amazon-braket-algorithm-library's Introduction

Amazon Braket Algorithm Library

The Braket Algorithm Library provides Amazon Braket customers with pre-built implementations of prominent quantum algorithms and experimental workloads as ready-to-run example notebooks.

Braket algorithms

Currently, Braket algorithms are tested on Linux, Windows, and Mac.

Running notebooks locally requires additional dependencies located in notebooks/textbook/requirements.txt. See notebooks/textbook/README.md for more information.

Textbook algorithms	Notebook	References
Bell's Inequality	Bells_Inequality.ipynb	Bell1964, Greenberger1990
Bernstein–Vazirani	Bernstein_Vazirani_Algorithm.ipynb	Bernstein1997
CHSH Inequality	CHSH_Inequality.ipynb	Clauser1970
Deutsch-Jozsa	Deutsch_Jozsa_Algorithm.ipynb	Deutsch1992
Grover's Search	Grovers_Search.ipynb	Figgatt2017, Baker2019
QAOA	Quantum_Approximate_Optimization_Algorithm.ipynb	Farhi2014
Quantum Circuit Born Machine	Quantum_Circuit_Born_Machine.ipynb	Benedetti2019, Liu2018
QFT	Quantum_Fourier_Transform.ipynb	Coppersmith2002
QPE	Quantum_Phase_Estimation_Algorithm.ipynb	Kitaev1995
Quantum Walk	Quantum_Walk.ipynb	Childs2002
Shor's	Shors_Algorithm.ipynb	Shor1998
Simon's	Simons_Algorithm.ipynb	Simon1997

Advanced algorithms	Notebook	References
Quantum PCA	Quantum_Principal_Component_Analysis.ipynb	He2022
QMC	Quantum_Computing_Quantum_Monte_Carlo.ipynb	Motta2018, Peruzzo2014

Auxiliary functions	Notebook
Random circuit generator	Random_Circuit.ipynb

Community repos

⚠️ The following includes projects that are not provided by Amazon Braket. You are solely responsible for your use of those projects (including compliance with any applicable licenses and fitness of the project for your particular purpose).

Quantum algorithm implementations using Braket in other repos:

Algorithm	Repo	References	Additional dependencies
Quantum Reinforcement Learning	quantum-computing-exploration-for-drug-discovery-on-aws	Learning Retrosynthetic Planning through Simulated Experience(2019)	dependencies

Installing the Amazon Braket Algorithm Library

The Amazon Braket Algorithm Library can be installed from source by cloning this repository and running a pip install command in the root directory of the repository.

git clone https://github.com/amazon-braket/amazon-braket-algorithm-library.git
cd amazon-braket-algorithm-library
pip install .

To run the notebook examples locally on your IDE, first, configure a profile to use your account to interact with AWS. To learn more, see Configure AWS CLI.

After you create a profile, use the following command to set the AWS_PROFILE so that all future commands can access your AWS account and resources.

export AWS_PROFILE=YOUR_PROFILE_NAME

Configure your AWS account with the resources necessary for Amazon Braket

If you are new to Amazon Braket, onboard to the service and create the resources necessary to use Amazon Braket using the AWS console.

Support

Issues and Bug Reports

If you encounter bugs or face issues while using the algorithm library, please let us know by posting the issue on our GitHub issue tracker.
For other issues or general questions, please ask on the Quantum Computing Stack Exchange and add the tag amazon-braket.

Feedback and Feature Requests

If you have feedback or features that you would like to see on Amazon Braket, we would love to hear from you!
GitHub issues is our preferred mechanism for collecting feedback and feature requests, allowing other users to engage in the conversation, and +1 issues to help drive priority.

License

This project is licensed under the Apache-2.0 License.

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amazon-braket-algorithm-library's Issues

Amplitude amplification

Describe the feature you'd like
Implement Amplitude amplification for matrix product verification (and if possible, generalized use)
Literature: https://arxiv.org/pdf/quant-ph/0005055.pdf

How would this feature be used? Please describe.
Amplitude amplification is used to obtain a quadratic speedup over several classical algorithms.

Describe alternatives you've considered
There is a amplify() method in grovers_search.py, but that would not work with the implementation of matrix product verification

Additional context
This is going to be a pre-req for implementing Matrix product verification as raised by issue #73. See page 39-40 of Quantum Algorithms for Beginners.

Hadamard test

Describe the feature you'd like
The Hadamard test is an algorithm to compute matrix elements of unitary operators. Implement the Hadamard algorithm.

For a detailed description see page 37 of Quantum Algorithms for Beginners.

Additional context
Please ensure that you follow the Contribution Guidelines for this repository.

Matrix product verification

Describe the feature you'd like
The matrix product verification algorithm splits a matrix verification problem into smaller subsets and then uses amplitude amplification to search if one of these verifications fail. Implement matrix product verification.

For a detailed description see page 40 of Quantum Algorithms for Beginners.