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https://github.com/qiskit-community/prototype-qrao
Quantum random access optimization prototype
https://github.com/qiskit-community/prototype-qrao
optimization prototype qiskit quantum-computing
Last synced: 17 days ago
JSON representation
Quantum random access optimization prototype
- Host: GitHub
- URL: https://github.com/qiskit-community/prototype-qrao
- Owner: qiskit-community
- License: apache-2.0
- Archived: true
- Created: 2022-04-18T19:40:23.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-16T14:47:29.000Z (8 months ago)
- Last Synced: 2024-04-18T13:25:55.828Z (2 months ago)
- Topics: optimization, prototype, qiskit, quantum-computing
- Language: Python
- Homepage: https://arxiv.org/abs/2111.03167
- Size: 1.66 MB
- Stars: 27
- Watchers: 5
- Forks: 15
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.bib
Lists
- awesome-qiskit - Quantum Random Access Optimization - The quantum random access optimization (qrao) module is designed to enable users to leverage a new quantum method for combinatorial optimization problems. (Community)
README
:exclamation: _This repository, which implements quantum random access optimization (QRAO), [has been contributed upstream](https://github.com/qiskit-community/qiskit-optimization/pull/487) to [Qiskit Optimization](https://github.com/qiskit-community/qiskit-optimization), and is included with Qiskit Optimization version 0.6.0 and higher. Any future development will continue in the Qiskit Optimization repository instead of in this repository._
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[](https://www.python.org/)
[](https://github.com/Qiskit/qiskit)
[](https://github.com/Qiskit/qiskit-optimization)
[](https://github.com/qiskit-community/prototype-qrao/blob/main/LICENSE.txt)
[](https://github.com/psf/black)
[](https://github.com/qiskit-community/prototype-qrao/actions/workflows/test_latest_versions.yml)
[](https://coveralls.io/github/qiskit-community/prototype-qrao?branch=main)
Quantum Random Access Optimization
### Table of Contents
* [About This Project](#about-this-project)
* [Installation](#installation)
* [Documentation](#documentation)
- [Tutorials](docs/tutorials/)
- [How-Tos](docs/how_tos/)
- [Background](docs/background/README.md)
- [API Reference](https://qiskit-community.github.io/prototype-qrao/apidocs/)
* [Important Usage Notes](#important-usage-notes)
* [How to Give Feedback](#how-to-give-feedback)
* [Contribution Guidelines](#contribution-guidelines)
* [Acknowledgements](#acknowledgements)
* [About Prototypes](#about-prototypes)
* [Deprecation Policy](#deprecation-policy)
* [References](#references)
* [License](#license)
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### About This Project
The Quantum Random Access Optimization (QRAO) module is designed to enable users to leverage a new quantum method for combinatorial optimization problems. This approach incorporates Quantum Random Access Codes (QRACs) as a tool to encode multiple classical binary variables into a single qubit, thereby saving quantum resources and enabling exploration of larger problem instances on a quantum computer. The encodings produce a local quantum Hamiltonian whose ground state can be approximated with standard algorithms such as [VQE](https://qiskit.org/documentation/tutorials/algorithms/04_vqe_advanced.html) and [QAOA](https://qiskit.org/documentation/tutorials/algorithms/05_qaoa.html), and then rounded to yield approximation solutions of the original problem.
The figure at the top of this page illustrates a simple example of encoding a degree-3 graph with 10 vertices into just 4 qubits, instead of 10. The graph is colored (with a largest-degree-first greedy algorithm) such that adjacent vertices are of a different color. Then, vertices of a given color are encoded using a (3,1,p) QRAC so that a maximum of 3 vertices are associated with a single qubit. Check out the [background documentation](https://github.com/qiskit-community/prototype-qrao/blob/main/docs/background/README.md) to learn more about the coloring algorithm, QRACs, and building associated local quantum Hamiltonians.
This project evolved out of research at IBM Quantum on [Approximate Solutions of Combinatorial Problems via Quantum Relaxations](https://arxiv.org/abs/2111.03167).
***Problem Statement***
Given a [quadratic unconstrained binary optimization](https://en.wikipedia.org/wiki/Quadratic_unconstrained_binary_optimization) (QUBO) problem represented by a relaxation to a local quantum Hamiltonian, find an approximate solution by rounding the energy associated with a candidate ground state. This Hamiltonian is constructed using quantum random access codes for memory compression, which allows each qubit to encode more than one binary variable.
***QRAO Flowchart***
Quantum random access optimization is composed of two main steps as shown in the flowchart below.
1. The input problem is encoded into a quantum Hamiltonian using the `QuantumRandomAccessEncoding` class. The maximum number of original classical binary variables encoded into each qubit is specified by `max_vars_per_qubit`.
2. The encoded problem is solved with the `QuantumRandomAccessOptimizer`, which uses a minimum eigensolver on the quantum Hamiltonian, rounds the solution, and returns the processed results.
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### Installation
Python 3.7 or higher is required. Full installation instructions are provided in [`INSTALL.md`](INSTALL.md), but here is a quick summary:
```sh
$ git clone https://github.com/qiskit-community/prototype-qrao.git
$ cd prototype-qrao
$ python3 -m venv venv
$ source venv/bin/activate
$ pip install tox notebook -e '.[notebook-dependencies]'
$ jupyter notebook
```
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### Documentation
Documentation is stored in the [`docs/`](docs/) directory. It is organized according to the [Diátaxis framework](https://diataxis.fr/):
- [Tutorials](docs/tutorials/): longer examples of end-to-end usage
- [How-to guides](docs/how_tos/): targeted answers to common questions
- [Background material](docs/background/README.md): in-depth exploration of theoretical concepts
- [API Reference](https://qiskit-community.github.io/prototype-qrao/apidocs/): documentation of each individual class and function
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### Important Usage Notes
The current version of this module has some important usage notes and limitations.
#### Supported Problem Types
As with `qiskit-optimization`, this module currently only supports input problems that are of type `QuadraticProgram` and that can be converted to a QUBO. Furthermore, the conversion to a QUBO must be made _before_ encoding the problem into a quantum Hamiltonian with `QuantumRandomAccessEncoding` (refer to the tutorial to [see an example of this](https://github.com/qiskit-community/prototype-qrao/blob/main/docs/tutorials/01_getting_started.ipynb)).
Check out the documentation for `qiskit-optimization` to see [how to construct a `QuadraticProgram`](https://github.com/Qiskit/qiskit-optimization/blob/main/docs/tutorials/01_quadratic_program.ipynb) and [apply converters to it](https://github.com/Qiskit/qiskit-optimization/blob/main/docs/tutorials/02_converters_for_quadratic_programs.ipynb) (including a [wrapper that converts a `QuadraticProgram` to a QUBO](https://github.com/Qiskit/qiskit-optimization/blob/main/qiskit_optimization/converters/quadratic_program_to_qubo.py)).
#### Statevector Simulator Usage
If you would like to use a statevector simulation for Magic Rounding, we advise using the new `AerSimulator` ([source](https://github.com/Qiskit/qiskit-aer/blob/main/qiskit/providers/aer/backends/aer_simulator.py)) with the `"statevector"` method and _not_ the ([soon-to-be-deprecated](https://github.com/Qiskit/qiskit-aer/blob/73b29ad75a0d206c68aff42d4d63adf3a42b61b5/qiskit/providers/aer/backends/statevector_simulator.py#L178)) `StatevectorSimulator`. The latter suffers from a very poor runtime scaling when used with the Magic Rounding scheme, as it effectively brute-forces all possible solutions.
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### How to Give Feedback
We encourage your feedback! You can share your thoughts with us by:
- [Opening an issue](https://github.com/qiskit-community/prototype-qrao/issues) in the repository
- [Starting a conversation on GitHub Discussions](https://github.com/qiskit-community/prototype-qrao/discussions)
- [Filling out our survey](https://airtable.com/shrWnomSunVJTz6PV)
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### Contribution Guidelines
For information on how to contribute to this project, please take a look at our [contribution guidelines](CONTRIBUTING.md).
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### Acknowledgements
This prototype is based on the research described in [[1]](#references).
The initial codebase was written by Takashi Imamichi, Toshinari Itoko, and Bryce Fuller.
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### About Prototypes
[Prototypes](https://qiskit-community.github.io/prototypes/) is a collaboration between developers and researchers that will give users access to prototypes from cutting-edge research in areas like quantum simulation and machine learning. These software packages are built on top of, and may eventually be integrated into the Qiskit SDK. They are a contribution as part of the Qiskit community.
Check out our [blog post](https://medium.com/qiskit/try-out-the-latest-advances-in-quantum-computing-with-ibm-quantum-prototypes-11f51124cb61) for more information!
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### Deprecation Policy
Prototypes are meant to evolve rapidly and, as such, do not follow [Qiskit's deprecation policy](https://qiskit.org/documentation/contributing_to_qiskit.html#deprecation-policy). We may occasionally make breaking changes in order to improve the user experience. When possible, we will keep old interfaces and mark them as deprecated, as long as they can co-exist with the new ones. Each substantial improvement, breaking change, or deprecation will be documented in [`NEWS.md`](NEWS.md).
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### References
[1] Bryce Fuller, Charles Hadfield, Jennifer R. Glick, Takashi Imamichi, Toshinari Itoko, Richard J. Thompson, Yang Jiao, Marna M. Kagele, Adriana W. Blom-Schieber, Rudy Raymond, and Antonio Mezzacapo. Approximate Solutions of Combinatorial Problems via Quantum Relaxations. [arXiv:2111.03167](https://arxiv.org/abs/2111.03167).
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### License
[Apache License 2.0](LICENSE.txt)