qRAM Library for Q#

This library implements a variety of different proposals for memory for quantum computers, also commonly called qRAM. Want to learn more about what qRAM is? Check out the primer on memory for quantum computers in our docs!

Motivation

There are many different proposals for qRAM in quantum computing that each have different tradeoffs, and currently come up a lot in quantum machine learning applications. We want to better understanding the costs and benefits of different qRAM implementations in quantum machine learning as well as quantum computing more generally. This library will help achieve these goals by giving us a concrete way to measure the resources each approach takes; choosing to do this in Q# allows us to leverage the built-in resource estimator to quickly iterate profiling the qRAM implementations and optimizing the circuits.

Build status

Code style

Screenshots

Example of a Bucket Brigade qRAM circuit:

TODO: Include logo/demo screenshot etc.

Tech/framework used

Built with:

• Quantum Development Kit
• .NET Core SDK 3.1
• Python
• Visual Studio Code and Visual Studio
• Jupyter Notebook

Features

What makes your project stand out?

Code Example

TODO: Add more! Fix syntax highlighting

operation QueryAndMeasureQRAM(memory : QRAM, queryAddress : Int) : Int {
        using ((addressRegister, targetRegister) = (Qubit[memory::AddressSize], Qubit[memory::DataSize])) {
            ApplyPauliFromBitString (PauliX, true, IntAsBoolArray(queryAddress, memory::AddressSize), addressRegister);
            memory::Lookup(LittleEndian(addressRegister), targetRegister);
            ResetAll(addressRegister);
            return MeasureInteger(LittleEndian(targetRegister));
        }
    }

Installation

TODO:

• Docker
• Binder
• Codespaces
• Remote dev env
• local install

API Reference

TODO: Complete once compiler extension is finalized for scraping API docs

Tests

TODO: Describe and show how to run the tests with code examples.

How to use?

TODO: Link to user manual in docs

Contribute

Please see our contributing guidelines and our code of conduct before working on a contribution, thanks!

Credits

• Primary developers: @glassnotes, @shikharsingh3, @crazy4pi314
• Code review and API design assistance: @RolfHuisman, @cgranade

Anything else that seems useful

• WIQCA talk on qRAM by @glassnotes
• Live development of this library with @crazy4pi314 on Twitch

License

MIT © qsharp-community