Strawberry Fields: A Software Platform for Photonic Quantum Computing

By Nathan Killoran, Josh A. Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, Christian Weedbrook. Quantum, 2018

358 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f5cdf6c1b657ca4c9bfbca095728710f5cd414b8

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Citations in the corpus

Evaluating analytic gradients on quantum hardware (2018, methodology): “[18] Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook, “Strawberry Fields: A software platform for photonic quantum computing,” arXiv preprint (2018), arxiv:1804.”
Transfer learning in hybrid classical-quantum neural networks (2019, methodology): “The numerical simulation was performed with the Strawberry Fields software platform [25], combined with the TensorFlow [4] optimization back-end.”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, methodology): “As of version 0.8, PennyLane has plugins available for Strawberry Fields [ 25, 51 ] , Rigetti Forest [ 52, 53 ] , Qiskit [ 43, 44 ] , Cirq [ 54 ] , ProjectQ [ 22, 55 ] .”
Continuous-variable quantum neural networks (2018, methodology): “[95] Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook.”
Quantum Generative Adversarial Learning. (2018, methodology): “[16] N. Killoran, J. Izaac, N. Quesada, V. Bergholm, M. Amy, C. Weedbrook, Strawberry Fields: a software platform for photonic quantum computing, arXiv:1804.03159 (2018).”
Blueprint for a Scalable Photonic Fault-Tolerant Quantum Computer (2020, methodology): “Application of π/2 rotations on the second mode before and after the CZ gate implements a CV CX gate [177] with Target state 1 becoming the control and Target state 2 becoming the target, and thus a GKP qubit CNOT gate.”
Quantum circuits with many photons on a programmable nanophotonic chip (2021, methodology): “The system is accessed by a high-level application programming interface: a classical computer providing the quantum programs for the photonic chip, using the Strawberry Fields Python library [31].”