https://github.com/bbn-q/qgl.jl

A performance orientated QGL compiler.
https://github.com/bbn-q/qgl.jl

julia-language quantum-computing

Last synced: about 2 months ago
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A performance orientated QGL compiler.

• Host: GitHub
• URL: https://github.com/bbn-q/qgl.jl
• Owner: BBN-Q
• License: apache-2.0
• Created: 2016-12-20T14:47:00.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2023-12-19T14:44:37.000Z (about 1 year ago)
• Last Synced: 2024-06-25T02:33:07.990Z (6 months ago)
• Topics: julia-language, quantum-computing
• Language: Julia
• Size: 134 KB
• Stars: 12
• Watchers: 11
• Forks: 5
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# QGL.jl

[![Build Status](https://travis-ci.org/BBN-Q/QGL.jl.svg?branch=master)](https://travis-ci.org/BBN-Q/QGL.jl)

A performance orientated [QGL](https://github.com/BBN-Q/QGL) compiler.

## Installation

The package is not yet registered with METADATA.jl and so must be cloned with

```julia

Pkg.clone("https://github.com/BBN-Q/QGL.jl.git")

```

## Benchmarks

Preliminary benchmarks show speed-ups for Python QGL of ~25-30X.

In the absence of proper benchmarking and regression testing we use the 1 qubit

GST sequences from [QGL issue #69](https://github.com/BBN-Q/QGL/issues/69) and

the sequence creation script in `test/benchmark.jl`. With `q1` having 20ns

pulses and 100MHz sidebanding frequency and at commit 8fbbee6. Since it takes

5-6 seconds to compile and the default Benchmarking.jl times out with a single

run. There is some variation so it is worth running a few trials.

```julia

julia> using QGL

julia> q1 = Qubit("q1")

q1

julia> include("test/benchmark.jl")

create_1Q_GST_seqs

julia> seqs = create_1Q_GST_seqs("/home/cryan/Downloads/sequence_numbers.csv", q1);

julia> using BenchmarkTools

julia> t = @benchmark compile_to_hardware(seqs, "silly") samples=5 seconds=60

BenchmarkTools.Trial:

  memory estimate:  1.56 gb

  allocs estimate:  66356642

  --------------

  minimum time:     6.685 s (5.09% GC)

  median time:      7.104 s (5.14% GC)

  mean time:        7.091 s (5.26% GC)

  maximum time:     7.373 s (5.65% GC)

  --------------

  samples:          5

  evals/sample:     1

  time tolerance:   5.00%

  memory tolerance: 1.00%

```

## License

Apache License v2.0

## Funding

This work was funded in part by the Army Research Office under contract W911NF-14-C-0048.