Overview and Comparison of Gate Level Quantum Software Platforms

Abstract

Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms---Forest (pyQuil), Qiskit, ProjectQ, and the Quantum Developer Kit (Q\#)---that enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages.

v2: Added appendix on Cirq, updated appendix on other quantum software, minor syntax changes. Accepted in Quantum

References in corpus (18)

• Quantum Machine Learning
• Quantum Simulation
• Improved Simulation of Stabilizer Circuits
• Quantum principal component analysis
• Towards Quantum Chemistry on a Quantum Computer
• Quantized Majorana conductance
• ProjectQ: An Open Source Software Framework for Quantum Computing
• Cloud Quantum Computing of an Atomic Nucleus
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Quantum computational finance: Monte Carlo pricing of financial derivatives
• Learning the quantum algorithm for state overlap
• 0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit
• A Software Methodology for Compiling Quantum Programs
• Toward an architecture for quantum programming
• Quipper: A Scalable Quantum Programming Language
• An Introduction to Quantum Programming in Quipper
• Quantum annealing via environment-mediated quantum diffusion
• A lambda calculus for quantum computation with classical control

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