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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[1] M. Kostylev, A. B. Ustinov, A. V. Drozdovskii, B. A. Kalinikos, and E. Ivanov, "Towards experimental observation of parametrically squeezed states of microwave magnons in yttrium iron garnet films", Physical Review B 100 2, 020401 (2019).

[2] Andreas Björklund, Brajesh Gupt, and Nicolás Quesada, "A Faster Hafnian Formula for Complex Matrices and Its Benchmarking on a Supercomputer", Journal of Experimental Algorithmics 24 1, 1 (2019).

[3] 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", Physical Review A 100 2, 022341 (2019).

[4] Juan Miguel Arrazola, Timjan Kalajdzievski, Christian Weedbrook, and Seth Lloyd, "Quantum algorithm for nonhomogeneous linear partial differential equations", arXiv:1809.02622, Physical Review A 100 3, 032306 (2019).

[5] Ryan LaRose, "Overview and Comparison of Gate Level Quantum Software Platforms", Quantum 3, 130 (2019).

[6] Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran, "Evaluating analytic gradients on quantum hardware", Physical Review A 99 3, 032331 (2019).

[7] Nicolás Quesada, "Franck-Condon factors by counting perfect matchings of graphs with loops", The Journal of Chemical Physics 150 16, 164113 (2019).

[8] Krishna Kumar Sabapathy, Haoyu Qi, Josh Izaac, and Christian Weedbrook, "Production of photonic universal quantum gates enhanced by machine learning", Physical Review A 100 1, 012326 (2019).

[9] Ömer Eryılmaz and İhsan Yılmaz, "Sürekli Değişken Modele Dayalı Gözetimli Kuantum Makine Öğrenmesi ile Kişilerin Satın Alma Davranışlarının Tespitinin Simulasyonu", Çanakkale Onsekiz Mart Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 1, 140 (2019).

[10] Matthew Amy, Lecture Notes in Computer Science 11497, 87 (2019) ISBN:978-3-030-21499-9.

[11] Jarrod R. McClean, Kevin J. Sung, Ian D. Kivlichan, Yudong Cao, Chengyu Dai, E. Schuyler Fried, Craig Gidney, Brendan Gimby, Pranav Gokhale, Thomas Häner, Tarini Hardikar, Vojtěch Havlíček, Oscar Higgott, Cupjin Huang, Josh Izaac, Zhang Jiang, Xinle Liu, Sam McArdle, Matthew Neeley, Thomas O'Brien, Bryan O'Gorman, Isil Ozfidan, Maxwell D. Radin, Jhonathan Romero, Nicholas Rubin, Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Damian S. Steiger, Mark Steudtner, Qiming Sun, Wei Sun, Daochen Wang, Fang Zhang, and Ryan Babbush, "OpenFermion: The Electronic Structure Package for Quantum Computers", arXiv:1710.07629.

[12] Seth Lloyd and Christian Weedbrook, "Quantum Generative Adversarial Learning", Physical Review Letters 121 4, 040502 (2018).

[13] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Carsten Blank, Keri McKiernan, and Nathan Killoran, "PennyLane: Automatic differentiation of hybrid quantum-classical computations", arXiv:1811.04968.

[14] Daiqin Su, Krishna Kumar Sabapathy, Casey R. Myers, Haoyu Qi, Christian Weedbrook, and Kamil Brádler, "Implementing quantum algorithms on temporal photonic cluster states", Physical Review A 98 3, 032316 (2018).

[15] Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd, "Continuous-variable quantum neural networks", arXiv:1806.06871.

[16] Juan Miguel Arrazola, Thomas R. Bromley, Josh Izaac, Casey R. Myers, Kamil Brádler, and Nathan Killoran, "Machine learning method for state preparation and gate synthesis on photonic quantum computers", arXiv:1807.10781, Quantum Science and Technology 4 2, 024004 (2018).

[17] Patrick Rebentrost, Brajesh Gupt, and Thomas R. Bromley, "Quantum computational finance: Monte Carlo pricing of financial derivatives", Physical Review A 98 2, 022321 (2018).

[18] Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler, and Nathan Killoran, "A Quantum Approximate Optimization Algorithm for continuous problems", arXiv:1902.00409.

[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.