Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities
DOI:
https://doi.org/10.56919/usci.2324.008Keywords:
quantum computing, quantum machine learning, quantum algorithm, classical algorithm, classificationAbstract
Variational Quantum-Classical Algorithm (VQCA) is a potential tool for machine learning (ML) prediction tasks, but its efficacy, adaptability to big datasets, and optimization for noise reduction on quantum hardware are not clear. We aim to accomplish three study goals in this literature review. We begin by reviewing the justifications for ML practitioners' use of VQCA. Second, we compare the accuracy and effectiveness of VQCA in diverse domains to see whether it has a performance advantage over other ML methods. Finally, we evaluate VQCA's immediate and long-term effects on quantum ML and how well it performs compared to ML techniques for prediction tasks across various applications or domains. Our findings show that VQCA can be significantly more accurate and efficient than conventional algorithms. We also compare traditional ML algorithms with VQCA on various datasets and examine their theoretical guarantees. We equally look into how VQCA might be used practically to address problems in a variety of industries, including banking, healthcare, and energy. In various datasets, we assess the performance and efficacy of VQCA for unsupervised learning tasks. Finally, we go through ways to improve VQCA, particularly for big and complicated problems, to lessen the effect of noise and other sources of error in quantum hardware. Overall, we looked at VQCA’s advantages and disadvantages for ML prediction tasks, including possible directions for future study. Our findings show that VQCA has the potential to completely transform the ML industry, particularly in this emerging era of quantum computing.
References
A. Amogh, S. Dhruv, G. Pratyanush & R. Madhavi (2020) "A Review of Supervised Variational Quantum Classifiers," International Journal of Engineering Research & Technology (IJERT) 9(4), 574-576, 2020. DOI: https://doi.org/10.17577/IJERTV9IS040485
A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, L. Chow, J. Gambetta (2017) Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets. DOI: https://doi.org/10.1038/nature23879
A. Peruzzo, J. McClean, P. Shadbolt, M. H. Yung, X. Q. Zhou, P. J. Love, ... & J. L. O'Brien (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5(1), 1-7. DOI: https://doi.org/10.1038/ncomms5213
A. Ralli, P. Love, A. Tranter & P. Coveney (2021) Implementation of measurement reduction for the variational quantum eigensolver Phys. Rev. Research 3, 033195. DOI: https://doi.org/10.1103/PhysRevResearch.3.033195
A. Rocchetto, E. Grant, S. Strelchuk, G. Carleo & S. Severini (2018). Learning hard quantum distributions with variational autoencoders. Npj Quantum Information. 4(1), 1-7. DOI: https://doi.org/10.1038/s41534-018-0077-z
A.Shaib, M. H. Naim, M. E. Fouda, R. Kanj & F. Kurdahi (2023). Efficient noise mitigation technique for quantum computing. Scientific Reports, 13(1), 1-7. DOI: https://doi.org/10.1038/s41598-023-30510-5
A. Shukla, & P. Vedula (2022). A hybrid classical-quantum algorithm for digital image processing. ArXiv. DOI: https://doi.org/10.1007/s11128-022-03755-8
A. V. Antipov, E. O. Kiktenko & A. K. Fedorov (2023). Realizing a class of stabilizer quantum error correction codes using a single ancilla and circular connectivity Phys. Rev. A 107, 032403. DOI: https://doi.org/10.1103/PhysRevA.107.032403
Adebayo P., Basaky F. & Osaghae E. (2022) Developing a Model for Predicting Lung Cancer Using Variational Quantum-Classical Algorithm: A Survey. Journal of Applied Artificial Intelligence. DOI: https://doi.org/10.48185/jaai.v3i1.446
B. Marcello, L. Erika, S. Stefan, F. Mattia (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology. 4 043001. DOI: https://doi.org/10.1088/2058-9565/ab4eb5
D. Dadkhah, M. Zomorodi, S. E. Hosseini, P. Plawiak & X. Zhou (2022). Reordering and Partitioning of Distributed Quantum Circuits," in IEEE Access, vol. 10, pp. 70329 70341. DOI: https://doi.org/10.1109/ACCESS.2022.3186485
D. Kok, (2021) "Building a quantum kNN classifier with Qiskit: theoretical gains put to practice".
D. Su, R. Israel, K. Sharma, H. Qi, I. Dhand & K. Brádler (2020). Error mitigation on a near term quantum photonic device. ArXiv. DOI: https://doi.org/10.22331/q-2021-05-04-452
E. Farhi & H. Neven (2018). Classification with Quantum Neural Networks on Near Term Processors. ArXiv. /abs/1802.06002
E. Farhi & H. Neven (2018). Classification with quantum neural networks on near term processors. arXiv preprint arXiv:1802.06002.
E. G. Rieffel & D. Venturelli (2019). A case study in programming a quantum annealer for hard operational planning problems. INFORMS Journal on Computing, 31(2), 283 291.
H.-S. Zhong, D.-L. Deng, L.-M. Lu & X.-L. Chen (2020). Quantum principal component analysis with application to large-dimensional data. Physical Review Research, 2(4), 043002.
J. Lin, Y. Zhang, L. Wang, X. Cai & S. Liu (2021). Circuit optimization for quantum variational algorithms. Physical Review Research, 3(1), 013248.
J. R. McClean, J. Romero, R. Babbush & A. Aspuru-Guzik (2016). The theory of variational hybrid quantum-classical algorithms. New Journal of Physics, 18(2), 023023. DOI: https://doi.org/10.1088/1367-2630/18/2/023023
J. S. Otterbach, R. Manenti, N. Alidoust, A. Bestwick, M. Block, B. Bloom, S. Caldwell, N. Didier, E. S. Fried, S. Hong, P. Karalekas, C. B. Osborn, A. Papageorge, E. C. Peterson, G. Prawiroatmodjo, N. Rubin, C. A. Ryan, D. Scarabelli, M. Scheer, C. Rigetti (2017). Unsupervised Machine Learning on a Hybrid Quantum Computer. ArXiv. /abs/1712.05771.
K. Mitarai, M. Negoro, M. Kitagawa & K. Fujii (2018). Quantum circuit learning. Physical Review A, 98(3), 032309. DOI: https://doi.org/10.1103/PhysRevA.98.032309
L. Alessandro (2020) Quantum algorithms for machine learning.
L. Liu, P. Rebentrost & P. Wittek (2020). Machine learning in financial fraud detection: A quantum-inspired approach. Quantum Information Processing, 19(8), 1-15.
M. Benedetti, O. Perdomo & Y. Nam (2019). A generative modeling approach for benchmarking and training shallow quantum circuits. Npj Quantum Information, 5(1), 1-9. DOI: https://doi.org/10.1038/s41534-019-0157-8
M. Cerezo, A. Sone, T. Volkoff & P. J. Coles (2020). Variational quantum optimization of combinatorial problems with a tree tensor network. Quantum Science and Technology, 5(2), 024003.
M. Kashif (2021). Design Space Exploration of Hybrid Quantum-Classical Neural Networks. Electronics, 10(23), 2980. DOI: https://doi.org/10.3390/electronics10232980
M. Schuld, A. Bocharov, K. Svore & N. Wiebe (2018) Circuit-centric quantum classifiers, arXiv:1804.00633v1.
M. Schuld, M. Fingerhuth & F. Petruccione (2018). Implementing a distance-based classifier with a quantum interference circuit. Europhysics Letters, 120(6), 60002. DOI: https://doi.org/10.1209/0295-5075/119/60002
M. Schuld, M. Fingerhuth & F. Petruccione (2020). Implementing a distance-based classifier with a quantum interference circuit. Quantum, 4, 316.
M. Schuld, V. Bergholm & C. Gogolin (2019). Evaluating analytic gradients on quantum hardware. Physical Review A, 99(3), 032331. DOI: https://doi.org/10.1103/PhysRevA.99.032331
M. Wang, L. Song, K. Sun and Z. Jia, (2020) "F-2D-QPCA: A Quaternion Principal Component Analysis Method for Color Face Recognition," in IEEE Access, vol. 8, pp. 217437 217446, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3041847
N. Barraza, G. Barrios, J. Peng, L. Lamata, E. Solano & F. Albarrán-Arriagada (2022) Analog quantum approximate optimization algorithm. Quantum Sci. Technol. 7 045035. DOI: https://doi.org/10.1088/2058-9565/ac91f0
N. Thompson, N. Nguyen, E. Behrman, J. Steck (2020), Experimental pairwise using machine learning techniques applied to primary care data. PLOS sustainability and transformation.
P. Rebentrost, A. Steffens, I. Marvian & S. Lloyd (2018). Quantum singular-value decomposition of nonsparse low-rank matrices. Physical Review A, 97(1), 012327. DOI: https://doi.org/10.1103/PhysRevA.97.012327
R. Stephens (2019) Essential Algorithms, A practical Approach to Computer Algorithms using Python and C#, Indianapolis: John Wiley and Sons, Inc.(Pp 572). DOI: https://doi.org/10.1002/9781119575955
S. Bravyi, D. Gosset & R. König (2020). Quantum advantage with shallow circuits. Science, 370(6523), 1457-1460.
S. Endo, S. C. Benjamin & X. Yuan (2020). Hybrid quantum error correction with a classical decoder. Physical Review Research, 2(4), 043063.
S. Hadfield, Z. Wang, E. G. Rieffel, D. Venturelli & R. Biswas (2019). From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz. Algorithms, 12(2), 34. DOI: https://doi.org/10.3390/a12020034
S. Hadfield, Z. Wang, J. Gorman, E. G. Rieffel, D. Venturelli, & R. Biswas (2019). Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. Nature Communications, 10(1), 1-10.
S. McArdle, S. Endo, A. Aspuru-Guzik, S.C. Benjamin & X. Yuan (2020). Quantum computational chemistry. Reviews of Modern Physics, 92(1), p.015003. DOI: https://doi.org/10.1103/RevModPhys.92.015003
V. Dunjko & H. Briegel (2018) Machine learning & artificial intelligence in the quantum domain: a review of recent progress, Rep. Prog. Phys. 81 074001 DOI: https://doi.org/10.1088/1361-6633/aab406
W. J. Huggins, J. R. McClean, N. C. Rubin, Z. Jiang, N. Wiebe, K. B. Whaley & R. Babbush (2021). Efficient and noise resilient measurements for quantum chemistry on near term quantum computers. Npj Quantum Information, 7(1), 1-9. DOI: https://doi.org/10.1038/s41534-020-00341-7
Y. Cao, J. Romero, J.P. Olson, M. Degroote, P.D. Johnson, M. Kieferová, I.D. Kivlichan, T. Menke, B. Peropadre, N.P.D. Sawaya & S. Sim, (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), pp.10856-10915. DOI: https://doi.org/10.1021/acs.chemrev.8b00803
Y. Tang, M. C. Chen, X. Peng & J. Liu (2021). Theoretical Analysis of Variational Quantum Classical Algorithms for Supervised Learning. arXiv preprint arXiv:2102.07618.
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