Parallel hybrid quantum-classical machine learning for kernelized time-series classification

Baker, Jack S.; Park, Gilchan; Yu, Kwangmin; Ghukasyan, Ara; Goktas, Oktay; Radha, Santosh Kumar

doi:10.1007/s42484-024-00149-0

Parallel hybrid quantum-classical machine learning for kernelized time-series classification

Journal Article · Sat Mar 09 23:00:00 EST 2024 · Quantum Machine Intelligence

DOI:https://doi.org/10.1007/s42484-024-00149-0· OSTI ID:2336573

Baker, Jack S. ^[1]; Park, Gilchan ^[2]; Yu, Kwangmin ^[2]; Ghukasyan, Ara ^[1]; Goktas, Oktay ^[1]; Radha, Santosh Kumar ^[1]

Agnostiq Inc., Toronto, ON (Canada)
Brookhaven National Laboratory (BNL), Upton, NY (United States)

Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. Here, in this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a timeseries Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multiple kernel learning. Because we treat the kernel weighting step as a differentiable convex optimization problem, our method can be regarded as an end-to-end learnable hybrid quantum-classical-convex neural network, or QCC-net, whose output is a data set-generalized kernel function suitable for use in any kernelized machine learning technique such as the support vector machine (SVM). Using our TSHK as input to a SVM, we classify univariate and multivariate time-series using quantum circuit simulators and demonstrate the efficient parallel deployment of the algorithm to 127-qubit superconducting quantum processors using quantum multi-programming.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: SC0012704; AC05-00OR22725; AC02-05CH11231

OSTI ID:: 2336573

Report Number(s):: BNL--225479-2024-JAAM

Journal Information:: Quantum Machine Intelligence, Journal Name: Quantum Machine Intelligence Journal Issue: 1 Vol. 6; ISSN 2524-4906

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

References (49)

The Elements of Statistical Learning Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome Springer Series in Statistics https://doi.org/10.1007/978-0-387-84858-7	book	January 2009
A Kernel Method for the Optimization of the Margin Distribution Aiolli, Fabio; Da San Martino, Giovanni; Sperduti, Alessandro Artificial Neural Networks - ICANN 2008 https://doi.org/10.1007/978-3-540-87536-9_32	book	January 2008
Support-vector networks Cortes, Corinna; Vapnik, Vladimir Machine Learning, Vol. 20, Issue 3 https://doi.org/10.1007/BF00994018	journal	September 1995
Deep learning for time series classification: a review Ismail Fawaz, Hassan; Forestier, Germain; Weber, Jonathan Data Mining and Knowledge Discovery, Vol. 33, Issue 4 https://doi.org/10.1007/s10618-019-00619-1	journal	March 2019
Conic Optimization via Operator Splitting and Homogeneous Self-Dual Embedding O’Donoghue, Brendan; Chu, Eric; Parikh, Neal Journal of Optimization Theory and Applications, Vol. 169, Issue 3 https://doi.org/10.1007/s10957-016-0892-3	journal	February 2016
Quantum multi-programming for Grover’s search Park, Gilchan; Zhang, Kun; Yu, Kwangmin Quantum Information Processing, Vol. 22, Issue 1 https://doi.org/10.1007/s11128-022-03793-2	journal	January 2023
EasyMKL: a scalable multiple kernel learning algorithm Aiolli, Fabio; Donini, Michele Neurocomputing, Vol. 169 https://doi.org/10.1016/j.neucom.2014.11.078	journal	December 2015
A review on time series forecasting techniques for building energy consumption Deb, Chirag; Zhang, Fan; Yang, Junjing Renewable and Sustainable Energy Reviews, Vol. 74 https://doi.org/10.1016/j.rser.2017.02.085	journal	July 2017
Barren plateaus in quantum neural network training landscapes McClean, Jarrod R.; Boixo, Sergio; Smelyanskiy, Vadim N. Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-07090-4	journal	November 2018
Variational fast forwarding for quantum simulation beyond the coherence time Cîrstoiu, Cristina; Holmes, Zoë; Iosue, Joseph npj Quantum Information, Vol. 6, Issue 1 https://doi.org/10.1038/s41534-020-00302-0	journal	September 2020
Supervised learning with quantum-enhanced feature spaces Havlíček, Vojtěch; Córcoles, Antonio D.; Temme, Kristan Nature, Vol. 567, Issue 7747 https://doi.org/10.1038/s41586-019-0980-2	journal	March 2019
Efficient quantum circuits for diagonal unitaries without ancillas Welch, Jonathan; Greenbaum, Daniel; Mostame, Sarah New Journal of Physics, Vol. 16, Issue 3 https://doi.org/10.1088/1367-2630/16/3/033040	journal	March 2014
Higher order decompositions of ordered operator exponentials Wiebe, Nathan; Berry, Dominic; Høyer, Peter Journal of Physics A: Mathematical and Theoretical, Vol. 43, Issue 6 https://doi.org/10.1088/1751-8113/43/6/065203	journal	January 2010
Deep Learning for Time Series Forecasting: A Survey Torres, José F.; Hadjout, Dalil; Sebaa, Abderrazak Big Data, Vol. 9, Issue 1 https://doi.org/10.1089/big.2020.0159	journal	February 2021
Validating quantum computers using randomized model circuits Cross, Andrew W.; Bishop, Lev S.; Sheldon, Sarah Physical Review A, Vol. 100, Issue 3 https://doi.org/10.1103/PhysRevA.100.032328	journal	September 2019
Circuit-centric quantum classifiers Schuld, Maria; Bocharov, Alex; Svore, Krysta M. Physical Review A, Vol. 101, Issue 3 https://doi.org/10.1103/PhysRevA.101.032308	journal	March 2020
Training quantum embedding kernels on near-term quantum computers Hubregtsen, Thomas; Wierichs, David; Gil-Fuster, Elies Physical Review A, Vol. 106, Issue 4 https://doi.org/10.1103/PhysRevA.106.042431	journal	October 2022
Absence of Barren Plateaus in Quantum Convolutional Neural Networks Pesah, Arthur; Cerezo, M.; Wang, Samson Physical Review X, Vol. 11, Issue 4 https://doi.org/10.1103/PhysRevX.11.041011	journal	October 2021
Noisy intermediate-scale quantum algorithms Bharti, Kishor; Cervera-Lierta, Alba; Kyaw, Thi Ha Reviews of Modern Physics, Vol. 94, Issue 1 https://doi.org/10.1103/RevModPhys.94.015004	journal	February 2022
Quantum circuit learning Mitarai, K.; Negoro, M.; Kitagawa, M. Physical Review A, Vol. 98, Issue 3 https://doi.org/10.1103/physreva.98.032309	journal	September 2018
Evaluating analytic gradients on quantum hardware Schuld, Maria; Bergholm, Ville; Gogolin, Christian Physical Review A, Vol. 99, Issue 3 https://doi.org/10.1103/physreva.99.032331	journal	March 2019
Quantum Machine Learning in Feature Hilbert Spaces Schuld, Maria; Killoran, Nathan Physical Review Letters, Vol. 122, Issue 4 https://doi.org/10.1103/physrevlett.122.040504	journal	February 2019
QuCloud: A New Qubit Mapping Mechanism for Multi-programming Quantum Computing in Cloud Environment Liu, Lei; Dou, Xinglei 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA) https://doi.org/10.1109/HPCA51647.2021.00024	conference	February 2021
Dynamic programming algorithm optimization for spoken word recognition Sakoe, H.; Chiba, S. IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 26, Issue 1 https://doi.org/10.1109/TASSP.1978.1163055	journal	February 1978
Simultaneous Execution of Quantum Circuits on Current and Near-Future NISQ Systems Ohkura, Yasuhiro; Satoh, Takahiko; Van Meter, Rodney IEEE Transactions on Quantum Engineering, Vol. 3 https://doi.org/10.1109/TQE.2022.3164716	journal	January 2022
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines Choi, Kukjin; Yi, Jihun; Park, Changhwa IEEE Access, Vol. 9 https://doi.org/10.1109/access.2021.3107975	journal	January 2021
Generative Adversarial Network for Synthetic Time Series Data Generation in Smart Grids Zhang, Chi; Kuppannagari, Sanmukh R.; Kannan, Rajgopal 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) https://doi.org/10.1109/smartgridcomm.2018.8587464	conference	October 2018
Modern Strategies for Time Series Regression Clark, Stephanie; Hyndman, Rob J.; Pagendam, Dan International Statistical Review, Vol. 88, Issue S1 https://doi.org/10.1111/insr.12432	journal	December 2020
Three Myths about Dynamic Time Warping Data Mining Ratanamahatana, Chotirat Ann; Keogh, Eamonn Proceedings of the 2005 SIAM International Conference on Data Mining https://doi.org/10.1137/1.9781611972757.50	conference	April 2005
Optimization Methods for Large-Scale Machine Learning Bottou, Léon; Curtis, Frank E.; Nocedal, Jorge SIAM Review, Vol. 60, Issue 2 https://doi.org/10.1137/16m1080173	journal	January 2018
Operator Splitting for a Homogeneous Embedding of the Linear Complementarity Problem O'Donoghue, Brendan SIAM Journal on Optimization, Vol. 31, Issue 3 https://doi.org/10.1137/20M1366307	journal	January 2021
A Case for Multi-Programming Quantum Computers Das, Poulami; Tannu, Swamit S.; Nair, Prashant J. Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture https://doi.org/10.1145/3352460.3358287	conference	October 2019
Software Mitigation of Crosstalk on Noisy Intermediate-Scale Quantum Computers Murali, Prakash; Mckay, David C.; Martonosi, Margaret ASPLOS '20: Architectural Support for Programming Languages and Operating Systems, Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems https://doi.org/10.1145/3373376.3378477	conference	March 2020
A Review on Outlier/Anomaly Detection in Time Series Data Blázquez-García, Ane; Conde, Angel; Mori, Usue ACM Computing Surveys, Vol. 54, Issue 3 https://doi.org/10.1145/3444690	journal	April 2021
A Transformer-based Framework for Multivariate Time Series Representation Learning Zerveas, George; Jayaraman, Srideepika; Patel, Dhaval Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining https://doi.org/10.1145/3447548.3467401	conference	August 2021
Long Short-Term Memory Hochreiter, Sepp; Schmidhuber, Jürgen Neural Computation, Vol. 9, Issue 8 https://doi.org/10.1162/neco.1997.9.8.1735	journal	November 1997
On One-Parameter Unitary Groups in Hilbert Space Stone, M. H. The Annals of Mathematics, Vol. 33, Issue 3 https://doi.org/10.2307/1968538	journal	July 1932
How Parallel Circuit Execution Can Be Useful for NISQ Computing? Niu, Siyuan; Todri-Sanial, Aida 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) https://doi.org/10.23919/DATE54114.2022.9774512	conference	March 2022
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz Hadfield, Stuart; Wang, Zhihui; O'Gorman, Bryan Algorithms, Vol. 12, Issue 2 https://doi.org/10.3390/a12020034	journal	February 2019
PennyLane: Automatic differentiation of hybrid quantum-classical computations Bergholm, Ville; Izaac, Josh; Schuld, Maria arXiv https://doi.org/10.48550/ARXIV.1811.04968	preprint	January 2018
Differentiating Through a Cone Program Agrawal, Akshay; Barratt, Shane; Boyd, Stephen arXiv https://doi.org/10.48550/ARXIV.1904.09043	preprint	January 2019
Mogrifier LSTM Melis, Gábor; Kočiský, Tomáš; Blunsom, Phil arXiv https://doi.org/10.48550/ARXIV.1909.01792	preprint	January 2019
Quantum constraint learning for quantum approximate optimization algorithm Radha, Santosh Kumar arXiv https://doi.org/10.48550/ARXIV.2105.06770	preprint	January 2021
A quantum generative model for multi-dimensional time series using Hamiltonian learning Horowitz, Haim; Rao, Pooja; Radha, Santosh Kumar arXiv https://doi.org/10.48550/ARXIV.2204.06150	preprint	January 2022
Out-of-distribution generalization for learning quantum dynamics Caro, Matthias C.; Huang, Hsin-Yuan; Ezzell, Nicholas arXiv https://doi.org/10.48550/ARXIV.2204.10268	text	January 2022
Dynamical simulation via quantum machine learning with provable generalization Gibbs, Joe; Holmes, Zoë; Caro, Matthias C. arXiv https://doi.org/10.48550/ARXIV.2204.10269	text	January 2022
Quantum Variational Rewinding for Time Series Anomaly Detection Baker, Jack S.; Horowitz, Haim; Radha, Santosh Kumar arXiv https://doi.org/10.48550/ARXIV.2210.16438	preprint	January 2022
Application-Oriented Performance Benchmarks for Quantum Computing Lubinski, Thomas; Johri, Sonika; Varosy, Paul Zenodo https://doi.org/10.5281/zenodo.6972744	dataset	January 2021
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification" Baker, Jack; Park, Gilchan; Yu, Kwangmin Zenodo https://doi.org/10.5281/zenodo.7996534	dataset	January 2023

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