Strawberry Fields: A Software Platform for Photonic Quantum Computing

Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook

Xanadu, 372 Richmond St W, Toronto, M5V 1X6, Canada

We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers, and detail its key features. Built in Python, Strawberry Fields is a full-stack library for design, simulation, optimization, and quantum machine learning of continuous-variable circuits. The platform consists of three main components: (i) an API for quantum programming based on an easy-to-use language named Blackbird; (ii) a suite of three virtual quantum computer backends, built in NumPy and TensorFlow, each targeting specialized uses; and (iii) an engine which can compile Blackbird programs on various backends, including the three built-in simulators, and - in the near future - photonic quantum information processors. The library also contains examples of several paradigmatic algorithms, including teleportation, (Gaussian) boson sampling, instantaneous quantum polynomial, Hamiltonian simulation, and variational quantum circuit optimization.

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[19] Z. Vernon, N. Quesada, M. Liscidini, B. Morrison, M. Menotti, K. Tan, and J. E. Sipe, "Scalable squeezed light source for continuous variable quantum sampling", arXiv:1807.00044.

[20] Miller Eaton, Rajveer Nehra, and Olivier Pfister, "Gottesman-Kitaev-Preskill state preparation by photon catalysis", arXiv:1903.01925.

[21] C. Wetterich, "Quantum computing with classical bits", arXiv:1806.05960.

[22] Colleen M. Farrelly, Srikanth Namuduri, and Uchenna Chukwu, "Quantum Generalized Linear Models", arXiv:1905.00365.

[23] Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, and Ramona Wolf, "Efficient Learning for Deep Quantum Neural Networks", arXiv:1902.10445.

[24] Mark Fingerhuth, Tomáš Babej, and Peter Wittek, "Open source software in quantum computing", PLoS ONE 13 12, e0208561 (2018).

[25] M. Kostylev, A. B. Ustinov, A. V. Drozdovskii, B. A. Kalinikos, and E. Ivanov, "Parametrically squeezed states of microwave magnons in yttrium iron garnet films", arXiv:1811.02104.

[26] Maria Schuld and Nathan Killoran, "Quantum Machine Learning in Feature Hilbert Spaces", Physical Review Letters 122 4, 040504 (2019).

[27] Hao Tang, Yan-Yan Zhu, Jun Gao, Marcus Lee, Peng-Cheng Lai, and Xian-Min Jin, "FeynmanPAQS: A Graphical Interface Program for Photonic Analog Quantum Computing", arXiv:1810.02289.

[28] Logan G. Wright and Peter L. McMahon, "The Capacity of Quantum Neural Networks", arXiv:1908.01364.

[29] Christian Pehle, Karlheinz Meier, Markus Oberthaler, and Christof Wetterich, "Emulating quantum computation with artificial neural networks", arXiv:1810.10335.

[30] Nicolás Quesada and Agata M. Brańczyk, "Gaussian functions are optimal for waveguided nonlinear-quantum-optical processes", Physical Review A 98 4, 043813 (2018).

[31] Patrick Rebentrost, Brajesh Gupt, and Thomas R. Bromley, "Photonic quantum algorithm for Monte Carlo integration", arXiv:1809.02579.

[32] Rosanna Nichols, Lana Mineh, Jesús Rubio, Jonathan C. F. Matthews, and Paul A. Knott, "Designing quantum experiments with a genetic algorithm", arXiv:1812.01032.

The above citations are from Crossref's cited-by service (last updated 2019-09-22 07:29:10) and SAO/NASA ADS (last updated 2019-09-22 07:29:11). The list may be incomplete as not all publishers provide suitable and complete citation data.