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

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Abstract

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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[2] 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", Physical Review Applied 12 6, 064024 (2019).

[3] 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).

[4] Tiffany M. Mintz, Alexander J. McCaskey, Eugene F. Dumitrescu, Shirley V. Moore, Sarah Powers, and Pavel Lougovski, "QCOR", ACM Journal on Emerging Technologies in Computing Systems 16 2, 1 (2020).

[5] F. Benatti, S. Mancini, and S. Mangini, "Continuous variable quantum perceptron", International Journal of Quantum Information 17 08, 1941009 (2019).

[6] Juan Carrasquilla, "Machine learning for quantum matter", Advances in Physics: X 5 1, 1797528 (2020).

[7] Miller Eaton, Rajveer Nehra, and Olivier Pfister, "Non-Gaussian and Gottesman–Kitaev–Preskill state preparation by photon catalysis", New Journal of Physics 21 11, 113034 (2019).

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

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

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

[11] Yanyan Feng, Ronghua Shi, Jinjing Shi, Wei Zhao, Yuhu Lu, and Yongze Tang, "Arbitrated quantum signature protocol with boson sampling-based random unitary encryption", Journal of Physics A: Mathematical and Theoretical 53 13, 135301 (2020).

[12] Ilan Tzitrin, J. Eli Bourassa, Nicolas C. Menicucci, and Krishna Kumar Sabapathy, "Progress towards practical qubit computation using approximate Gottesman-Kitaev-Preskill codes", Physical Review A 101 3, 032315 (2020).

[13] Jinjing Shi, Shuhui Chen, Yuhu Lu, Yanyan Feng, Ronghua Shi, Yuguang Yang, and Jian Li, "An Approach to Cryptography Based on Continuous-Variable Quantum Neural Network", Scientific Reports 10 1, 2107 (2020).

[14] Brajesh Gupt, Josh Izaac, and Nicolás Quesada, "The Walrus: a library for the calculation of hafnians, Hermite polynomials and Gaussian boson sampling", Journal of Open Source Software 4 44, 1705 (2019).

[15] 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).

[16] Nathan Killoran, Thomas R. Bromley, Juan Miguel Arrazola, Maria Schuld, Nicolás Quesada, and Seth Lloyd, "Continuous-variable quantum neural networks", Physical Review Research 1 3, 033063 (2019).

[17] 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).

[18] 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", Quantum Science and Technology 5 3, 034010 (2020).

[19] Michel Barbeau and Joaquin Garcia-Alfaro, 2019 IEEE Globecom Workshops (GC Wkshps) 1 (2019) ISBN:978-1-7281-0960-2.

[20] 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).

[21] Rosanna Nichols, Lana Mineh, Jesús Rubio, Jonathan C F Matthews, and Paul A Knott, "Designing quantum experiments with a genetic algorithm", Quantum Science and Technology 4 4, 045012 (2019).

[22] Wei Hu and James Hu, "Distributional Reinforcement Learning with Quantum Neural Networks", Intelligent Control and Automation 10 02, 63 (2019).

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

[24] Haoyu Qi, Daniel J. Brod, Nicolás Quesada, and Raúl García-Patrón, "Regimes of Classical Simulability for Noisy Gaussian Boson Sampling", Physical Review Letters 124 10, 100502 (2020).

[25] Matthew Amy and Vlad Gheorghiu, "staq—A full-stack quantum processing toolkit", Quantum Science and Technology 5 3, 034016 (2020).

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

[27] Jinjing Shi, Shuhui Chen, Jiali Liu, Fangfang Li, Yanyan Feng, and Ronghua Shi, "Quantum Dual Signature with Coherent States Based on Chained Phase-Controlled Operations", Applied Sciences 10 4, 1353 (2020).

[28] 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.

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

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

[31] Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, M. Sohaib Alam, Shahnawaz Ahmed, Juan Miguel Arrazola, Carsten Blank, Alain Delgado, Soran Jahangiri, Keri McKiernan, Johannes Jakob Meyer, Zeyue Niu, Antal Száva, and Nathan Killoran, "PennyLane: Automatic differentiation of hybrid quantum-classical computations", arXiv:1811.04968.

[32] 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 (2019).

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

[34] 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).

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

[36] 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.

[37] C. Wetterich, "Quantum computing with classical bits", Nuclear Physics B 948, 114776 (2019).

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

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

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

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

[42] 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.

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

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

[45] Evandro Chagas Ribeiro da Rosa and Bruno G. Taketani, "QSystem: bitwise representation for quantum circuit simulations", arXiv:2004.03560.

[46] Bhupesh Bishnoi, "Quantum-Computation and Applications", arXiv:2006.02799.

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The above citations are from Crossref's cited-by service (last updated successfully 2020-08-10 20:29:54) and SAO/NASA ADS (last updated successfully 2020-08-10 20:29:55). The list may be incomplete as not all publishers provide suitable and complete citation data.