Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media, as well as to allow video information to be shared for both marketing, analytics and editorial purposes.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Springer Nature Link
Log in

Comparative Study of Quantum Computing Tools and Frameworks

  • Conference paper
  • First Online:
Save conference paper
View saved research
Innovation and Emerging Trends in Computing and Information Technologies (IETCIT 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2126))

  • 272 Accesses

  • 4 Citations

Abstract

In the last two decades we have seen massive growth in the area of quantum technology. Quantum computers can provide solution to certain classes of problem that classical computers fail to solve. Today quantum computers are used in various fields to solve difficult optimization problems and critical prediction problem in finance, artificial intelligence, and medicine. Hence a greater number of optimized quantum algorithms are being developed. Different quantum computing frameworks help us to program and develop the algorithms on real quantum hardware and simulators. In this paper we are presenting detailed study of different quantum computing tools and programming environments. We demonstrated some basic circuit using these quantum frameworks on real quantum hardware, simulators and discussed different quantum libraries and platform for different task.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+
from ¥17,985 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Feynman, R.P.: Quantum mechanical computers. Opt. News 11(2), 11–20 (1985)

    Article  MATH  Google Scholar 

  2. Shor, P.W.: Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41(2), 303–332 (1999)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  3. Grover, L.K.: A fast quantum mechanical algorithm for database search. In: Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, pp. 212–219 (1996)

    Google Scholar 

  4. Deutsch, D., Jozsa, R.: Rapid solution of problems by quantum computation. Proc. R. Soc. Lond. Ser A Math. Phys. Sci. 439(1907), 553–558 (1992). https://doi.org/10.1098/rspa.1992.0167

  5. Farhi, E., Goldstone, J., Gutmann, S.: A quantum approximate optimization algorithm (2014). http://arxiv.org/abs/1411.4028

  6. Raymer, M.G., Monroe, C.: Quantum Sci. Technol. 4, 020504 (2019)

    Google Scholar 

  7. Nielsen, M.A., Chuang, I.L.: Quantum Computation and Quantum Information. Cambridge University Press, Cambridge (2010). https://doi.org/10.1017/CBO9780511976667

  8. Gambetta, J.: IBM’s roadmap for scaling quantum technology (2020). https://research.ibm.com/blog/ibm-quantum-roadmap

  9. Chow, J., Dial, O., Gambetta, J.: IBM Quantum breaks the 100-qubit processor barrier (2021). https://research.ibm.com/blog/127-qubitquantum-processor-eagle

  10. Google’s new chip is a stepping stone to quantum computing supremacy. MIT technology rev computing. Unsupervised machine learning on Rigetti 19Q with forest 1.2. Medium, 24 April 2018 (2018). https://medium.com/rigetti/unsupervised-machine-learning-on-rigetti-19q-with-forest-1-2-39021339699. Accessed 18 Apr 2020

  11. Arute, F., Arya, K., Babbush, R.: Supplementary information for ‘Quantum supremacy using a programmable superconducting processor.’ Nat. Int. Wkly. J. Sci. 574, 505 (2020)

    Google Scholar 

  12. IBM builds its most powerful universal quantum computing processors. https://phys.org/news/2017-05-ibm-powerfulluniversal-quantumprocessors.html. Accessed 18 Apr 2020

  13. Abraham, H., et al.: Qiskit: an open-source framework for quantum computing (2021). https://doi.org/10.5281/zenodo.2573505

  14. IBM Quantum: Qiskit Textbook. IBM (2022). https://qiskit.org/textbook/

  15. Sicilia, M.A., Sánchez-Alonso, S., Mora-Cantallops, M., García-Barriocanal, E.: On the source code structure of quantum code: insights from Q# and QDK. In: Shepperd, M., Brito e Abreu, F., Rodrigues da Silva, A., Pérez-Castillo, R. (eds.) QUATIC 2020. CCIS, vol. 1266, pp. 292–299. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58793-2_24

  16. Quan, J., Li, Q., Liu, C., Shi, J., Peng, Y.: A simplified verifiable blind quantum computing protocol with quantum input verification. Quantum Eng. 3(1), 1–10 (2021). https://doi.org/10.1002/que2.58

  17. Mintz, T.M., McCaskey, A.J., Dumitrescu, E.F., Moore, S.V., Powers, S., Lougovski, P.: QCOR: a language extension specification for the heterogeneous quantum-classical model of computation. ACM J. Emerg. Technol. Comput. Syst. 16(2), 1–17 (2020). https://doi.org/10.1145/3380964

  18. The quantum programming language Q (2023). https://learn.microsoft.com/en-us/azure/quantum/overview-what-is-qsharp-and-qdk

  19. Cirq Developers (2021). Cirq. https://doi.org/10.5281/zenedo.5182845

  20. Broughton, M., et al.: TensorFlow Quantum: A Software Framework for Quantum Machine Learning (2020). https://www.tensorflow.org/quantum

  21. Rigetti: Forest Framework (2023). https://github.com/rigetti/forest-software

  22. Amazon: Amazon Braket Python SDK (2022). https://github.com/aws/amazon-braket-sdk-python

  23. Gonzalez, C.: Cloud based QC with Amazon braket. Digitale Welt 5(2), 14–17 (2021). https://doi.org/10.1007/s42354-021-0330-z

  24. Bergholm, V., et al.: PennyLane: Automatic differentiation of hybrid quantum-classical computations (2020). https://pennylane.ai

  25. Killoran, N., Izaac, J., Quesada, N., Bergholm, V., Amy, M., Weedbrook, C.: Strawberry fields: a software platform for photonic quantum computing. Quantum 3, 129 (2019). https://doi.org/10.22331/q-2019-03-11-129

  26. Broughton, M., et al.: Tensorflow quantum: A software framework for quantum machine learning. arXiv preprint arXiv:2003.02989 (2020)

  27. Johansson, J.R., Nation, P.D., Nori, F.: QuTiP 2: a Python framework for the dynamics of open quantum systems. Comput. Phys. Commun. 184, 1234 (2013)

    Article  ADS  CAS  MATH  Google Scholar 

  28. Steiger, D.S., Häner, T., Troyer, M.: ProjectQ: an open-source software frame-work for quantum computing. Quantum 2, 49 (2018). arXiv on 23 Dec 2016

    Google Scholar 

  29. Häner, T., Steiger, D.S., Svore, K.M., Troyer, M.: A software methodology for compiling quantum programs. Quantum Sci. Technol. 3, 020501 (2018) arXiv on 5 Apr 2016

    Google Scholar 

  30. Willsch, D., Willsch, M., Gonzalez Calaza, C.D., Jin, F., De Raedt, H., Svensson, M., Michielsen, K.: Benchmarking advantage and D-wave 2000Q quantum annealers with exact cover problems. Quantum Inf. Process. 21(4), 141 (2022)

    Google Scholar 

  31. Hassan, M.W., Pakin, S., Feng, W.: C to D-wave: a high-level C compilation framework for quantum annealers. In: 2019 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–8. IEEE (2019)

    Google Scholar 

  32. Fingerhuth, M., Babej, T., Wittek, P.: Open source software in quantum computing. PLoS ONE (2018)

    Google Scholar 

  33. Cross, A., Bishop, L., Smolin, J., Gambetta, J.: Open quantum assembly language, June 2017. http://arxiv.org/abs/1707.03429

  34. Fu, X., et al.: Quingo: a programming framework for heterogeneous quantum-classical computing with NISQ features. ACM Trans. Quantum Comput. 2(4), 1–37 (2021). https://doi.org/10.1145/3483528

  35. Hevia, J.L., Peterssen, G., Piattini, M.: QuantumPath: a quantum software development platform. Softw. Pract. Exp. 2021, 1–14 (2021). https://doi.org/10.1002/spe.3064

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia University, New Delhi, India

    Muhammad Hamid & Bashir Alam

  2. Department of Computer Science, University of Delhi, New Delhi, India

    Om Pal

Authors
  1. Muhammad Hamid
    View author publications

    Search author on:PubMed Google Scholar

  2. Bashir Alam
    View author publications

    Search author on:PubMed Google Scholar

  3. Om Pal
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Hamid.

Editor information

Editors and Affiliations

  1. Chandigarh University, Mohali, Punjab, India

    Manisha Malhotra

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hamid, M., Alam, B., Pal, O. (2025). Comparative Study of Quantum Computing Tools and Frameworks. In: Malhotra, M. (eds) Innovation and Emerging Trends in Computing and Information Technologies. IETCIT 2024. Communications in Computer and Information Science, vol 2126. Springer, Cham. https://doi.org/10.1007/978-3-031-80842-5_8

Download citation

Keywords

Publish with us

Policies and ethics

Profiles

  1. Muhammad Hamid View author profile