Quantum-Practices: Best Practices for Quantum Algorithms in DeepSeek Harness
unitarylab/quantum-practices
A read-only tool bundle for DeepSeek Harness delivering quantum algorithm best practices with 60 packaged guides.
Provides structured quantum algorithm practice guides for DeepSeek Harness agents, covering a wide range of algorithms from primitives to quantum error correction, with multi-simulator support.
dsh plugin --profile web add github:unitarylab/quantum-practices- Category
- Data & Knowledge
- Platform
- DSH-Plugin
- Author
- unitarylab
- Distribution
- Plugin
quantum-practices Key Features
quantum-practices Repository
quantum-practices Repo Summary
Quantum-Practices is a DeepSeek Harness (DSH) plugin for quantum algorithm best practices, maintained by unitarylab and adapted from the GitHub project quantum-skills. It addresses the lack of structured, reviewable guidance in quantum algorithm learning and practice by providing a read-only quantum_practices tool that offers a packaged catalog of practice guides to DSH agents. The plugin covers a wide range of algorithms from primitives to quantum error correction and supports multiple simulators, making it suitable for education, circuit design, code review, and demonstrations.
What core features does the Quantum-Practices DSH plugin support?
Quantum-Practices features progressive disclosure (lightweight root SKILL.md, loading algorithm and simulator guides on demand), a read-only runtime (no network, subprocess, filesystem writes, Python execution, credentials, or native code), and multi-simulator support (UnitaryLab default recommended, Qiskit for noise models and IBM hardware workflows, PennyLane for differentiable hybrid optimization). It packages 60 skills covering primitives (Grover, QPE, Hadamard Test, etc.), linear systems (HHL, LCU, QSVT-QLSA, etc.), cryptography (Shor, discrete logarithm, Simon), Hamiltonian simulation (Trotter, QDrift, etc.), Schrodingerization (advection, heat equation), eigensolvers, gradient methods, quantum machine learning (VQE, QAOA, QCBM, etc.), state preparation, and quantum error correction (qLDPC, CSS codes, etc.).
How to install Quantum-Practices?
To install Quantum-Practices as a DSH plugin, run the following command in your terminal:
dsh plugin --profile web add github:unitarylab/quantum-practices
After installation, the plugin registers a read-only quantum_practices tool with list, search, and get operations to list, search, and read practice guides from the catalog.
Which DeepSeek Harness versions and platforms are compatible with Quantum-Practices?
Quantum-Practices integrates as a DeepSeek Harness tool bundle via the dsh.bundle manifest in package.json, depending on @deepseek-ai/dsh-tools. It supports Windows, macOS, and Linux platforms and does not depend on a specific DSH version because its runtime is read-only and does not execute code.
What is the source and license of Quantum-Practices practice guides?
The practice guide corpus is sourced from the public GitHub upstream unitarylab/quantum-skills, synchronized only from the public repository to ensure reviewability. The plugin is maintained by unitarylab, created on 2026-08-14 and last updated the same day, with 17 stars and 3 forks, and the license is NOASSERTION (not declared).
What use cases is Quantum-Practices suitable for?
Quantum-Practices is suitable for educational scenarios, including concept explanation, circuit design, code review, and hands-on demonstrations. It provides clear simulator selection rules to help users choose the appropriate backend based on needs (e.g., learning, noise simulation, or hybrid optimization). For developers who want to systematically learn quantum algorithms or apply quantum computing in real projects, this is a practical reference tool.