Hybrid Quantum-Classical ML

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Overview

Hybrid quantum-classical machine learning studies how quantum processors can work alongside classical models, often to provide useful feature transformations or compact computational modules. Common approaches include variational quantum circuits as layers or prediction heads in neural networks, quantum kernels, quantum reservoirs, and annealing-based optimization, applied to tasks from image and time-series analysis to physics-informed modeling. The potential value is in representing complex relationships or reducing model size while using classical systems for data handling and computation. Results span small benchmarks and some real-device tests, but whether quantum components offer gains beyond matched classical controls remains an open question, alongside hardware noise, training stability, and execution cost.

Latest papers

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179 papers15 papers in the last four weeks, down 17% on the four weeks before. 0.1% of all new papers.

Mar 16, 2026

Photonic Quantum-Enhanced Knowledge Distillation

Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during machine-learning training. Here we introduce Photonic Quantum-Enhanced Knowledge Distillation (PQKD), a hybrid quantum photonic--classical framework in which a programmable photonic circuit generates a compact conditioning signal that constrains and guides a parameter-efficient student network during distillation from a high-capacity teacher. PQKD replaces fully trainable convolutional kernels with dictionary convolutions: each layer learns only a small set of shared spatial basis filters, while sample-dependent channel-mixing weights are derived from shot-limited photonic features and mapped through a fixed linear transform. Training alternates between standard gradient-based optimisation of the student and sampling-robust, gradient-free updates of photonic parameters, avoiding differentiation through photonic hardware. Across MNIST, Fashion-MNIST and CIFAR-10, PQKD traces a controllable compression--accuracy frontier, remaining close to teacher performance on simpler benchmarks under aggressive convolutional compression. Performance degrades predictably with finite sampling, consistent with shot-noise scaling, and exponential moving-average feature smoothing suppresses high-frequency shot-noise fluctuations, extending the practical operating regime at moderate shot budgets.

Kuan-Cheng Chen, Shang Yu, Chen-Yu Liu +10

Department of Electrical and Electronic Engineering, Imperial College London, South Kensington, London, SW7 2AZ, England, United Kingdom. · Imperial Centre for Quantum Engineering, Science and Technology, Imperial College London, South Kensington, London, SW7 2AZ, England, United Kingdom. · JIJ, Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, United Kingdom. +9

Knowledge DistillationQuantum Machine LearningHybrid Quantum-Classical MLPhotonic Computing
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Mar 14, 2026

Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery

Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.

Soumyajit Maity, Behzad Ghanbarian

iResearchE3 Lab, Department of Earth and Environmental Sciences, University of Texas at Arlington, Arlington, TX, USA

Vision TransformerRemote Sensing Image UnderstandingHybrid Quantum-Classical MLFlood Inundation Mapping
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Mar 13, 2026

Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors

Embedded quantum machine learning (EQML) seeks to bring quantum machine learning (QML) capabilities to resource-constrained edge platforms such as IoT nodes, wearables, drones, and cyber-physical controllers. In 2026, EQML is technically feasible only in limited and highly experimental forms: (i) hybrid workflows where an embedded device performs sensing and classical processing while offloading a narrowly scoped quantum subroutine to a remote quantum processing unit (QPU) or nearby quantum appliance, and (ii) early-stage "embedded QPU" concepts in which a compact quantum co-processor is integrated with classical control hardware. A practical bridge is quantum-inspired machine learning and optimisation on classical embedded processors and FPGAs. This paper analyses feasibility from a circuits-and-systems perspective aligned with the academic community, formalises two implementation pathways, identifies the dominant barriers (latency, data encoding overhead, NISQ noise, tooling mismatch, and energy), and maps them to concrete engineering directions in interface design, control electronics, power management, verification, and security. We also argue that responsible deployment requires adversarial evaluation and governance practices that are increasingly necessary for edge AI systems.

Somdip Dey, Syed Muhammad Raza

School of Engineering and Computing, Regent College London, London, United Kingdom

Quantum Machine LearningHybrid Quantum-Classical MLEdge ML
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Mar 11, 2026

Quantum entanglement provides a competitive advantage in adversarial games

Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question. Competitive zero-sum reinforcement learning is particularly challenging, as success requires modelling dynamic interactions between opposing agents rather than static state-action mappings. Here, we conduct a controlled study isolating the role of quantum entanglement in a quantum-classical hybrid agent trained on Pong, a competitive Markov game. An 8-qubit parameterised quantum circuit serves as a feature extractor within a proximal policy optimisation framework, allowing direct comparison between separable circuits and architectures incorporating fixed (CZ) or trainable (IsingZZ) entangling gates. Entangled circuits consistently outperform separable counterparts with comparable parameter counts and, in low-capacity regimes, match or exceed classical multilayer perceptron baselines. Representation similarity analysis further shows that entangled circuits learn structurally distinct features, consistent with improved modelling of interacting state variables. These findings establish entanglement as a function resource for representation learning in competitive reinforcement learning.

Peiyong Wang, Kieran Hymas, James Quach

Technology, CSIRO Clayton, Research Way, Clayton, VIC 3168, Australia.

Quantum RLQuantum Machine LearningRepresentation Learning for RLQuantum Neural NetworksMulti-Agent Reinforcement LearningHybrid Quantum-Classical ML
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Mar 10, 2026

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 MLQuantum Circuit Born MachinesFinancial Time Series
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Mar 9, 2026

Characterization and upgrade of a quantum graph neural network for charged particle tracking

In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in reconstructing charged particle tracks, motivating frontier research in new technologies. Quantum machine learning models are being investigated as potential new approaches to high energy physics (HEP) tasks. We characterize and upgrade a quantum graph neural network (QGNN) architecture for charged particle track reconstruction on a simulated high luminosity dataset. The model operates on a set of event graphs, each built from the hits generated in tracking detector layers by particles produced in proton collisions, performing a classification of the possible hit connections between adjacent layers. In this approach the QGNN is designed as a hybrid architecture, interleaving classical feedforward networks with parametrized quantum circuits. We characterize the interplay between the classical and quantum components. We report on the principal upgrades to the original design, and present new evidence of improved training behavior, specifically in terms of convergence toward the final trained configuration.

Matteo Argenton, Laura Cappelli, Concezio Bozzi

Department of Physics and Earth Science, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy · INFN Ferrara, Via Saragat 1, Ferrara, 44122, Italy

Quantum Neural NetworksHigh-Energy PhysicsHybrid Quantum-Classical ML
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Mar 9, 2026

Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting

Forecasting physiological signals can support proactive monitoring and timely clinical intervention by anticipating critical changes in patient status. In this work, we address multivariate multi-horizon forecasting of physiological time series by jointly predicting heart rate, oxygen saturation, pulse rate, and respiratory rate at forecasting horizons of 15, 30, and 60 seconds. We propose a hybrid quantum-classical architecture that integrates a Variational Quantum Circuit (VQC) within a recurrent neural backbone. A GRU encoder summarizes the historical observation window into a latent representation, which is then projected into quantum angles used to parameterize the VQC. The quantum layer acts as a learnable non-linear feature mixer, modeling cross-variable interactions before the final prediction stage. We evaluate the proposed approach on the BIDMC PPG and Respiration dataset under a Leave-One-Patient-Out protocol. The results show competitive accuracy compared with classical and deep learning baselines, together with greater robustness to noise and missing inputs. These findings suggest that hybrid quantum layers can provide useful inductive biases for physiological time series forecasting in small-cohort clinical settings. The code is available at https://github.com/arco-group/quantum-ml.

Irene Iele, Floriano Caprio, Paolo Soda +1

Unit of Artificial Intelligence and Computer Systems, Università Campus Bio-Medico di Roma, Italy · Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Sweden · Department of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Italy

Multivariate Time Series ForecastingTime Series ForecastingHybrid Quantum-Classical ML
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Mar 9, 2026

Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction

Accurate prediction of residue-level pKa values is essential for understanding protein function, stability, and reactivity. While existing resources such as DeepKaDB and CpHMD-derived datasets provide valuable training data, their descriptors remain primarily classical and often struggle to generalize across diverse biochemical environments. We introduce a reproducible hybrid quantum-classical framework that enriches residue-level representations with a Gaussian kernel-based quantum-inspired feature mapping. These quantum-enhanced descriptors are combined with normalized structural features to form a unified hybrid encoding processed by a Deep Quantum Neural Network (DQNN). This architecture captures nonlinear relationships in residue microenvironments that are not accessible to classical models. Benchmarking across multiple curated descriptor sets demonstrates that the DQNN achieves improved cross-context generalization relative to classical baselines. External evaluation on the PKAD-R experimental benchmark and an Aββ40 case study further highlights the robustness and transferability of the quantum-inspired representation. By integrating quantum-inspired feature transformations with classical biochemical descriptors, this work establishes a scalable and experimentally transferable approach for residue-level pKa prediction and broader applications in protein electrostatics.

Van Le, Tan Le

the Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA · the School of Engineering, Architecture and Aviation, Hampton University, Hampton, VA 23669, USA

Molecular Property PredictionProtein Representation LearningQuantum-Inspired MLHybrid Quantum-Classical ML
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Mar 4, 2026

Fermi-Dirac thermal measurements: A framework for quantum hypothesis testing and semidefinite optimization

Quantum measurements are the means by which we recover messages encoded into quantum states. They are at the forefront of quantum hypothesis testing, wherein the goal is to perform an optimal measurement for arriving at a correct conclusion. Mathematically, a measurement operator is Hermitian with eigenvalues in [0,1]. By noticing that this constraint on each eigenvalue is the same as that imposed on fermions by the Pauli exclusion principle, we interpret every eigenmode of a measurement operator as an independent effective fermionic mode. Under this perspective, various objective functions in quantum hypothesis testing can be viewed as the total expected energy associated with these fermionic occupation numbers. By instead fixing a temperature and minimizing the total expected fermionic free energy, we find that optimal measurements for these modified objective functions are Fermi-Dirac thermal measurements, wherein their eigenvalues are specified by Fermi-Dirac distributions. In the low-temperature limit, their performance closely approximates that of optimal measurements for quantum hypothesis testing, and we show that their parameters can be learned by classical or hybrid quantum-classical optimization algorithms. This leads to a new quantum machine-learning model, termed Fermi-Dirac machines, consisting of parameterized Fermi-Dirac thermal measurements-an alternative to quantum Boltzmann machines based on thermal states. Beyond hypothesis testing, we show how general semidefinite optimization problems can be solved using this approach, leading to a novel paradigm for semidefinite optimization on quantum computers, in which the goal is to implement thermal measurements rather than prepare thermal states. Finally, we propose quantum algorithms for implementing Fermi-Dirac thermal measurements, and we also propose second-order hybrid quantum-classical optimization algorithms.

Nana Liu, Mark M. Wilde

Institute of Natural Sciences, School of Mathematical Sciences, Ministry of Education Key Laboratory in Scientific and Engineering Computing, and Global College, Shanghai Jiao Tong University, Shanghai 200240, China · School of Electrical and Computer Engineering, Cornell University, Ithaca, New York 14850, USA

Quantum Machine LearningSemidefinite ProgrammingHybrid Quantum-Classical ML
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Mar 3, 2026

Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing

Variational quantum circuits for image classification suffer from barren plateaus, while quantum kernel methods scale quadratically with dataset size. We propose an iterative framework based on Quadratic Unconstrained Binary Optimization (QUBO) for training the classifier head of convolutional neural networks (CNNs) via quantum annealing, entirely avoiding gradient-based circuit optimization. Following the Extreme Learning Machine paradigm, convolutional filters are randomly initialized and frozen, and only the fully connected layer is optimized. At each iteration, a convex quadratic surrogate derived from the feature Gram matrix replaces the non-quadratic cross-entropy loss, yielding an iteration-stable curvature proxy. A per-output decomposition splits the CC-class problem into CC independent QUBOs, each with (d+1)K(d+1)K binary variables, where dd is the feature dimension and KK is the bit precision, so that problem size depends on the image resolution and bit precision, not on the number of training samples. We evaluate the method on six image-classification benchmarks (sklearn digits, MNIST, Fashion-MNIST, CIFAR-10, EMNIST, KMNIST). A precision study shows that accuracy improves monotonically with bit resolution, with 10 bits representing a practical minimum for effective optimization; the 15-bit formulation remains within the qubit and coupler limits of current D-Wave Advantage hardware. The 20-bit formulation matches or exceeds classical stochastic gradient descent on MNIST, Fashion-MNIST, and EMNIST, while remaining competitive on CIFAR-10 and KMNIST. All experiments use simulated annealing, establishing a baseline for direct deployment on quantum annealing hardware.

Mostafa Atallah, Rebekah Herrman

Department of Industrial and Systems Engineering, University of Tennessee - Knoxville, Knoxville, 37996, TN, USA. · Department of Physics, Faculty of Science, Cairo University, Giza, 12613, Egypt.

Image ClassificationHybrid Quantum-Classical MLConvolutional Neural NetworksQuantum AnnealingQuadratic Unconstrained Binary Optimization
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Mar 3, 2026

Neural quantum support vector data description for one-class classification

One-class classification (OCC) is a fundamental problem in machine learning with numerous applications, such as anomaly detection and quality control. With the increasing complexity and dimensionality of modern datasets, there is a growing demand for advanced OCC techniques with better expressivity and efficiency. We introduce Neural Quantum Support Vector Data Description (NQSVDD), a classical-quantum hybrid framework for OCC that performs end-to-end optimized hierarchical representation learning. NQSVDD integrates a classical neural network with trainable quantum data encoding and a variational quantum circuit, enabling the model to learn nonlinear feature transformations tailored to the OCC objective. The hybrid architecture maps input data into an intermediate high-dimensional feature space and subsequently projects it into a compact latent space defined through quantum measurements. Importantly, both the feature embedding and the latent representation are jointly optimized such that normal data form a compact cluster, for which a minimum-volume enclosing hypersphere provides an effective decision boundary. Experimental evaluations on benchmark datasets demonstrate that NQSVDD achieves competitive or superior AUC performance compared to classical Deep SVDD and quantum baselines, while maintaining parameter efficiency and robustness under realistic noise conditions.

Changjae Im, Hyeondo Oh, Daniel K. Park

Department of Statistics and Data Science, Yonsei University, Seoul, Republic of Korea · Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea · Department of Quantum Information, Yonsei University, Seoul, Republic of Korea

One-Class ClassificationHybrid Quantum-Classical ML
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Mar 3, 2026

WTHaar-Net: a Hybrid Quantum-Classical Approach

Convolutional neural networks rely on linear filtering operations that can be reformulated efficiently in suitable transform domains. At the same time, advances in quantum computing have shown that certain structured linear transforms can be implemented with shallow quantum circuits, opening the door to hybrid quantum-classical approaches for enhancing deep learning models. In this work, we introduce WTHaar-Net, a convolutional neural network that replaces the Hadamard Transform used in prior hybrid architectures with the Haar Wavelet Transform (HWT). Unlike the Hadamard Transform, the Haar transform provides spatially localized, multi-resolution representations that align more closely with the inductive biases of vision tasks. We show that the HWT admits a quantum realization using structured Hadamard gates, enabling its decomposition into unitary operations suitable for quantum circuits. Experiments on CIFAR-10 and Tiny-ImageNet demonstrate that WTHaar-Net achieves substantial parameter reduction while maintaining competitive accuracy. On Tiny-ImageNet, our approach outperforms both ResNet and Hadamard-based baselines. We validate the quantum implementation on IBM Quantum cloud hardware, demonstrating compatibility with near-term quantum devices.

Vittorio Palladino, Tsai Idden, Ahmet Enis Cetin

University of Illinois Chicago (UIC), Chicago, IL, USA · Politecnico di Milano, Department of Electronics, Information and Bioengineering, Milano, Italy

Hybrid Quantum-Classical MLConvolutional Neural NetworksImage ClassificationWavelet Methods
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Feb 28, 2026

Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations

DeepONet enables retraining-free inference across varying initial conditions or source terms at the cost of high computational requirements. This paper proposes a hybrid quantum operator network (Quantum AS-DeepOnet) suitable for solving 2D evolution equations. By combining Parameterized Quantum Circuits and cross-subnet attention methods, we can solve 2D evolution equations using only 60% of the trainable parameters while maintaining accuracy and convergence comparable to the classical DeepONet method.

Hongquan Wang, Hanshu Chen, Ilia Marchevsky +1

School of Mathematics, Hohai University, Nanjing, Jiangsu 211100, China · College of Mechanics and Engineering Science, Hohai University, Nanjing, Jiangsu 211100, China · Department of Applied Mathematics, Bauman Moscow State Technical University, Moscow 105005, Russia

PDE Operator LearningDeep Operator NetworksHybrid Quantum-Classical ML
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Feb 27, 2026

Hybrid Quantum Temporal Convolutional Networks

Quantum machine learning models for sequential data face scalability challenges with complex multivariate signals. We introduce the Hybrid Quantum Temporal Convolutional Network (HQTCN), which combines classical temporal windowing with a quantum convolutional neural network core. By applying a shared quantum circuit across temporal windows, HQTCN captures long-range dependencies while achieving significant parameter reduction. Evaluated on synthetic NARMA sequences and high-dimensional EEG time-series, HQTCN performs competitively with classical baselines on univariate data and outperforms all baselines on multivariate tasks. The model demonstrates particular strength under data-limited conditions, maintaining high performance with substantially fewer parameters than conventional approaches. These results establish HQTCN as a parameter-efficient approach for multivariate time-series analysis.

Junghoon Justin Park, Maria Pak, Sebin Lee +4

Seoul National University Seoul, Korea · Wells Fargo New York, USA · Brookhaven National Laboratory New York, USA

Quantum Machine LearningHybrid Quantum-Classical MLQuantum Convolutional Neural Networks
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Feb 26, 2026

Quantum Deep Learning: A Comprehensive Review

Quantum deep learning (QDL) explores the use of both quantum and quantum-inspired resources to determine when deep learning's core capabilities, such as expressivity, generalization, and scalability, can be enhanced based on specific resource constraints. Distinct from broader quantum machine learning, QDL emphasizes compositional depth at the pipeline level and the integration of quantum or quantum-inspired components within end-to-end workflows. This review provides an operational definition of QDL and introduces a taxonomy comprising four primary paradigms: hybrid quantum-classical models, quantum deep neural networks, quantum algorithms for deep learning primitives, and quantum-inspired classical algorithms. Theoretical principles are connected to advanced architectures, software toolchains, and experimental demonstrations across superconducting, trapped-ion, photonic, semiconductor spin, and neutral-atom systems, as well as quantum annealers. Claims of quantum advantage are critically assessed by distinguishing provable complexity-theoretic separations from empirical observations. The analysis characterizes trade-offs between model expressivity, trainability, and classical simulability, while systematically detailing the bottlenecks imposed by optimization landscapes, input-output access models, and hardware constraints. Applications are surveyed in domains encompassing image classification, natural language processing, scientific discovery, quantum data processing, and quantum optimal control, underscoring fair benchmarking against optimized classical counterparts and a comprehensive assessment of resource requirements. This review serves as a tutorial entry point for graduate students while guiding readers to specialized literature. It concludes with a verification-aware roadmap to transition QDL from near-term demonstrations to scalable and fault-tolerant implementations.

Yanjun Ji, Zhao-Yun Chen, Marco Roth +10

Institute for Quantum Computing Analytics (PGI-12), Forschungszentrum Jülich, 52425 Jülich, Germany · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, 230088, P. R. China · Fraunhofer Institute for Manufacturing Engineering and Automation (IPA), Nobelstrasse 12, Stuttgart 70569, Germany +18

Quantum Machine LearningQuantum Neural NetworksHybrid Quantum-Classical MLQuantum-Inspired ML
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Feb 24, 2026

Stochastic Neural Networks for Quantum Devices

This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stochastic artificial neurons are introduced and combined into a quantum neural network. The Kiefer-Wolfowitz algorithm in combination with simulated annealing is used for training the network weights. Several topologies and models are presented, including shallow fully connected networks, Hopfield Networks, Restricted Boltzmann Machines, Autoencoders and convolutional neural networks. We also demonstrate the combination of our optimized neural networks as an oracle for the Grover algorithm to realize a quantum generative AI model.

Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche

Institute for Information Processing (tnt/L3S), Leibniz Universität Hannover, Germany · Institute for Theoretical Physics (ITP/L3S), Leibniz Universität Hannover, Germany

Quantum Neural NetworksHybrid Quantum-Classical MLQuantum Generative Modeling
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Feb 22, 2026

Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection

This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine into the PyTorch ecosystem, addressing classical inefficiencies in Energy-Based Models. The framework facilitates quantum integration in three key aspects: accelerating Boltzmann sampling, optimizing training data via Active Sampling, and constructing hybrid architectures like QBM-VAE and Q-Diffusion. Empirical results on single-cell and OpenWebText datasets demonstrate KPPs ability to achieve SOTA performance, validating a comprehensive quantum-classical paradigm.

Hongdong Zhu, Qi Gao, Yin Ma +6

Beijing QBoson Quantum Technology Co., Ltd. · Chinese Academy of Sciences

Hybrid Quantum-Classical MLQuantum Machine LearningQuantum Generative ModelingEnergy-Based Models
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Feb 20, 2026

Quantum-enhanced satellite image classification

We demonstrate the application of a quantum feature extraction method to enhance multi-class image classification for space applications. By harnessing the dynamics of many-body spin Hamiltonians, the method generates expressive quantum features that, when combined with classical processing, lead to quantum-enhanced classification accuracy. Using a strong and well-established ResNet50 baseline, we achieved a maximum classical accuracy of 83%, which can be improved to 84% with a transfer learning approach. In contrast, applying our quantum-classical method the performance is increased to 87% accuracy, demonstrating a clear and reproducible improvement over robust classical approaches. Implemented on several of IBM's quantum processors, our hybrid quantum-classical approach delivers consistent gains of 2-3% in absolute accuracy. These results highlight the practical potential of current and near-term quantum processors in high-stakes, data-driven domains such as satellite imaging and remote sensing, while suggesting broader applicability in real-world machine learning tasks.

Qi Zhang, Anton Simen, Carlos Flores-Garrigós +7

Kipu Quantum, Greifswalderstrasse 212, 10405 Berlin, Germany · Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain · IDAL, Electronic Engineering Department, ETSE-UV, University of Valencia, Avgda. Universitat s/n, 46100 Burjassot, Valencia, Spain +2

Hybrid Quantum-Classical MLRemote Sensing Image UnderstandingQuantum Feature Maps
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Feb 18, 2026

Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials

Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there tends to be a trade-off between accuracy and inference time. For this reason, we apply multi-objective hyperparameter optimization to both objectives. Additionally, we experiment with modified architectures by constructing variants of Allegro: one extended with additional classical layers and one incorporating quantum-classical hybrid layers. We evaluate all models on QM9, rMD17-aspirin, rMD17-benzene, and a self-generated dataset of copper-lithium structures. As results, both variants surpass Allegro in force prediction accuracy across multiple datasets. The classical variant consistently improves over the baseline, while the quantum-classical hybrid variant achieves the best overall force prediction accuracy on the Cu-Li dataset, where it was fully optimized, outperforming the classical variant by approximately 13%. Notably, the hybrid variant also achieves competitive results on the remaining datasets despite using hyperparameters transferred from Cu-Li without dataset-specific optimization, suggesting that quantum-classical hybridization is a promising direction for enhancing MLIP architectures.

G. Laskaris, D. Morozov, D. Tarpanov +6

Terra Quantum AG, Kornhausstrasse 25, 9000 St. Gallen, Switzerland · LIACS, Leiden University, Netherlands · Nanoscience Center and Department of Chemistry, University of Jyväskylä, P.O. Box 35, 40014 Jyväskylä, Finland +2

Machine Learning Interatomic PotentialsHybrid Quantum-Classical MLEquivariant Neural Networks
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Feb 17, 2026

Edge-Local and Qubit-Efficient Quantum Graph Learning for the NISQ Era

Graph neural networks (GNNs) are a powerful framework for learning representations from graph-structured data, but their direct implementation on near-term quantum hardware remains challenging due to circuit depth, multi-qubit interactions, and qubit scalability constraints. In this work, we introduce a hybrid quantum graph learning architecture designed explicitly for unsupervised learning in the noisy intermediate-scale quantum (NISQ) regime. Our approach combines a variational quantum feature extraction layer with an edge-local and qubit-efficient quantum message-passing mechanism inspired by the Quantum Alternating Operator Ansatz (QAOA) framework. The message-passing operation is decomposed into pairwise interactions along graph edges using standard single- and two-qubit gates. For a graph with NN nodes and nn-qubit feature registers, this reduces the number of qubits required at one time from NnNn to at most 2n2n. We train the model using the Deep Graph Infomax objective to perform unsupervised node representation learning. The external class labels are not used during graph construction or training and are used only to evaluate the learned embeddings. Experiments on the Cora citation network and the Phase 3 release of the 1000 Genomes Project show that the quantum edge interaction contributes to the quality of the learned node representations.

Armin Ahmadkhaniha, Jake Doliskani

Department of Computing and Software, McMaster University

Graph Representation LearningQuantum Neural NetworksHybrid Quantum-Classical MLGraph SSL
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Feb 17, 2026

Molecular Design beyond Training Data with Novel Extended Objective Functionals of Generative AI Models Driven by Quantum Annealing Computer

Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. However, one major issue in molecular generative models is their lower frequency of drug-like compounds. To resolve this problem, we developed a novel framework for optimization of deep generative models integrated with a D-Wave quantum annealing computer, where our Neural Hash Function (NHF) presented herein is used both as the regularization and binarization schemes simultaneously, of which the latter is for transformation between continuous and discrete signals of the classical and quantum neural networks, respectively, in the error evaluation (i.e., objective) function. The compounds generated via the quantum-annealing generative models exhibited higher quality in both validity and drug-likeness than those generated via the fully-classical models, and was further indicated to exceed even the training data in terms of drug-likeness features, without any restraints and conditions to deliberately induce such an optimization. These results indicated an advantage of quantum annealing to aim at a stochastic generator integrated with our novel neural network architectures, for the extended performance of feature space sampling and extraction of characteristic features in drug design.

Hayato Kunugi, Mohsen Rahmani, Yosuke Iyama +8

Inovation to Implementation Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., 1-1 Murasaki-cho, Takatsuki, 569-1125, Japan. · D-Wave Systems Inc., 3033 Beta Ave, Burnaby, V5G 4M9, Canada.

Molecular GenerationQuantum Generative ModelingDrug DiscoveryMolecular OptimizationHybrid Quantum-Classical MLQuantum Annealing
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Jan 17, 2026

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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Jan 8, 2026

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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Jan 8, 2026

The Role of Quantum in Hybrid Quantum-Classical Neural Networks: An Empirical Assessment

Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.

Dominik Freinberger, Philipp Moser

Research Unit Medical Informatics, RISC Software GmbH, Softwarepark 32a, Hagenberg, 4232, Upper Austria, Austria.

Hybrid Quantum-Classical MLQuantum Neural Networks
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Dec 3, 2025

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations

Solving reservoir seepage partial differential equations (PDEs) is critical for hydrocarbon development optimization and production forecasting. Conventional numerical solvers suffer from mesh errors and high computational cost. Standard Physics-Informed Neural Networks (PINNs) are limited by inefficient parameters. They also face weak high-dimensional representation and poor nonlinear fitting ability. We adapt the Discrete Variable (DV)-Circuit Quantum-Classical Physics-Informed Neural Network (QCPINN) to tackle these drawbacks. This work applies the method to four representative reservoir seepage models. The models cover heterogeneous single-phase pressure diffusion. They also include nonlinear Buckley-Leverett two-phase waterflooding. Another case is adsorption-coupled convection-diffusion compositional flow. The last one refers to fully coupled pressure-saturation two-phase flow with exponential permeability heterogeneity. The QCPINN combines classical preprocessing and postprocessing networks with a DV quantum core. It leverages quantum entanglement and superposition to boost high-dimensional feature mapping. Physical constraints are embedded within the framework to guarantee physically consistent solutions. We evaluate three quantum circuit topologies including Cascade, Cross-mesh and Alternate. Numerical tests prove QCPINNs outperform classical PINNs in prediction accuracy, and the circuit topologies show case-specific performance with clear applicable ranges. This work verifies QCPINN's practicability for reservoir engineering. It bridges quantum computing research with oilfield industrial applications.

Xiang Rao, Yina Liu, Yuxuan Shen

School of Petroleum Engineering, Yangtze University, Wuhan 430100, China · College of Future Technology, Yangtze University, Wuhan 430100, China · State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization (Yangtze University), Wuhan 430100, China +2

PDE SolvingPhysics-Informed MLHybrid Quantum-Classical ML
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Nov 26, 2025

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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Apr 5, 2025

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 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 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 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 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 bottleneck (an 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 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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Mar 20, 2025

Reducing QPU Calls in Variational Quantum Algorithms via Classical Offloading of Gradient Computation

Variational quantum algorithms (VQAs) are a promising approach for leveraging near-term quantum computers, but trainability and quantum-resource costs remain major challenges. Here we address these challenges with the Hardware Efficient and dynamical LIe algebra supported Ansatz (HELIA) and two training methods combining the classical-enhanced \g method with the quantum-based Parameter-Shift Rule (PSR). This distributes gradient estimation and training across classical and quantum hardware. We evaluate ground-state estimation of 6- to 18-qubit Hamiltonians using the Variational Quantum Eigensolver (VQE) and quantum phase classification for Hamiltonians up to 12 qubits. For VQE, our methods improve accuracy and success rates, with a mean reduction in estimated logical quantum evaluations of up to 60%60\% relative to Full-PSR training of the same ansatz. For classification, the largest improvement in mean peak test accuracy relative to Full-PSR is 2.82.8 percentage points. We also use these methods as a warm-start pretraining strategy for Hamiltonians with exponential-dimensional dynamical Lie algebras. Gradient diagnostics for the studied Hamiltonian configurations show slower gradient-variance decay than the selected deep hardware-efficient reference over the simulated system sizes, potentially paving the way for training large-qubit quantum models.

Rahul Bhowmick, Harsh Wadhwa, Avinash Singh +3

Quantum Lab, Fujitsu Research of India · Quantum Laboratory, Fujitsu Research, Fujitsu Limited Japan

Variational Quantum AlgorithmsQuantum Circuit TrainabilityHybrid Quantum-Classical ML
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Dec 14, 2024

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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