tensorflow/quantum

Efficiently handle circuit sizes 10x or more of today's circuits

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#1,086 opened on Jun 6, 2026

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area/circuitsarea/performancehelp wantedpriority/after-0.7.7skill-level/expert

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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

Research direction
Investigate TensorFlow Quantum's circuit representation and execution pipeline to identify bottlenecks for large circuits, then propose optimization strategies such as lazy evaluation or circuit compilation.
Tech stack
python
Domain
backend
Issue type
Performance
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
4
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonGit
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30