References
- Aaronson, S., and L. Chen. 2016. “Complexity-Theoretic Foundations of Quantum Supremacy Experiments.” arXiv:1612.05903. https://doi.org/10.48550/arXiv.1612.05903
- Abohashima, Z., M. Elhosen, E. H. Houssein, and W. M. Mohamed. 2020. “Classification with Quantum Machine Learning: A Survey.” arXiv Preprint arXiv: 2006.12270.
- Altaisky, M. V. 2001. “Quantum neural network.” arXiv. https://doi.org/10.48550/ARXIV.QUANT-PH/0107012.
- Benedetti, M., D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz. 2019. “A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits.” Npj Quantum Information 5 (1). https://doi.org/10.1038/s41534-019-0157-8.
- Benedetti, M., J. Realpe-Gómez, R. Biswas, and A. Perdomo-Ortiz. 2016. “Quantum-Assisted Learning of Graphical Models with Arbitrary Pairwise Connectivity.” arXiv Preprint arXiv: 1609.02542 980.
- Biamonte, J., P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd. 2017. “Quantum machine learning.” Nature 549 (7671): 195–202. https://doi.org/10.1038/nature23474.
- de Haan, P., C. Rainone, M. C. Cheng, and R. Bondesan. 2021. “Scaling Up Machine Learning for Quantum Field Theory with Equivariant Continuous Flows.” arXiv Preprint arXiv: 2110.02673.
- Deshmukh, S., and P. Mulay. January 2021. “Quantum Clustering Drives Innovations: A Bibliometric and Patentometric Analysis.”
- Deutsch, D. 1985. “Quantum Theory, the Church-Turing Principle and the Universal Quantum Computer.” Proceedings of the Royal Society of London A 400: 97–117. https://doi.org/10.1098/rspa.1985.0070.
- Dhawan, S. M., B. M. Gupta, and G. M. N. Mamdapur. 2021. “Quantum machine learning: A scientometric assessment of global publications during 1999–2020.” International Journal of Knowledge Content Development & Technology 11 (3): 29–44. https://doi.org/10.5865/IJKCT.2021.11.3.029.
- Dunjko, V., J. F. Fitzsimons, and H. J. Briegel. 2016. “Quantum-enhanced reinforcement learning.” Physical Review X 6 (2): 021001. https://doi.org/10.1103/PhysRevX.6.021011.
- Emmanoulopoulos, D., and S. Dimoska. 2022. “Quantum Machine Learning in Finance: Time Series Forecasting.” arXiv Preprint arXiv: 2202.00599.
- Farhi, E., J. Goldstone, and S. Gutmann. 2014. “A Quantum Approximate Optimization Algorithm.” arXiv Preprint arXiv: 1411.4028.
- Gilyén, A. 2019. “Quantum Singular Value Transformation & Its Algorithmic Applications.” PhD thesis., University of Amsterdam.
- Havlíček, V., A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta. 2019. “Supervised Learning With Quantum-Enhanced Feature Spaces.” Nature 567 (7747): 209–212. https://doi.org/10.1038/s41586-019-0980-2.
- Ichikawa, T. 2022. “Bibliometric Analysis of Topic Structure in Quantum Computation and Quantum Algorithm Research.” Accessed October 22, 2023. http://arxiv.org/abs/2201.01911.
- Kim, M., D. Venturelli, and K. Jamieson. November, 2020. “Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems.” In Proceedings of the 19th ACM Workshop on Hot Topics in Networks, 110–116. New York, NY: Association for Computing Machinery. https://dl.acm.org/doi/10.1145/3422604.3425924.
- Lloyd, M., and P. R. Mohseni. 2013. “Quantum Algorithms for Supervised and Unsupervised Machine Learning.” arXiv. https://doi.org/10.48550/ARXIV.1307.0411.
- Lloyd, S., M. Mohseni, and P. Rebentrost. 2014. “Quantum Principal Component Analysis.” Nature Physics 10 (9): 631–633. https://doi.org/10.1038/nphys3029.
- Lloyd, S., and C. Weedbrook. 2018. “Quantum Generative Adversarial Learning.” Physical Review Letters 121 (4): 040502. https://doi.org/10.1103/PhysRevLett.121.040502.
- Meinsma, A. L., S. W. Kristensen, W. G. Reijnierse, I. Smeets, and J. Cramer. 2023. “Is Everything Quantum ‘Spooky and weird’? An Exploration of Popular Communication About Quantum Science and Technology in Tedx Talks.” Quantum Science and Technology 8 (3): 035004. https://doi.org/10.1088/2058-9565/acc968.
- Montanaro, A. 2016. “Quantum Algorithms: An Overview.” Quantum Information 2 (1): 15023. https://doi.org/10.1038/npjqi.2015.23.
- Najafi, K., S. F. Yelin, and X. Gao. The Development of Quantum Machine Learning. Harvard Data Science Review 4 (1). January 27. 2022. https://doi.org/10.1162/99608f92.5a9fd72c.
- Nath, R. K., H. Thapliyal, and T. S. Humble. 2021. “A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications.” SN Computer Science 2 (5): 1–11. https://link.springer.com/article/10.1007/s42979-021-00751-0.
- Pande, M., and P. Mulay. 2020. “Bibliometric Survey of Quantum Machine Learning.” Science & Technology Libraries 39 (3): 329–348. https://doi.org/10.1080/0194262X.2020.1776193.
- Preskill, J. 2018. “Quantum computing in the nisq era and beyond.” Quantum 2:79. https://doi.org/10.22331/q-2018-08-06-79.
- Rebentrost, P., P. Gupt, and F. Wilhelm. 2016. “Quantum computational finance: Quantum algorithm for portfolio optimization.” Physical Review A 98 (2): 022321. https://doi.org/10.48550/arXiv.1811.03975.
- Rebentrost, P., and M. Mohseni. 2021. “Quantum Machine Learning and Privacy: Challenges and Opportunities.” ArXiv Preprint.
- Rebentrost, P., M. Mohseni, and S. Lloyd. 2018. “Quantum Computational Finance: Monte Carlo Pricing of Financial Derivatives.” Physical Review Letters 121 (9): 090503. https://doi.org/10.1103/PhysRevLett.121.090503.
- Rebentrost, P., T. R. Bromley, C. Weedbrook, and S. Lloyd. 2018. “A Quantum Hopfield Neural Network.” Physical Review A 98 (4). https://doi.org/10.1103/PhysRevA.98.042308.
- Sarma, S. D., D. L. Deng, and L. M. Duan. 2019. “Machine Learning Meets Quantum Physics.” arXiv Preprint arXiv: 1903.03516. https://doi.org/10.1063/pt.3.4164.
- Schuld, M., M. Fingerhuth, and F. Petruccione. 2017. “Implementing A Distance-Based Classifier with A Quantum Interference Circuit.” Europhysics Letters 119 (6): 60002. https://doi.org/10.1209/0295-5075/119/60002.
- Schuld, M., and N. Killoran. 2019. “Quantum Machine Learning in Feature Hilbert Spaces.” Physical Review Letters 122 (4). https://doi.org/10.1103/physrevlett.122.040504.
- Schuld, M., I. Sinayskiy, and F. Petruccione. 2014a. “The Quest for a Quantum Neural Network.” Quantum Information Processing 13 (11): 2567–2586. https://doi.org/10.1038/s41586-019-0988-2.
- Schuld, M., I. Sinayskiy, and F. Petruccione. 2014b. “Supervised Learning with Quantum Computers.” arXiv Preprint arXiv: 1412.3489.
- Schuld, M., I. Sinayskiy, and F. Petruccione. 2015. “An Introduction to Quantum Machine Learning.” Contemporary Physics 56 (2): 172–185. https://doi.org/10.1080/00107514.2014.964942.
- Shor, P. W. 1994. “Algorithms for Quantum Computation: Discrete Logarithms and Factoring.” IEEE Conference Publication. https://doi.org/10.1109/SFCS.1994.365700.
- Shor, P. W. 1997. “Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer.” SIAM Journal on Computing 26 (5): 1484–1509. https://doi.org/10.1137/S0097539795293172.
- Shukla, A., and P. Vedula. 2023. “A Hybrid Classical-Quantum Algorithm for Solution of Nonlinear Ordinary Differential Equations.” Applied Mathematics and Computation 442:127708. https://doi.org/10.1016/j.amc.2022.127708.
- Smolin, J. A., J. M. Gambetta, and G. Smith. 2012. “Efficient Method for Computing the Maximum-Likelihood Quantum State from Measurements with Additive Gaussian Noise.” Physical Review Letters 108 (7). https://doi.org/10.1103/physrevlett.108.070502.
- S. K. Sood and Monika. 2023. “Bibliometric Analysis and Visualization of Quantum Engineering Technology.” IEEE Transactions on Engineering Management, 1–15. https://doi.org/10.1109/TEM.2023.3313984.
- Stoudenmire, E. M., and D. J. Schwab. 2016. “Supervised Learning with Quantum-Inspired Tensor Networks.” Advances in Neural Information Processing Systems 29:4799–4807. https://doi.org/10.48550/arXiv.1605.05775.
- Temme, K., S. Bravyi, and J. M. Gambetta. 2017. “Error Mitigation for Short-Depth Quantum Circuits.” Physical Review Letters 119 (18): 180509. https://doi.org/10.1103/PhysRevLett.119.180509.
- Wauters, M. M., E. Panizon, G. B. Mbeng, and G. E. Santoro. 2020. “Reinforcement-Learning-Assisted Quantum Optimization.” Physical Review Research 2 (3). https://doi.org/10.1103/PhysRevResearch.2.033446.