Abstract
Computers are changing swiftly, which has brought us to a new era where the flaws with classical computing are becoming clearer and clearer. Problems are getting so hard that even the most powerful regular supercomputers can't solve them. This is true in fields like cryptography, drug development, materials research, and logistics optimisation. Quantum computing might be a good choice because it can execute a lot of calculations at once using quantum effects like superposition and entanglement. However, modern quantum systems, which are frequently called Noisy Intermediate-Scale Quantum (NISQ) devices, have a lot of issues, like not having enough qubits, having short coherence times, and making a lot of mistakes. Because of these issues, it will be hard to construct fully functional, stand-alone quantum computers in the near future. Hybrid computing models are a smart solution to bridge the gap between traditional and quantum capabilities. These models use both quantum and regular processors in a way that lets them work together, with each part doing the job it is best at. In a typical hybrid system, classical processors are in charge of optimising routines, data preprocessing, and control flow. Quantum processors, on the other hand, handle some computational subroutines that perform better when quantum speedup is used. This integration makes it easy to create and apply valuable quantum algorithms like the Quantum Approximate Optimisation Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). These algorithms need quantum circuits and classical optimisers to operate together again and over again.
This article goes into great detail about how quantum and classical computers can work together to make hybrid computing. It looks at several types of architecture, like co-located and cloud-based systems, and investigates the communication frameworks, software development kits (SDKs), and orchestration layers that make these function. Quantum machine learning, cryptography, and optimisation are some of the most important areas where hybrid systems could be used in the actual world. The report also talks about the issues that come up with latency, making sure software works with other software, scaling, and lowering errors. The hybrid method is more than simply a quick fix; it's a model for how computers will work in the future. It helps developers, scientists, and others in the field test quantum algorithms on current infrastructure as they wait for better quantum systems to come out. As quantum hardware and software ecosystems change, hybrid computing will be a big part of the future of high-performance, quantum-accelerated computing.
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Reference
[1] Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
[2] Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
[3] Bharti, K., et al. (2022). Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics, 94(1),015004.
[4] Schuld, M., & Petruccione, F. (2018). Supervised Learning with Quantum Computers. Springer.
[5] Cerezo, M., et al. (2021). Variational quantum algorithms. Nature Reviews Physics, 3(9), 625–644.
[6] Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press.
[7] Kandala, A., et al. (2017). Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature, 549(7671), 242–246.
[8] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv:1411.4028.
[9] Benedetti, M., et al. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4), 043001.
[10] Havlíček, V., et al. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567(7747), 209–212.
[11] Broughton, M., et al. (2021). TensorFlow Quantum: A software framework for quantum machine learning. arXiv:2003.02989.
[12] Bergholm, V., et al. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv:1811.04968.
[13] McCaskey, A. J., et al. (2020). XACC: A system-level software infrastructure for hybrid quantum-classical computing. Quantum Science and Technology, 5(2), 024002.
[14] IBM Quantum. (2023). Qiskit Documentation. https://qiskit.org
[15] Rigetti Computing. (2023). Forest SDK Documentation. https://www.rigetti.com/forest
[16] Google Quantum AI. (2023). Cirq Documentation. https://quantumai.google/cirq
[17] Peruzzo, A., et al. (2014). A variational eigenvalue solver on a quantum processor. Nature Communications, 5, 4213.
[18] Romero, J., et al. (2017). Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Science and Technology, 2(4), 045001.
[19] LaRose, R. (2019). Overview and comparison of gate level quantum software platforms. Quantum, 3, 130.
[20] Devitt, S. J., et al. (2013). Quantum error correction for beginners. Reports on Progress in Physics, 76(7), 076001.
[21] Shor, P. W. (1995). Scheme for reducing decoherence in quantum computer memory. Physical Review A, 52(4), R2493.
[22] Fowler, A. G., et al. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324.
[23] Gambetta, J. M., et al. (2020). Building IBM’s quantum future. IBM Journal of Research and Development, 64(1/2), 1–13.
[24] Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum algorithm for linear systems of equations. Physical Review Letters, 103(15), 150502.
[25] Zeng, W., et al. (2022). Realization of a hybrid quantum-classical architecture for optimization problems. Nature Communications, 13, 5606.
[26] Zhao, X., et al. (2021). Quantum algorithms for machine learning: A comprehensive survey. ACM Computing Surveys, 54(8), 1–37.
[27] Mohseni, M., et al. (2017). Commercialize quantum technologies in five years. Nature, 543(7644), 171–174.
[28] Cross, A. W., et al. (2019). Validating quantum computers using randomized model circuits. Physical Review A, 100(3), 032328.
[29] Tacchino, F., et al. (2019). An artificial neuron implemented on an actual quantum processor. npj Quantum Information, 5(1), 26.
[30] Gyongyosi, L., & Imre, S. (2019). A survey on quantum computing technology. Computer Science Review, 31, 51–71.
[31] Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864.
[32] Mari, A., Bromley, T. R., & Killoran, N. (2020). Transfer learning in hybrid classical-quantum neural networks. Quantum, 4, 340.
[33] Dasgupta, S., & Dutt, A. (2020). An overview of hybrid quantum-classical algorithms. In Proceedings of the 2020 IEEE International Conference on Quantum Computing and Engineering (QCE).
[34] Huang, H. Y., et al. (2021). Quantum advantage in learning from experiments. Science, 376(6598), 1182–1186.
[35] Qian, Y., et al. (2022). Quantum cloud computing: Opportunities and challenges. IEEE Network, 36(3), 162–169.
[36] Albash, T., & Lidar, D. A. (2018). Adiabatic quantum computation. Reviews of Modern Physics, 90(1), 015002.
[37] Reagor, M., et al. (2018). Demonstration of universal parametric entangling gates on a multi-qubit lattice. Science Advances, 4(2), eaao3603.
[38] Wang, D., et al. (2021). Noise-resilient quantum computing with hybrid quantum-classical workflows. npj Quantum Information, 7(1), 1–10.
[39] Egger, D. J., et al. (2021). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 2, 1–24.
[40] Ladd, T. D., et al. (2010). Quantum computers. Nature, 464(7285), 45–53.