Abstract
Quantum computing is moving quickly, but it's still challenging to use in real life since there isn't enough quantum gear and software that can be used in large groups. Cloud-based quantum computing has become a promising way to make quantum workloads more accessible and bigger. This research looks at quantum software frameworks that can be scaled up and work in cloud-based quantum computing systems. It looks at their architectural designs, functional capabilities, techniques for integration, and problems with scaling. We look at major frameworks including IBM's Qiskit, Google's Cirq, Microsoft's Q#, and Amazon Braket in terms of how modular they are, how abstract they are, how flexible their programming is, and how well they operate. The paper also talks about middleware and hybrid quantum-classical systems that make it easier for quantum hardware backends and classical infrastructure to work together through cloud platforms.
We talk about how containerisation, microservices, and REST APIs can let quantum services grow to meet the needs of different customers and applications. We also talk about the problems that come up when making software stacks that work on any hardware but are optimised for performance. These problems include noise, qubit decoherence, limited quantum volume, and classical simulation limits. The study points out architectural constraints in present frameworks and suggests ways to make them more scalable, such as AI-driven compiler optimisations, resource-aware schedulers, and hybrid orchestration. Our research shows that current frameworks are a good starting point for development and testing, but to really scale up, they need to work with next-generation cloud infrastructure, edge computing, and real-time quantum error prevention. This work gives a plan for making strong, scalable software systems that can handle the whole lifetime of quantum algorithms, from design and simulation to running them on real quantum hardware in the cloud.
PDF
Reference
[1] Abraham, H., AduOffei, I., Akhalwaya, I. Y., et al. (2022). Qiskit: An Open-source Framework for Quantum Computing. Qiskit Documentation. https://qiskit.org.
[2] Ahle, T., Dell, H., & Wocjan, P. (2023). Scalability limits of quantum simulation frameworks. Quantum Science and Technology, 8(2), 025010. https://doi.org/10.1088/2058-9565/acb145.
[3] Alam, M. S., Rashid, M. A., & Hossain, M. A. (2022). Cloud-based quantum computing: A systematic review. Journal of Cloud Computing, 11(1), 1–20. https://doi.org/10.1186/s13677-022-00296-1.
[4] Amazon Web Services. (2023). Amazon Braket Developer Guide. https://docs.aws.amazon.com/braket.
[5] Arute, F., Arya, K., Babbush, R., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574, 505–510. https://doi.org/10.1038/s41586-019-1666-5.
[6] Babbush, R., McClean, J., & Wiebe, N. (2021). Software abstractions for hybrid quantum-classical workflows. npj Quantum Information, 7(1), 1–11. https://doi.org/10.1038/s41534-021-00436-4.
[7] Ballance, C. J., & Lucas, D. M. (2023). Trapped-ion quantum computing in the cloud era. Nature Physics, 19(4), 327–334.
[8] Barkoutsos, P. K., Gacon, J., & Tavernelli, I. (2020). Quantum algorithms for chemistry in the cloud. Frontiers in Chemistry, 8, 589097. https://doi.org/10.3389/fchem.2020.589097.
[9] Benedetti, M., Lloyd, E., Sack, S., & Fiorentini, M. (2019). Parameterized quantum circuits as machine learning models. Quantum Science and Technology, 4(4), 043001.
[10] Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., & Killoran, N. (2018). PennyLane: Automatic differentiation of hybrid quantum-classical computations. arXiv:1811.04968.
[11] Bharti, K., et al. (2022). Noisy intermediate-scale quantum algorithms. Reviews of Modern Physics, 94(1), 015004.
[12] Blais, A., Grimsmo, A. L., Girvin, S. M., & Wallraff, A. (2021). Circuit quantum electrodynamics. Reviews of Modern Physics, 93(2), 025005.
[13] Chen, J., & Weng, Y. (2022). Quantum cloud computing: Challenges and future research directions. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/TCC.2022.3145672.
[14] Chen, Z., et al. (2023). A survey on quantum programming frameworks. ACM Computing Surveys, 56(1), 1–45.
[15] Cirq Developers. (2023). Cirq Documentation. Google Quantum AI. https://quantumai.google/cirq.
[16] Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D., & Gambetta, J. M. (2019). Validating quantum computers using randomized model circuits. Physical Review A, 100(3), 032328.
[17] Deist, L., & Sanders, Y. R. (2022). Cloud-native quantum computing. Quantum Engineering, 4(2), e70.
[18] Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv:1411.4028.
[19] Fowler, A. G., et al. (2012). Surface codes: Towards practical large-scale quantum computation. Physical Review A, 86(3), 032324.
[20] Gheorghiu, V., & Mosca, M. (2020). Quantum cloud computing security. npj Quantum Information, 6(1), 1–8.
[21] Gidney, C., & Ekerå, M. (2021). How to factor 2048-bit RSA integers in 8 hours using 20 million noisy qubits. Quantum, 5, 433.
[22] Google Quantum AI. (2023). TensorFlow Quantum (TFQ). https://www.tensorflow.org/quantum.
[23] Green, A. S., et al. (2013). Quipper: A scalable quantum programming language. ACM SIGPLAN Notices, 48(6), 333–342.
[24] Häner, T., Steiger, D. S., Svore, K. M., & Troyer, M. (2016). A software methodology for compiling quantum programs. Quantum Science and Technology, 3(2), 020501.
[25] IBM Quantum. (2023). Qiskit Runtime Documentation. https://quantum-computing.ibm.com/.
[26] Ilyas, S., et al. (2023). Hybrid quantum-classical workflows on the cloud: Opportunities and barriers. Future Generation Computer Systems, 147, 207–222.
[27] Johansson, J. R., Nation, P. D., & Nori, F. (2012). QuTiP: An open-source Python framework for simulating quantum systems. Computer Physics Communications, 183(8), 1760–1772.
[28] Jones, T., et al. (2019). Layered quantum computing: A software stack perspective. IEEE Computer, 52(6), 72–81.
[29] Kelly, J., et al. (2018). Physical optimization of quantum circuits for superconducting qubits. Nature, 546, 410–414.
[30] Killoran, N., et al. (2019). Strawberry Fields: A software platform for photonic quantum computing. Quantum, 3, 129.
[31] Krantz, P., et al. (2019). A quantum engineer’s guide to superconducting qubits. Applied Physics Reviews, 6(2), 021318.
[32] Kübler, J. M., et al. (2023). QIR: A quantum intermediate representation for enhanced portability. IEEE Transactions on Quantum Engineering, 4, 1–10.
[33] Lin, C., & Yang, Y. (2021). Scheduling hybrid quantum-classical computations on cloud platforms. ACM Transactions on Quantum Computing, 2(2), 1–23.
[34] McCaskey, A., et al. (2020). XACC: A system-level software infrastructure for hybrid quantum-classical computing. Quantum Science and Technology, 5(3), 034014.
[35] Microsoft. (2023). Azure Quantum Documentation. https://learn.microsoft.com/en-us/azure/quantum/.
[36] Moll, N., et al. (2018). Quantum optimization using variational algorithms on near-term devices. Quantum Science and Technology, 3(3), 030503.
[37] National Institute of Standards and Technology. (2022). Post-Quantum Cryptography Standardization. https://csrc.nist.gov/Projects/post-quantum-cryptography.
[38] Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79.
[39] Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172–185.
[40] Svore, K., & Hastings, M. B. (2021). Scalable software for a quantum future. Communications of the ACM, 64(3), 64–73.