In this paper, we made a pioneering effort for the first time to implement Quantum Neural Network regressor model to predict the sensing behavior of Surface plasmon resonance (SPR) sensor and compared the performance of the proposed model with two traditional algorithms namely Support Vector Regressor (SVR) and Artificial Neural Network (ANN) regressor. The proposed trained quantum regressor model is crucial and efficient enough as it could be used to predict the trend of the target value that is confinement loss of the SPR biosensor.
In this paper, we made a pioneering effort for the first time to implement Quantum Neural Network regressor model to predict the sensing behavior of Surface plasmon resonance (SPR) sensor and compared the performance of the proposed model with two traditional algorithms namely Support Vector Regressor (SVR) and Artificial Neural Network (ANN) regressor. The proposed trained quantum regressor model is crucial and efficient enough as it could be used to predict the trend of the target value that is confinement loss of the SPR biosensor.
Abstract A relatively new area of study, quantum machine learning, seeks to bring together the quantum computer’s significant performance improvement and the machine learning algorithms’ capacity to solve real world problems. In this research work,a quantum regression model (QRM) is proposed by combining an autoencoder and dressed quantum circuit (DQC) to predict the behavior of fiber optic temperature sensor. The autoencoder is employed to augment the experimental data-setas it is insufficient to train the DQC model. We examined the regression performance of the QRM by running multiple simulations by varying the quantum hyper-parameters such as quantum depth Qdepth, number of shots nshots andthe number of qubits nqubits of the quantum node. Moreover, the regression performance with the unknown data exhibits high R-squared score (r2scr) as0.965, high explained variance (ExpV arscr) as 0.969 and less maximum error(MaxErrscr) as 0.212 for 4 Qdepth, 1500 nshots and 4 nqubits. On the otherhand, we proved the superiority performance of the proposed QRM in predicting relative power and it is compared with four conventional machine learning regressors,namely artificial neural network (ANN) regressor, support vector regressor(SVR), decision tree (DT) regressor, and random forest (RF) regressor.
Surface plasmon resonance (SPR) sensors can reach the quantum noise limit of the optical readout field in various configurations. We demonstrate that two-mode intensity squeezed states produce a further enhancement in sensitivity compared with a classical optical readout when the quantum noise is used to transduce an SPR sensor signal in the Kretschmann configuration. The quantum noise reduction between the twin beams when incident at an angle away from the plasmonic resonance, combined with quantum noise resulting from quantum anticorrelations when on resonance, results in an effective SPR-mediated modulation that yields a measured sensitivity 5 dB better than that with a classical optical readout in this configuration. The theoretical potential of this technique points to resolving particle concentrations with more accuracy than is possible via classical approaches to optical transduction.
We investigate the use of twin-mode quantum states of light with symmetric statistical features in their photon number for improving intensity-sensitive surface plasmon resonance (SPR) sensors. For this purpose, one of the modes is sent into a prism setup where the Kretschmann configuration is employed as a sensing platform and the analyte to be measured influences the SPR excitation conditions. This influence modifies the output state of light that is subsequently analyzed by an intensity-difference measurement scheme. We show that quantum noise reduction is achieved not only as a result of the sub-Poissonian statistical nature of a single mode, but also as a result of the nonclassical correlation of the photon number between the two modes. When combined with the high sensitivity of the SPR sensor, we show that the use of twin-mode quantum states of light notably enhances the estimation precision of the refractive index of an analyte. With this we are able to identify a clear strategy to further boost the performance of SPR sensors, which are already a mature technology in biochemical and medical sensing applications.
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 layer combinations, twist angles, and stacking patterns presents a significant computing challenge. As the volume of accessible datasets expands, training machine learning models on conventional hardware may ultimately become excessively expensive, prompting the advancement of quantum machine learning (QML) methodologies for materials discovery. This study presents a quantum-enhanced machine learning framework for predicting the bandgaps of van der Waals bilayers utilizing quantum simulators. We start with conventional feature selection to ascertain the most important descriptors affecting electronic properties. The properties are subsequently encoded into quantum states utilizing parameterized quantum circuits. We evaluated three quantum feature maps, namely ZFeatureMap, ZZFeatureMap, and PauliFeatureMap, and observed task-dependent performance. For the classification task, ZFeatureMap with two repetitions gave the strongest quantum-classifier performance, whereas for Quantum Support Vector Regressor (QSVR) bandgap regression, PauliFeatureMap with one repetition achieved the lowest average root-mean-square error (RMSE). These results indicate that feature-map choice should be optimized separately for classification and regression rather than being interpreted as the universal superiority of a single feature map. Using these feature-map comparisons, we employ a QSVR and a variational quantum regressor to predict the bandgaps of 1850 semiconducting vdW bilayers, illustrating that QML may attain significant predictive accuracy despite limited training data. Overall, the results demonstrate the feasibility of a simulator-based QML workflow for materials-property prediction. The quantum classifiers are competitive with the classical support vector classifier (SVC) baseline for zero-gap/non-zero-gap classification, whereas the classical Support Vector Regression (SVR) achieves the lowest RMSE in the regression task. Therefore, the present study should be interpreted as an exploratory benchmark of hybrid classical-quantum models for van der Waals (vdW) bilayers rather than as evidence of a definitive quantum performance advantage.
The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely related to the classical difficulty of choosing suitable neural-network topologies, which is computationally hard. This work investigates automated quantum-circuit construction for regression tasks and introduces a genetic-algorithm framework that discovers Reduced Regressor QNN architectures. The approach explores depth, parametrized gate configurations, and flexible data re-uploading patterns, formulating the construction of quantum regressors as an optimization process. The discovered circuits are evaluated against seventeen classical regression models on twenty-two nonlinear benchmark functions and four analytical functions. Although classical methods often achieve comparable results, they typically require far more parameters, whereas the evolved quantum models remain compact while providing competitive performance. We further analyze dataset complexity using twelve structural descriptors and show, across five increasingly challenging meta-learning scenarios, that these measures can reliably predict which quantum architecture will perform best. The results demonstrate perfect or near-perfect predictive accuracy in several scenarios, indicating that complexity metrics offer powerful and compact representations of dataset structure and can effectively guide automated model selection. Overall, this study provides a principled basis for meta-learning-driven quantum architecture design and advances the understanding of how quantum models behave in regression settings--a topic that has received limited exploration in prior work. These findings pave the way for more systematic and theoretically grounded approaches to quantum regression.
Quantum Support Vector Regression (QSVR) has emerged as one of the most promising quantum machine learning algorithms for nonlinear regression on Noisy Intermediate-Scale Quantum (NISQ) devices. However, despite numerous simulation-based studies, experimental evaluations on physical quantum hardware remain relatively scarce. This work presents a comparative analysis of three regression approaches: classical Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel, classical SVR utilizing precomputed quantum kernels, and the native QSVR implementation available in the qiskit-machine-learning framework. Experiments were conducted on three benchmark regression datasets using three execution environments: a noiseless Qiskit Aer simulator, the IQM Adonis hardware-aware simulator, and the physical 5-qubit IQM Spark superconducting quantum computer (Odra 5) located at Wrocław University of Science and Technology. Multiple quantum feature maps and circuit depths were investigated, while model hyperparameters were optimized using grid search. The obtained results demonstrate that QSVR can be successfully executed on contemporary superconducting quantum hardware and, for selected datasets, achieves prediction accuracy comparable to or exceeding that of a classical RBF-based SVR. The experiments further reveal the significant influence of quantum hardware noise, feature map selection, and circuit depth on regression quality. Moreover, the implementation based on precomputed quantum kernels generally exhibits greater robustness on physical hardware than the native QSVR implementation. The presented benchmark provides an experimental assessment of QSVR under realistic execution conditions and contributes to the ongoing evaluation of quantum machine learning algorithms on current NISQ-era quantum computers.