewer than half the trainable parameters. These results position quantum neural networks as compact and competitive building blocks for continuous volumetric scene representation, opening a new direction for quantum-enhanced implicit neural rendering.

Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra +1

Department of Mathematics, Computer Science and Physics, University of Udine, Italy · Department of Biology, University of Naples Federico II, Italy · work done during an internship at the 4DQV research group, MPI for Informatics +1

Hybrid Quantum-Classical MLNovel View SynthesisQuantum Neural NetworksNeural Radiance Fields
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Apr 5, 2025quant-ph

Quantum Adaptive Self-Attention for Quantum Transformer Models

A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it. Our primary contribution is methodological: a protocol for attributing such gains honestly -- a capacity-matched classical bottleneck of identical parameter budget, transparent reporting of where quantum does \emph{not} help, and validation on real quantum hardware -- which we develop and apply through a concrete case study. That case study is Quantum Adaptive Self-Attention (QASA), a hybrid Transformer that replaces the value projection of a \emph{single} encoder layer with a 36-parameter parameterized quantum circuit (PQC), keeping all other layers classical. Across nine synthetic benchmarks and the real-world ETTh1 dataset, QASA improves on a full-capacity classical Transformer for chaotic and trend-dominated signals. To ask whether this is a genuinely \emph{quantum} effect, we introduce a control rarely applied in quantum machine learning -- a capacity-matched classical bottleneck with the same parameter budget -- and find that it matches the PQC on the error metrics. The gain is therefore attributable to the low-rank value-projection \emph{bottleneck} (an \emph{architectural parsimony} principle), not to quantumness; adding further quantum layers only degrades performance and trainability. We accordingly position the quantum layer not as a source of accuracy advantage but as a \emph{competitive} instantiation of this principle: its low-rank compression onto the signal's intrinsic dimensionality is matched by a classical bottleneck, so the gain is architectural rather than quantum.

Chi-Sheng Chen, En-Jui Kuo

Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

Hybrid Quantum-Classical MLTransformer AttentionVariational Quantum Circuits
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Dec 14, 2024quant-ph

Classification of Financial Data Using Quantum Support Vector Machine

Quantum Support Vector Machine is a kernel-based approach to classification problems. We study the applicability of quantum kernels to financial data, specifically our self-curated Dhaka Stock Exchange (DSEx) Broad Index dataset. To the best of our knowledge, this is the first systematic study of quantum kernels applied to this dataset. Working within the empirical quantum advantage (EQA) framework of Krunic et al., we benchmark several quantum kernels against a classical RBF-kernel SVM baseline, propose the best-performing kernel for this dataset, and relate the observations to the Phase Space Terrain Ruggedness Index metric. We estimate the resources needed to carry out these investigations on a larger scale for future practitioners.

Seemanta Bhattacharjee, MD. Muhtasim Fuad, A. K. M. Fakhrul Hossain

Shahjalal University Of Science and Technology, Sylhet-3114, Bangladesh

Quantitative FinanceHybrid Quantum-Classical MLQuantum Kernel MethodsSupport Vector MachinesFinance
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Date pendingquant-ph

Classical and quantum kernel fusion for two-sample testing

Two-sample tests have been extensively employed in various scientific fields and machine learning to discriminate whether two sets of samples come from the same distribution or not. Kernel-based procedures for hypothetical testing have been proposed to efficiently disentangle high-dimensional complex structures in data to obtain accurate results in a model-free way by embedding the data into the reproducing kernel Hilbert space (RKHS). While the choice of kernels plays a crucial role for their performance, little is understood about how to choose kernel especially for small datasets. Here we construct a hypothetical test which can be effective even for small datasets, based on the theoretical foundation of kernel-based tests using maximum mean discrepancy, which is called MMD-FUSE. We enhance the MMD-FUSE framework by incorporating quantum kernels and propose a novel hybrid testing strategy that fuses classical and quantum kernels. This approach creates a powerful and adaptive test by combining the domain-specific inductive biases of classical kernels with the unique expressive power of quantum kernels. We evaluate our method on various synthetic and real-world clinical datasets, and our experiments reveal two key findings: 1) With appropriate hyperparameter tuning, MMD-FUSE with quantum kernels consistently improves test power over classical counterparts, especially for small and high-dimensional data. 2) The proposed hybrid framework demonstrates remarkable robustness, adapting to different data characteristics and achieving high test power across diverse scenarios. These results highlight the potential of quantum-inspired and hybrid kernel strategies to build more effective statistical tests, offering versatile tools for data analysis where sample sizes are limited.

Yu Terada, Yugo Ogio, Ken Arai +2

Advanced Research Laboratory, Sony Group Corporation, 1-7-1 Konan, Minato-ku, Tokyo 108-0075, Japan · Deloitte Tohmatsu Consulting LLC.

Two-Sample TestingMaximum Mean DiscrepancyHybrid Quantum-Classical MLQuantum Kernel MethodsKernel Methods
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Date pendingcs.LG

Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization

Quantum neural networks (QNNs) have attracted growing interest for scientific machine learning, yet in regression settings they often suffer from limited trainability under noisy gradients and ill-conditioned optimization. We propose a hybrid quantum--classical regression framework designed to mitigate these bottlenecks. Our model prepends a lightweight classical embedding that acts as a learnable geometric preconditioner, reshaping the input representation to better condition a downstream variational quantum circuit. Building on this architecture, we introduce a curriculum optimization protocol that progressively increases circuit depth and transitions from SPSA-based stochastic exploration to Adam-based gradient fine-tuning. We evaluate the approach on PDE-informed regression benchmarks and standard regression datasets under a fixed training budget in a simulator setting. Empirically, the proposed framework consistently improves over pure QNN baselines and yields more stable convergence in data-limited regimes. We further observe reduced structured errors that are visually correlated with oscillatory components on several scientific benchmarks, suggesting that geometric preconditioning combined with curriculum training is a practical approach for stabilizing quantum regression.

Qingyu Meng, Yangshuai Wang

Shanghai Jiao Tong University-Chongqing Institute of Artificial Intelligence, Chongqing 401329, China. · Department of Mathematics, National University of Singapore, 10 Lower Kent Ridge Road, Singapore.

Quantum Machine LearningHybrid Quantum-Classical MLCurriculum LearningVariational Quantum CircuitsQuantum Circuit Trainability
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