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Construct PsiQDK

What is PsiQDK?

The PsiQuantum Development Kit (PsiQDK) is a collection of Python libraries (a meta-package) in Construct used to write, analyze, optimize, and visualize fault-tolerant quantum computing (FTQC) algorithms. The PsiQDK makes it easy to research FTQC algorithms with a single set of tools that are tested to work together.

Components

Construct's PsiQDK consists of the following components:

  • Workbench: Efficiently write, analyze, and execute quantum programs.
  • Algorithms: Create quantum applications by using a collection of more than 100 of reusable and interoperable quantum algorithms and subroutines.
  • Bartiq: Compile Workbench programs and conduct quantum resource estimation (QREs).
  • QREF: Exchange compiled quantum algorithms with an open source data format.
  • Visualize: View quantum circuits and QREs in Jupyter directly within VS Code and Jupyter notebooks.
flowchart TB
    PSIQDK[PsiQDK]
    PSIQDK --> WB[Workbench]
    PSIQDK --> ALG[Algorithms]
    PSIQDK --> VZ[Visualize]
    WB -. uses .-> BQ[Bartiq]
    BQ -. uses .-> QR[QREF]

Features

  • FTQC Primitives: Build with abstractions designed for fault-tolerant quantum computing, including quantum data types, mid-circuit measurements, and automatic uncomputation.
  • Built for Runtime: Scale to large circuits and runtime-style execution with support for streaming billions of operations without relying on fixed kernels.
  • Large Algorithm Library: Access more than 100 interoperable, FTQC-focused algorithm implementations, including alias sampling, quantum phase estimation, and more.
  • 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.
  • Hardware Agnostic: Write, compile, and optimize quantum algorithms for a range of FTQC hardware architectures.
  • 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.

Support

For questions, issues, and help, visit the GitHub discussion forum ⧉.

To report a bug, please open an issue on the GitHub issues board ⧉.

Next Steps