catalyst
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PennyLaneAI
A JIT compiler for hybrid quantum programs in PennyLane
A draft PR (⚠️ **don't merge**) with an MLIR test that should serve as a guide through the implementation of local folding in the ZNE transform of the Mitigation dialect....
**Context:** > In LLVM IR, memory does not have types, so LLVM’s own type system is not suitable for doing type based alias analysis (TBAA). Instead, metadata is added to...
### Context In PennyLane and Catalyst, we support shot-based execution of quantum circuits, meaning the results of a quantum computation are computed based on the repeated execution of the circuit...
The [`mitigate_with_zne`](https://docs.pennylane.ai/projects/catalyst/en/latest/code/api/catalyst.mitigate_with_zne.html) API in Catalyst accepts a `scale_factors` argument, which represents a range of scalar factors by which the circuit is scaled. In the literature of Zero-Noise Extrapolation [^1][^2], a...
**Description of the Change:** CUDAQ now inherits from the QJIT class **Benefits:** Better code. **Possible Drawbacks:** None **Related GitHub Issues:** Still cannot handle keyword arguments but we assume that once...
**Context:** First step to allow plugins in Catalyst is to allow them in quantum-opt. **Description of the Change:** Just make small changes in the build system to allow quantum-opt to...
**Context** In LLVM IR, memory does not have types. It is therefore not possible to do type based alias analysis without adding metada. Tbaa metada in LLVM (https://llvm.org/docs/LangRef.html#tbaa-metadata), adds type...
#### Issue description After updating jax and mlir dependency chain to v0.4.28 [(PR#931)](https://github.com/PennyLaneAI/catalyst/pull/931), jax introduces new `_sin_lowering` and `_cos_lowering` with fails with dynamic shapes. In the following code from [jax._src.lax.lax](https://github.com/google/jax/blob/ccc4c42ec9d0625fd25a7361acf241f41fd2109e/jax/_src/lax/lax.py#L1928-L1932),...
For the following circuit, gradient verification fails: ```python @qml.qjit def f(params, x): @qml.qnode(dev, diff_method="parameter-shift") def circuit(params, x): qml.IQPEmbedding(x, wires=range(3), n_repeats=1) qml.StronglyEntanglingLayers(params["weights"], wires=range(3), imprimitive=qml.CZ) return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) return catalyst.grad(circuit)(params, x)...
Currently, to access the intermediate stages of the milr compilation pipeline, we need to do `@qjit(keep_intermediate=True)`, and access the produced files with either python's `open()`, or some sort of `subprocess.call("less/cat...
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A JIT compiler for hybrid quantum programs in PennyLane