tensorflow / tensorflow/quantum
Efficiently handle circuit sizes 10x or more of today's circuits
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 2.2k
- Forks
- 665
- PR merge metrics
- No merged PRs in 30d
Description
The field of quantum computing is progressing from quantum hardware having small numbers of noisy, short-lived physical qubits, to hardware having 100s–1000s of physical qubits combined algorithmically into error-corrected, long-lived logical qubits. For TensorFlow Quantum to support QML in the early fault-tolerant quantum computing era, it should be possible to handle circuit sizes that are at least 10x what it can comfortably handle today.
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
No files, tests, or entry points are named in the issue. Start by locating the current circuit-size handling and measuring the largest circuits TensorFlow Quantum can comfortably process today. Done means demonstrating support for circuits at least 10 times larger, with appropriate validation or benchmarks.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100