munich-quantum-toolkit / munich-quantum-toolkit/bench
✨ Support for running randomized benchmarking
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 131
- Forks
- 44
- Avg merge
- 2h 3m
- Merged PRs (30d)
- 36
Description
What's the problem this feature will solve?
Randomized benchmarking (RB) is a popular protocol for characterizing the error rate of quantum processors. An RB experiment consists of the generation of random Clifford circuits on the given qubits such that the unitary computed by the circuits is the identity. After running the circuits, the number of shots resulting in an error (i.e. an output different from the ground state) are counted, and from this data one can infer error estimates for the quantum device, by calculating the Error Per Clifford.
This sounds like something worthwhile to support as part of MQT Bench.
Brought up by @echavarria-lrz 🙏🏼
Describe the solution you'd like
It would be nice, if MQT Bench could be used to orchestrate RB experiments.
We still need to flesh out the details of how such an integration could look like, but I think it should be doable with moderate effort.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked randomized benchmarking manual to understand the proposed experiment and its Error Per Clifford output. The issue names no files, tests, or entry points, and says the integration details still need to be defined; done would mean agreeing on and implementing a way for MQT Bench to orchestrate RB experiments.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- performance, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100