tensorflow / tensorflow/quantum
Support dynamic quantum circuits
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- Dominant language
- Python
- Stars
- 2.2k
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Description
“Adaptive quantum circuits” (also known as dynamic circuits or feedforward circuits) is where a sequence of quantum operations is not fixed in advance but is determined by the outcomes of measurements made during execution. It involves a combination of (a) mid-circuit measurements in one part of a circuit, (b) real-time classical computation on the measurement outcomes, and (c) logic to decide on the application of controlled gates elsewhere in the circuit based on the results of the classical computation.
To support research into feedforward circuits, TensorFlow Quantum should support the ability for mid-circuit measurements to control later operations. This violates theoretical assumptions underlying the common approach of preparing a circuit and doing only terminal measurements, and may require significant work to implement
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. Start by locating the existing circuit execution, mid-circuit measurement, classical computation, and conditional-operation paths, then determine the design needed for measurement outcomes to control later gates. Done means TensorFlow Quantum supports the described feedforward circuit behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- quantum-computing
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100