Abstract
In this research work, a quantum regression model (QRM) is proposed by combining an autoencoder and a dressed quantum circuit (DQC) to predict the behavior of fiber optic temperature sensors. As the experimental data gathered during our observations was limited to effectively train the proposed QRM model, we employed an autoencoder to expand the dataset. We examined the regression performance of the QRM by running multiple simulations by varying the quantum hyperparameters such as quantum depth \(\varvec{Q_{depth}}\), number of shots \(\varvec{n_{shots}}\), and the number of qubits \(\varvec{n_{qubits}}\) of the quantum node. Moreover, the regression performance with the unknown data exhibits high R-squared \(\varvec{(r^2)}\) as 0.965, high explained variance \(\varvec{(ExpVar)}\) as 0.969, and small maximum error \(\varvec{(MaxErr)}\) as 0.212 for 4 \(\varvec{Q_{depth}}\), 1500 \(\varvec{n_{shots}}\) and 4 \(\varvec{n_{qubits}}\). Additionally, we proved the superiority performance of the proposed QRM for predicting relative power as 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.
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Availability of data and materials
The dataset used for numerical analysis, and the code implementation used in this proposed research are made available online in GitHub resources for the research community to explore. https://github.com/DrSrideviBala/PCFTemperatureSensorPrediction-QRM.git
References
Arute F, Arya K, Babbush R, Bacon D, Bardin J, Barends R, Biswas R, Boixo S, Brandao F, Buell D, Burkett B, Chen Y, Chen J, Chiaro B, Collins R, Courtney W, Dunsworth A, Farhi E, Foxen B, Fowler A, Gidney CM, Giustina M, Graff R, Guerin K, Habegger S, Harrigan M, Hartmann M, Ho A, Hoffmann MR, Huang T, Humble T, Isakov S, Jeffrey E, Jiang Z, Kafri D, Kechedzhi K, Kelly J, Klimov P, Knysh S, Korotkov A, Kostritsa F, Landhuis D, Lindmark M, Lucero E, Lyakh D, Mandrá S, McClean JR, McEwen M, Megrant A, Mi X, Michielsen K, Mohseni M, Mutus J, Naaman O, Neeley M, Neill C, Niu MY, Ostby E, Petukhov A, Platt J, Quintana C, Rieffel EG, Roushan P, Rubin N, Sank D, Satzinger KJ, Smelyanskiy V, Sung KJ, Trevithick M, Vainsencher A, Villalonga B, White T, Yao ZJ, Yeh P, Zalcman A, Neven H, Martinis J (2019) Quantum supremacy using a programmable superconducting processor. Nature 574:505–510
Asano T, Noda S (2018) Optimization of photonic crystal nanocavities based on deep learning. Opt Express 26(25):32704–32717. https://doi.org/10.1364/OE.26.032704
Bergholm V, Izaac J, Schuld M, Gogolin C, Blank C, McKiernan K, Killoran N (2020) Pennylane: automatic differentiation of hybrid quantum-classical computations. Quantum Phys, 1–15. https://doi.org/10.48550/arXiv.1811.04968
Boixo S, Isakov SV, Smelyanskiy VN, Babbush R, Ding N, Jiang Z, Bremner MJ, Martinis JM, Neven H (2018) Characterizing quantum supremacy in near-term devices. Nat Phys 14(6):595–600. https://doi.org/10.1038/s41567-018-0124-x
Bravyi S, Gosset D, König R, Tomamichel M (2020) Quantum advantage with noisy shallow circuits. Nat Phys 16(10):1040–1045. https://doi.org/10.1038/s41567-020-0948-z
Coyle B, Mills D, Danos V, Kashefi E (2020) The born supremacy: quantum advantage and training of an ising born machine. npj Quantum Inf 6(1). https://doi.org/10.1038/s41534-020-00288-9
Du B, Yang D, She X, Yuan Y, Mao D, Jiang Y, Lu F (2017) Mos2-based all-fiber humidity sensor for monitoring human breath with fast response and recovery. Sens Actuators, B Chem 251:180–184. https://doi.org/10.1016/j.snb.2017.04.193
Dunjko V, Briegel HJ (2017) Machine learning & artificial intelligence in the quantum domain
Farhi E, Neven H (2018) Classification with quantum neural networks on near term processors
Floris I, Adam JM, Calderón PA, Sales S (2021) Fiber optic shape sensors: a comprehensive review. Opt Lasers Eng 139:106508. https://doi.org/10.1016/j.optlaseng.2020.106508
Harrow AW, Hassidim A, Lloyd S (2009) Quantum algorithm for linear systems of equations. Phys Rev Lett 103(15). https://doi.org/10.1103/physrevlett.103.150502
Havlíček V, Córcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209–212. https://doi.org/10.1038/s41586-019-0980-2
Henderson M, Shakya S, Pradhan S, Cook T (2019) Quanvolutional neural networks: powering image recognition with quantum circuits
Karanov B, Chagnon M, Thouin F, Eriksson TA, Bülow H, Lavery D, Bayvel P, Schmalen L (2018) End-to-end deep learning of optical fiber communications. J Lightwave Technol 36(20):4843–4855. https://doi.org/10.1109/JLT.2018.2865109
Kiarashinejad Y, Abdollahramezani S, Zandehshahvar M, Hemmatyar O, Adibi A (2019) Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Adv Theory Simul 2(9):1900088. https://doi.org/10.1002/adts.201900088
Kiarashinejad Y, Zandehshahvar M, Abdollahramezani S, Hemmatyar O, Pourabolghasem R, Adibi A (2020) Knowledge discovery in nanophotonics using geometric deep learning. Adv Intell Syst 2(2):1900132. https://doi.org/10.1002/aisy.201900132
Killoran N, Bromley TR, Arrazola JM, Schuld M, Quesada N, Lloyd S (2019) Continuous-variable quantum neural networks. Phys Rev Res 1(3). https://doi.org/10.1103/physrevresearch.1.033063
Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. Mach Learn, 1–15. https://doi.org/10.48550/arXiv.1412.6980v9
Lloyd S, Mohseni M, Rebentrost P (2014) Quantum principal component analysis. Nat Phys 10(9):631–633. https://doi.org/10.1038/nphys3029
Lundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on mri. Z Med Phys 29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002. Special Issue: Deep Learning in Medical Physics
Ma W, Cheng F, Liu Y (2018) Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12(6):6326–6334. https://doi.org/10.1021/acsnano.8b03569
Mari A, TR TB, Izaac J, Schuld M, Killoran N (2020) Transfer learning in hybrid classical-quantum neural networks. Quantum 4(340):1–13. https://doi.org/10.48550/arXiv.1912.08278
McClean JR, Romero J, Babbush R, Aspuru-Guzik A (2016) The theory of variational hybrid quantum-classical algorithms. New J Phys 18(2):023023. https://doi.org/10.1088/1367-2630/18/2/023023
Mitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. Phys Rev A 98(3). https://doi.org/10.1103/physreva.98.032309
Mohanraj J, Velmurugan V, Sathiyan S, Sivabalan S (2018) All fiber-optic ultra-sensitive temperature sensor using few-layer mos2 coated d-shaped fiber. Opt Commun 406:139–144. https://doi.org/10.1016/j.optcom.2017.06.011
Pondick JV, Woods JM, Xing J, Zhou Y, Cha JJ (2018) Stepwise sulfurization from moo3 to mos2 via chemical vapor deposition. ACS Appl Nano Mater 1(10):5655–5661. https://doi.org/10.1021/acsanm.8b01266
Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79. https://doi.org/10.22331/q-2018-08-06-79
Quoc CN, Ho LB, Tran LN, Nguyen HQ (2022) Qsun: an open-source platform towards practical quantum machine learning applications. Mach Learn: Sci Technol, 1–18. https://doi.org/10.48550/arXiv.2107.10541
Schuld M, Sinayskiy I, Petruccione F (2016) Prediction by linear regression on a quantum computer. Phys Rev A 94:022342
Schuld M, Bocharov A, Svore KM, Wiebe N (2020) Circuit-centric quantum classifiers. Phys Rev A 101(3). https://doi.org/10.1103/physreva.101.032308
Schuld M, Killoran N (2019) Quantum machine learning in feature hilbert spaces. Phys Rev Lett 122(4). https://doi.org/10.1103/physrevlett.122.040504
Schuld M, Sweke R, Meyer JJ (2021) Effect of data encoding on the expressive power of variational quantum-machine-learning models. Phys Rev A 103(3). https://doi.org/10.1103/physreva.103.032430
Silva Ferreira A, Malheiros-Silveira GN, Hernández-Figueroa HE (2018) Computing optical properties of photonic crystals by using multilayer perceptron and extreme learning machine. J Lightwave Technol 36(18):4066–4073. https://doi.org/10.1109/JLT.2018.2856364
Sim S, Johnson PD, Aspuru-Guzik A (2019) Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv Quantum Technol 2(12):1900070. https://doi.org/10.1002/qute.201900070
Simões RDM, Huber P, Meier N, Smailov N, Füchslin RM, Stockinger K (2023) Experimental evaluation of quantum machine learning algorithms. IEEE Access 11:6197–6208. https://doi.org/10.1109/ACCESS.2023.3236409
Sridevi S, Kanimozhi T, Ayyanar N, Chugh S, Valliammai M, Mohanraj J (2022) Deep learning based data augmentation and behavior prediction of photonic crystal fiber temperature sensor. IEEE Sens J 22(7):6832–6839. https://doi.org/10.1109/JSEN.2022.3150240
Wiebe N, Braun D, Lloyd S (2012) Quantum algorithm for data fitting. Phys Rev Lett 109(5). https://doi.org/10.1103/physrevlett.109.050505
Yu F, Liu Q, Gan X, Hu M, Zhang T, Li C, Kang F, Terrones M, Lv R (2017) Ultrasensitive pressure detection of few-layer mos2. Adv Mater 29(4):1603266. https://doi.org/10.1002/adma.201603266
Zelaci A, Yasli A, Kalyoncu C, Ademgil H (2021) Generative adversarial neural networks model of photonic crystal fiber based surface plasmon resonance sensor. J Lightwave Technol 39(5):1515–1522. https://doi.org/10.1109/JLT.2020.3035580
Zhao R, Wang S (2021) A review of quantum neural networks: methods, models, dilemma
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This work was supported through the SEED Fund granted by Veltech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology.
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Authors (Kanimozhi.T and Sridevi.S) have contributed to the development of the proposed QRM model and implemented the same, the authors (Valliammai.M, Mohanraj. J and Vinodkumar. N) came up with the experimental results of DSF sensor, and wrote the manuscript, and the author (Sathasivam. A) reviewed the manuscript.
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Kanimozhi, T., Sridevi, S., Valliammai, M. et al. Behavior prediction of fiber optic temperature sensor based on hybrid classical quantum regression model. Quantum Mach. Intell. 6, 20 (2024). https://doi.org/10.1007/s42484-024-00150-7
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DOI: https://doi.org/10.1007/s42484-024-00150-7