Workbench is a Python package designed for efficient writing and execution of quantum programs. It allows developers to create quantum circuits and algorithms using Python, while leveraging the performance of an optimized, multi-threaded C++ core.
This folder contains the tutorial notebooks that accompany the official Workbench documentation.
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FTQC Primitives: Build with abstractions designed for fault-tolerant quantum computing, including quantum data types, mid-circuit measurements, and automatic uncompute.
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Built for Runtime: Scale to large circuits and runtime-style execution with support for streaming billions of operations without relying on fixed kernels.
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Large Algorithm Library: Access more than 100 interoperable, FTQC-focused algorithm implementations, including alias sampling, quantum phase estimation, and more.
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Quantum Resource Estimates (QREs): Generate accurate QREs for circuits of any size, including circuits with billions of gates, and analyze results with Resource Analyzer, Bartiq, or the Resource Estimator.
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Hardware Agnostic: Write, compile, and optimize quantum algorithms for a range of FTQC hardware architectures.
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Highly Performant Simulation: Iterate quickly with optimized C++ simulation, including native bit and Clifford simulators as well as GPU-powered tensor-network and state-vector simulation via CUDA-Q.
Workbench ships as part of PsiQDK:
pip install psiqdkWorkbench is then available under the psiqdk.workbench namespace:
from psiqdk.workbench import ...The tutorials/ directory contains runnable Jupyter notebooks (the notebooks used to generate the documentation pages).
Please file an issue to report a bug or to request a feature, or start a discussion to ask a question.