unitarylab

quantum-practices

Name: quantum-practices
Author: unitarylab

Quantum Algorithms Best Practices

Stars: 13
Language: Python
Created: Aug 14, 2026
Updated: Aug 14, 2026

Skills

GitHub repo

Introduction

⚛ Quantum-Practices

Quantum Algorithms Best Practices
量子算法最佳实践

English · 中文

English

What is this?

Quantum-Practices is a DeepSeek Harness tool bundle for quantum algorithm best practices. It provides structured, reviewable guidance to DeepSeek Harness agents through a read-only model-facing tool.

As a DeepSeek Harness plugin, it registers one read-only quantum_practices tool for listing, searching, and reading packaged quantum algorithm practice guides from an immutable build-time catalog.

Quantum-Practices is based on and adapted from the GitHub project unitarylab/quantum-skills. The original project provides the quantum algorithm guide corpus; this repository reworks that foundation into a DeepSeek Harness tool bundle with a generated, read-only practice catalog.

✨ Key Features

Progressive Disclosure — Root SKILL.md is lightweight; algorithm and simulator guides load only when needed.
DeepSeek Harness Tool Bundle — quantum_practices exposes list, search, and get without executing code.
Read-Only Runtime — No network, subprocess, filesystem writes, Python execution, credentials, or native code.
Best-Practice Coverage — Primitives, linear systems, cryptography, Hamiltonian simulation, Schrodingerization, eigensolvers, gradients, quantum machine learning, state preparation, and quantum error correction.
Multi-Simulator Support — UnitaryLab (recommended), Qiskit, and PennyLane, with clear selection rules.
GitHub-Sourced Corpus — Practice guides are synchronized from the public GitHub upstream only.
Education-Friendly — Suitable for concept explanation, circuit design, code review, and hands-on demos.

🌟 Algorithms Covered

Category	Algorithms
Primitives	Grover, QPE, Hadamard Test, Hadamard Transform, Amplitude Amplification, Amplitude Estimation
Linear Systems	HHL, LCU, AQC, VQLS, QSVT-QLSA, QFT, Quantum Signal Processing (QSP)
Cryptography	Shor's Algorithm, Discrete Logarithm, Simon's Algorithm
Hamiltonian Simulation	Cartan decomposition, Trotter, QDrift, Taylor Series, QSP
Schrodingerization	Advection, Heat (1D/2D)
Eigensolvers	NumPyEigensolver, VQD
Gradients	Parameter-shift, Finite-difference, Linear-combination, SPSA, Reverse-mode, QFI
Quantum Machine Learning	VQE, VQC, QAOA, QCBM, CVQNN, Fermi-Hubbard VQE
State Preparation	Mottonen, MPS, Multiplexer, Pauli, Superposition
Quantum Error Correction	qLDPC, CSS Codes, Hypergraph Product Codes

💻 Supported Simulators

Simulator	When to Use	Platform
UnitaryLab (default)	Learning, algorithm demos, PDE workflows	Win / macOS / Linux
Qiskit	Noise models, IBM hardware workflows	Win / macOS / Linux
PennyLane	Differentiable hybrid optimization	Win / macOS / Linux

📁 Repository Structure

quantum-practices/
|
+-- SKILL.md                    # Root practice index used by the catalog
+-- README.md
+-- package.json                # DeepSeek Harness tool-bundle metadata
+-- cordis.patch.yml            # Profile Bundle patch
+-- src/                        # DSH plugin source
+-- lib/                        # Built release artifact
|
+-- algorithms/                 # Quantum algorithm skills
|   +-- primitives/             # Grover, QPE, Hadamard test/transform, AA, AE
|   +-- linear-systems/         # HHL, LCU, AQC, VQLS, QSVT-QLSA, QFT, QSP
|   +-- cryptography/           # Shor, discrete logarithm, Simon
|   +-- hamiltonian-simulation/ # Cartan, Trotter, QDrift, Taylor, QSP
|   +-- schrodingerization/     # Advection and heat-equation workflows
|   +-- eigensolvers/           # NumPyEigensolver, VQD
|   +-- gradients/              # Parameter-shift, finite-diff, SPSA, reverse, QFI
|   +-- quantum-machine-learning/ # VQE, VQC, QAOA, QCBM, CVQNN
|   +-- state-preparation/      # Mottonen, MPS, multiplexer, Pauli, superposition
|   +-- quantum-error-correction/ # qLDPC, CSS codes
|
+-- simulators/                 # Simulator selection & installation guides
    +-- unitarylab/             # Recommended simulator guide
    +-- qiskit/
    +-- pennylane/

DeepSeek Harness Plugin

For most users, install Quantum-Practices into the DeepSeek Harness profile you use, then ask your agent to consult Quantum-Practices before answering quantum algorithm questions.

If you use the Web UI:

npx @deepseek-ai/dsh@0.1.0-rc.6 plugin --profile web add \
  github:unitarylab/quantum-practices#main

Restart DeepSeek Harness Web after installation.

If you use the headless CLI:

npx @deepseek-ai/dsh@0.1.0-rc.6 plugin --profile headless add \
  github:unitarylab/quantum-practices#main

For local development, install this checkout directly:

dsh plugin --profile web add "/path/to/quantum-practices"
dsh plugin --profile headless add "/path/to/quantum-practices"

For review or release evidence, replace main with a pinned 40-character commit SHA.

Verify that the profile contains the inserted row:

dsh --profile headless --dump-config | \
  rg "tool-quantum-practices|dsh-unitarylab-quantum-practices"

Expected output:

# == dsh-unitarylab-quantum-practices
- id: tool-quantum-practices
  name: dsh-unitarylab-quantum-practices

Run a functional test:

npx @deepseek-ai/dsh@0.1.0-rc.6 --profile headless \
  "Use the quantum_practices tool to find the HHL practice guide and explain the required matrix constraints."

After installation, users can ask naturally. The model should call quantum_practices in the background:

Use Quantum-Practices to review HHL before explaining the matrix constraints on A.
Before writing Grover code, check Quantum-Practices and list the common implementation pitfalls.
Use Quantum-Practices to compare quantum phase estimation and the quantum Fourier transform.
Consult Quantum-Practices and recommend a simulator for a variational quantum algorithm.
Check Quantum-Practices and explain how Trotter and QDrift differ for Hamiltonian simulation.

By default, get returns a brief, token-conscious view with the most relevant sections. The model should request detail="full" only when the user needs full implementation notes, complete examples, or debugging context.

Developers can also inspect the tool contract directly:

quantum_practices(action="list")
quantum_practices(action="search", query="HHL linear system")
quantum_practices(action="get", id="algorithms/linear-systems/hhl")
quantum_practices(action="get", query="Explain HHL matrix constraints")
quantum_practices(action="get", query="Implement HHL with a 2x2 example", detail="full")

The DSH plugin never executes algorithms/**/scripts/*.py and never installs or imports Python dependencies.

Build and Verify

npm ci
npm run check
npm pack --dry-run --json

npm run build regenerates src/generated/skill-catalog.ts and compiles the committed lib/ release artifact.

Python Runtime

Quantum-Practices does not ship a root requirements.txt, bundled wheels, or a Python runtime. Any Python setup belongs to the separate project where you choose to run generated examples; it is not part of the DeepSeek Harness plugin install path.

License

This repository source is licensed under the MIT License.

Attribution

Quantum-Practices is a derivative adaptation of unitarylab/quantum-skills. See NOTICE for attribution details.

中文

这是什么？

Quantum-Practices 是一个面向量子算法最佳实践的 DeepSeek Harness 工具包。它通过一个只读模型工具，为 DeepSeek Harness Agent 提供结构化、可审查的量子算法实践指南。

作为 DeepSeek Harness 插件，它注册一个只读 quantum_practices 工具，用于从构建期固化的 Practice Catalog 中列出、搜索和读取量子算法实践指南。

Quantum-Practices 基于 GitHub 项目 unitarylab/quantum-skills 进行二次创作。原项目提供了量子算法指南语料；本仓库在此基础上改造为 DeepSeek Harness 工具包，并生成只读的 Practice Catalog。

✨ 核心特性

渐进式加载 — 根 SKILL.md 轻量，算法与模拟器指南仅在需要时才加载。
DeepSeek Harness 工具包 — quantum_practices 提供 list、search、get，不执行代码。
只读运行时 — 无网络、无 subprocess、无写盘、无 Python 执行、无 credentials、无 native code。
最佳实践覆盖 — 基元、线性系统、密码学、哈密顿量模拟、Schrodingerization、本征求解器、梯度方法、量子机器学习、态制备与量子纠错一应俱全。
多模拟器支持 — UnitaryLab（推荐）、Qiskit、PennyLane，附明确选型规则。
GitHub 来源语料 — Practice guides 仅从公开 GitHub 上游同步。
教学友好 — 适用于概念解释、电路设计、代码审查和动手实验。

🌟 算法覆盖范围

分类	算法
基础量子算法	Grover、QPE、Hadamard 测试、Hadamard 变换、振幅放大、振幅估计
线性系统	HHL、LCU、AQC、VQLS、QSVT-QLSA、QFT、量子信号处理（QSP）
密码学	Shor 算法、离散对数、Simon 算法
哈密顿量模拟	Cartan 分解、Trotter、QDrift、Taylor 级数、QSP
Schrodingerization	对流、热方程（一维/二维）
本征求解器	NumPyEigensolver、VQD
梯度方法	参数位移、有限差分、线性组合、SPSA、反向模式、QFI
量子机器学习	VQE、VQC、QAOA、QCBM、CVQNN、Fermi-Hubbard VQE
态制备	Mottonen、MPS、Multiplexer、Pauli、Superposition
量子纠错	qLDPC、CSS 码、超图乘积码

💻 支持的模拟器

模拟器	适用场景	平台
UnitaryLab (默认)	学习、算法演示、PDE 工作流	Win / macOS / Linux
Qiskit	噪声模型、IBM 硬件工作流	Win / macOS / Linux
PennyLane	可微分混合优化	Win / macOS / Linux

📁 仓库结构

quantum-practices/
|
+-- SKILL.md                    # Catalog 使用的根实践索引
+-- README.md
+-- package.json                # DeepSeek Harness tool-bundle 元数据
+-- cordis.patch.yml            # Profile Bundle patch
+-- src/                        # DSH 插件源码
+-- lib/                        # 编译后的 release artifact
|
+-- algorithms/                 # 量子算法技能
|   +-- primitives/             # Grover、QPE、Hadamard 测试/变换、振幅放大与估计
|   +-- linear-systems/         # HHL、LCU、AQC、VQLS、QSVT-QLSA、QFT、QSP
|   +-- cryptography/           # Shor、离散对数、Simon
|   +-- hamiltonian-simulation/ # Cartan、Trotter、QDrift、Taylor、QSP
|   +-- schrodingerization/     # 对流与热方程工作流
|   +-- eigensolvers/           # NumPyEigensolver、VQD
|   +-- gradients/              # 参数位移、有限差分、SPSA、反向模式、QFI
|   +-- quantum-machine-learning/ # VQE、VQC、QAOA、QCBM、CVQNN
|   +-- state-preparation/      # Mottonen、MPS、Multiplexer、Pauli、Superposition
|   +-- quantum-error-correction/ # qLDPC、CSS 码
|
+-- simulators/                 # 模拟器选型与安装指南
    +-- unitarylab/             # 推荐模拟器指南
    +-- qiskit/
    +-- pennylane/

DeepSeek Harness 插件

普通用户不需要理解底层 action。把 Quantum-Practices 安装进正在使用的 DeepSeek Harness profile 之后，直接让 Agent 先查 Quantum-Practices，再回答量子算法问题即可。

如果你使用 Web UI：

npx @deepseek-ai/dsh@0.1.0-rc.6 plugin --profile web add \
  github:unitarylab/quantum-practices#main

安装后重启 DeepSeek Harness Web。

如果你使用 headless CLI：

npx @deepseek-ai/dsh@0.1.0-rc.6 plugin --profile headless add \
  github:unitarylab/quantum-practices#main

本地开发时，可以直接安装当前 checkout：

dsh plugin --profile web add "/path/to/quantum-practices"
dsh plugin --profile headless add "/path/to/quantum-practices"

审核或 release evidence 建议把 main 换成固定的 40 位 commit SHA。

验证 profile 中是否出现插入的 row：

dsh --profile headless --dump-config | \
  rg "tool-quantum-practices|dsh-unitarylab-quantum-practices"

期望输出：

# == dsh-unitarylab-quantum-practices
- id: tool-quantum-practices
  name: dsh-unitarylab-quantum-practices

做一次真实功能测试：

npx @deepseek-ai/dsh@0.1.0-rc.6 --profile headless \
  "Use the quantum_practices tool to find the HHL practice guide and explain the required matrix constraints."

安装完成后，用户可以直接自然提问；模型应在后台调用 quantum_practices：

请先查 Quantum-Practices，再解释 HHL 对矩阵 A 的约束。
写 Grover 代码前，请查 Quantum-Practices 并列出常见实现错误。
请根据 Quantum-Practices 比较量子相位估计和量子傅里叶变换。
请查 Quantum-Practices，并建议变分量子算法应该使用哪个 simulator。
请参考 Quantum-Practices，说明 Trotter 和 QDrift 在哈密顿量模拟中的区别。

默认情况下，get 返回省 token 的 brief 视图，只包含最相关的章节。只有当用户需要完整实现说明、完整示例或调试上下文时，模型才应该请求 detail="full"。

开发者也可以直接查看工具接口：

quantum_practices(action="list")
quantum_practices(action="search", query="HHL linear system")
quantum_practices(action="get", id="algorithms/linear-systems/hhl")
quantum_practices(action="get", query="Explain HHL matrix constraints")
quantum_practices(action="get", query="Implement HHL with a 2x2 example", detail="full")

DSH 插件不会执行 algorithms/**/scripts/*.py，也不会安装或导入 Python 依赖。

构建与验证

npm ci
npm run check
npm pack --dry-run --json

npm run build 会重新生成 src/generated/skill-catalog.ts，并编译需要提交的 lib/ release artifact。

Python 运行时

Quantum-Practices 不发布根 requirements.txt、内置 wheel 或 Python runtime。任何 Python 环境都应属于你实际运行示例的独立项目，不属于 DeepSeek Harness 插件安装路径。

License

本仓库源码采用 MIT License。

Attribution

Quantum-Practices 是基于 unitarylab/quantum-skills 的二次创作。来源说明详见 NOTICE。