lanl/qmasm

Name: qmasm

Owner: Los Alamos National Laboratory

Description: Quantum macro assembler for D-Wave systems

Created: 2016-07-08 20:10:22.0

Updated: 2018-05-22 20:35:34.0

Pushed: 2018-05-22 05:36:38.0

Homepage: null

Size: 387

Language: Python

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README

QMASM: A Quantum Macro Assembler

Build Status

Description

QMASM fills a gap in the software ecosystem for D-Wave's adiabatic quantum computers by shielding the programmer from having to know system-specific hardware details while still enabling programs to be expressed at a fairly low level of abstraction. It is therefore analogous to a conventional macro assembler and can be used in much the same way: as a target either for programmers who want a great deal of control over the hardware or for compilers that implement higher-level languages.

N.B. This tool used to be called “QASM” but was renamed to avoid confusion with MIT's QASM, which is used to describe quantum circuits (a different model of quantum computation from what the D-Wave uses) and the IBM Quantum Experience's QASM (now OpenQASM) language, also used for describing quantum circuits.

Installation

QMASM is written in Python and uses Setuptools for installation. Use

on setup.py install

to install in the default location and

on setup.py install --prefix=/my/install/directory

to install elsewhere.

Documentation

Documentation for QMASM can be found on the QMASM wiki.

QMASM (then known as QASM) is discussed in the following publication:

Scott Pakin. “A Quantum Macro Assembler”. In Proceedings of the 20th Annual IEEE High Performance Extreme Computing Conference (HPEC 2016), Waltham, Massachusetts, USA, 13?15 September 2016. DOI: 10.1109/HPEC.2016.7761637.

License

QMASM is provided under a BSD-ish license with a “modifications must be indicated” clause. See the LICENSE file for the full text.

This package is part of the Hybrid Quantum-Classical Computing suite, known internally as LA-CC-16-032.

Author

Scott Pakin, pakin@lanl.gov


This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.