Research and contribution


If you are doing research using Strawberry Fields, please cite our papers:

Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. “Strawberry Fields: A Software Platform for Photonic Quantum Computing”, Quantum, 3, 129 (2019).

Thomas R. Bromley, Juan Miguel Arrazola, Soran Jahangiri, Josh Izaac, Nicolás Quesada, Alain Delgado Gran, Maria Schuld, Jeremy Swinarton, Zeid Zabaneh, and Nathan Killoran. “Applications of Near-Term Photonic Quantum Computers: Software and Algorithms”, arxiv:1912.07634 (2019).

We are always open for collaboration, and can be contacted at


Strawberry Fields is an open-source library, with the up-to-date code available on GitHub:

We welcome contributions - simply fork the Strawberry Fields repository, and then make a pull request containing your contribution. All contributers to Strawberry Fields will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements, and even links to cool projects or applications built on Strawberry Fields.


If you are having issues, please let us know by posting the issue on our Github issue tracker.

To chat directly with the team designing and building Strawberry Fields, as well as members of our community—ranging from professors of quantum physics, to students, to those just interested in being a part of a rapidly growing industry—you can join our discussion forum and Slack channel.

Sometimes, it might take us a couple of hours to reply - please be patient!

External resources

Below are some external web resources that use or highlight Strawberry Fields.

  • Verifying continuous-variable Bell correlations - Peter Wittek

    Explore how Strawberry Fields can be used to understand core concepts in quantum physics, such as the violation of the Bell inequalities.

  • Quantum state learning and gate synthesis - Xanadu

    A collection of scripts to automate the process of quantum state learning and gate synthesis using Strawberry Fields, based on the paper “Machine learning method for state preparation and gate synthesis on photonic quantum computers” (arXiv:1807.10781). Also included are some useful Python functions for generating well-known CV states and gates.

  • Getting Started with Quantum Programming - Tanisha Bassan

    The timeless game of battleships has been updated for the 21st century; Quantum Battleships, powered by Strawberry Fields.

  • The World of Photonic Quantum Computing - Brianna Gopaul

    See how Strawberry Fields can be used to visualize how the elementary CV gate set transforms the vacuum state.

Research papers

Finally, some links to studies and research papers that utilize Strawberry Fields.

  1. D. Su, K. K. Sabapathy, C. R. Myers, H. Qi, C. Weedbrook, and K. Brádler. Implementing quantum algorithms on temporal photonic cluster states. Physical Review A 98, 032316, 2018.

  2. N. Quesada, and A. M. Brańczyk. Gaussian functions are optimal for waveguided nonlinear-quantum-optical processes. Phys. Rev. A 98, 043813, 2018.

  3. N. Killoran, T. R. Bromley, J. M. Arrazola, M. Schuld, N. Quesada, and S. Lloyd. Continuous-variable quantum neural networks. arXiv:1806.06871, 2018.

  4. J. M. Arrazola, T. R. Bromley, J. Izaac, C. R. Myers, K. Brádler, and N. Killoran. Machine learning method for state preparation and gate synthesis on photonic quantum computers. Quantum Science and Technology, 4 024004, 2019.

  5. K. K. Sabapathy, H. Qi, J. Izaac, and C. Weedbrook. Near-deterministic production of universal quantum photonic gates enhanced by machine learning. arXiv:1809.04680, 2018.

  6. M. Fingerhuth, T. Babej, and P. Wittek. Open source software in quantum computing. PloS one, 13(12), e0208561, 2018.

  7. V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, and N. Killoran. Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv:1811.04968, 2018.

  8. N. Quesada. Franck-Condon factors by counting perfect matchings of graphs with loops. Journal of Chemical Physics, 150, 164113, 2019.

  9. M. Eaton, R. Nehra, and O. Pfister. Gottesman-Kitaev-Preskill state preparation by photon catalysis. arXiv:1903.01925, 2019.

  10. P. H. Qiu, X. G. Chen, and Y. W. Shi. Solving Quantum Channel Discrimination Problem With Quantum Networks and Quantum Neural Networks. IEEE Access, 7, 50214-50222, 2019.

  11. C. M. Farrelly, S. Namuduri, U. Chukwu. Quantum Generalized Linear Models. arXiv:1905.00365, 2019.

  12. N. Quesada, L. G. Helt, J. Izaac, J. M. Arrazola, R. Shahrokhshahi, C. R. Myers, and K. K. Sabapathy. Simulating realistic non-Gaussian state preparation. arXiv:1905.07011, 2019.

  13. W. Hu, J. Hu. Training a quantum neural network to solve the contextual multi-armed bandit problem. Natural science, Vol.11 No.1, 2019.

  14. A. Pesah. Learning quantum state properties with quantum and classical neural networks. Masters dissertation, 2019.