A JIT compiler for hybrid quantum programs in PennyLane
C-Rank Grade: A (Welcoming) - 61/100
External PR Merge Rate: 42%
Response Time: 22h
First Timer Success: 44%
PennyLaneAI/catalyst has a recorded first-timer success rate of 44.4%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in PennyLaneAI/catalyst respond to incoming external pull requests in approximately 21.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 60.7 places PennyLaneAI/catalyst in the A tier.
The external contributor pull request merge rate for PennyLaneAI/catalyst is 42.2%, based on public PR activity from non-core contributors.
PennyLaneAI/catalyst currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in PennyLaneAI/catalyst welcoming community pull requests and bug fixes.
Average Response Latency
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External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 20:00 UTC (20:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 17:00 to 21:00 UTC (17:00 - 21:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (53% approval). Large PRs (>500 lines) take ~115 hours (19% approval). Keep your diff compact for rapid review.
All binary artifacts produced in a Catalyst build are built for the specific local architecture (arm on mac), except for libcustom_calls built with setup.py. The latter is built as a fat binary (universal2) and the resulting wheel is also incorrectly tagged as a universal2 wheel. Fixing this in the setup.py config should be straightforward and would save us the rename step during wheel publishing, as well as shaving off a tiny bit of unused x86 code in the libcustom_calls.
Consider the circuit CNOT(0, 1) - X(0) %0 = quantum.alloc( 2) : !quantum.reg %1 = quantum.extract %0[ 0] : !quantum.reg -> !quantum.bit %2 = quantum.extract %0[ 1] : !quantum.reg -> !quantum.bit %out_qubits:2 = quantum.custom "CNOT"() %1, %2 : !quantum.bit, !quantum.bit %3 = quantum.insert %0[ 0], %out_qubits#0 : !quantum.reg, !quantum.bit %4 = quantum.insert %3[ 1], %out_qubits#1 : !quantum.reg, !quantum.bit %5 = quantum.extract %4[ 0] : !quantum.reg -> !quantum.bit %out_qubits_0 = quantum.custom "PauliX"() %5 : !quantum.bit %6 = quantum.insert %4[ 0], %out_qubits_0 : !quantum.reg, !quantum.bit quantum.dealloc %6 : !quantum.reg After --canonicalize, the IR stays unchanged. Proposed improvement: By interchanging the order of the two inserts after the CNOT: %0 = quantum.alloc( 2) : !quantum.reg %1 = quantum.extract %0[ 0] : !quantum.reg -> !quantum.bit %2 = quantum.extract %0[ 1] : !quantum.reg -> !quantum.bit %out_qubits:2 = quantum.custom "CNOT"() %1, %
Similar to #2604, but even simpler by just deleting phase ops without control wires. There is no control regions yet in the quantum dialect, but once there are this pass would need to ensure there are no control regions in its parent chain, or call graph ancestry. One way to guarantee this right now for the future is to analyze this criteria with an opt-in model (e.g. only func, scf, module, and adjoint regions are allowed), making the pass safe.
When a gate operation carries the adjoint attribute, we cannonicalize it away in the operation's canonicalizer itself. For example, this is the one on quantum.custom op. We see that Hermitian gates have their adjoint attribute removed, and rotation gates have the rotation angle negated. However, maybe we shouldn't automatically (because canonicalizations happen very frequently) optimize the adjoint of operations away, because: quantum compilation is often a bit more deliberate from the user side a user may also expect to see an adjointed op when inspecting a circuit (because that's what they wrote) the adjoint carries semantic meaning A more controllable approach would simply wrap this is a pass. This was proposed by Romain when we first started to implement merge rotations (for OQD). At the time we didn't want to deal with all the adjoint possibilities in the merge rotation pass, so we just decided to canonicalize the adjoint away on the gate itself. But of course, pulling it out in
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When evaluating whether to contribute to PennyLaneAI/catalyst, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of PennyLaneAI/catalyst acknowledge new external contributions in approximately 21 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 42.2% were successfully merged into the primary branch.
PennyLaneAI/catalyst has a recorded first-timer success rate of 44.4%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~21 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 60.7 places PennyLaneAI/catalyst in the Welcoming tier.
The external pull request merge rate is 42.2%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, PennyLaneAI/catalyst currently has 3 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for PennyLaneAI/catalyst are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.