Show HN: Q⊗DASH – 基于图的量子计算的 Rust/Python 量子算符框架
2 分•作者: dioniceOS•7 个月前
我发布了一个实验性的量子计算框架，它源于我自己的算子/图工作：Q⊗DASH (MetatronQSO)
Rust 核心 crate：metatron-qso-rs
Python 包：metatron_qso
GitHub：[https://github.com/LashSesh/qso](https://github.com/LashSesh/qso)
crates.io：[https://crates.io/crates/metatron-qso-rs](https://crates.io/crates/metatron-qso-rs)
PyPI：[https://pypi.org/project/metatron_qso/](https://pypi.org/project/metatron_qso/)
# 它是做什么的
MetatronQSO 是一个以 Rust 为主导的量子算子框架，带有 Python 绑定。它专注于基于图和算子的算法：量子行走、VQE、QAOA 风格的电路和相关实验。
核心理念：
- 用于状态演化、电路、行走和变分算法的 Rust 库
- 基于 PyO3 的 Python SDK，反映相同的概念
- 后端抽象（现在是本地模拟器，通过 traits 为硬件提供商预留空间）
- 一个非平凡的默认几何结构（Metatron-cube 风格的图），而不是玩具线/网格图
目标不是“又一个 Qiskit 包装器”，而是一个你可以融入自己模型的自包含算子核心。
# 架构（高层次）
工作空间组织为多个 crate，关键的包括：
- metatron-qso-rs：核心量子库（状态、算子、电路、行走、VQE/QAOA、示例二进制文件）
- metatron_qso_py：Python 绑定
- 用于插入执行后端和公开基本指标的后端/遥测 crate
一切都是常规的 Rust + Cargo，Python wheel 通过 maturin 构建。
# 当前状态
metatron-qso-rs 已在 crates.io 上发布 (0.1.x)
metatron_qso 已在 PyPI 上发布 (0.1.x)
CI 构建 Rust + Python，运行测试和一些基准测试
仓库中有文档/笔记，解释了算子模型和后端设计
它还处于早期阶段，但它可以编译、运行示例，并且如果你熟悉 Rust（或者乐于从 Python 驱动它），就可以用于实验。
# 我希望得到哪些反馈
Rust API 表面是否感觉符合习惯用法且可组合？
对于 Python 用户：当前的绑定层是否是你真正会使用的，还是你期望更高层次的抽象？
后端抽象（现在是本地模拟器，未来是硬件）的结构是否易于扩展？
在将图/几何结构作为主要对象处理方面，是否存在任何明显的危险信号？
如果你对量子计算、基于图的算法或不寻常的 Rust 工作空间感兴趣，我很乐意收到任何反馈、批评或关于下一步应该怎么做的想法。
查看原文
I’ve released an experimental quantum computing framework that grew out of my own operator/graph work: Q⊗DASH (MetatronQSO)<p>Rust core crate: metatron-qso-rs
Python package: metatron_qso<p>GitHub: <a href="https://github.com/LashSesh/qso" rel="nofollow">https://github.com/LashSesh/qso</a>
crates.io: <a href="https://crates.io/crates/metatron-qso-rs" rel="nofollow">https://crates.io/crates/metatron-qso-rs</a>
PyPI: <a href="https://pypi.org/project/metatron_qso/" rel="nofollow">https://pypi.org/project/metatron_qso/</a><p># What it is<p>MetatronQSO is a Rust-first quantum operator framework with Python bindings. It focuses on graph- and operator-based algorithms: quantum walks, VQE, QAOA-style circuits and related experiments.<p>Core ideas:
- Rust library for state evolution, circuits, walks and variational algorithms
- PyO3-based Python SDK mirroring the same concepts
- Backend abstraction (local simulator now, room for hardware providers via traits)
- A nontrivial default geometry (a Metatron-cube–style graph) instead of toy line/grid graphs<p>The goal is not “yet another Qiskit wrapper”, but a self-contained operator core you can bend into your own models.<p># Architecture (high level)<p>The workspace is organized as multiple crates, the key ones:
- metatron-qso-rs: core quantum library (state, operators, circuits, walks, VQE/QAOA, example binaries)
- metatron_qso_py: Python bindings
- backend/telemetry crates for plugging in execution backends and exposing basic metrics<p>Everything is regular Rust + Cargo, with Python wheels built via maturin.<p># Current status<p>metatron-qso-rs published on crates.io (0.1.x)
metatron_qso published on PyPI (0.1.x)
CI builds Rust + Python, runs tests and some benchmarks
There are docs/notes in the repo explaining the operator model and backend design<p>It’s early-stage, but it compiles, runs examples, and is usable for experiments if you’re comfortable with Rust (or happy to drive it from Python).<p># What I’d like feedback on<p>Does the Rust API surface feel idiomatic and composable?
For Python users: is the current binding layer something you’d realistically work with, or would you expect a higher-level abstraction?
Is the backend abstraction (local simulator now, future hardware later) structured in a way that’s easy to extend?
Any obvious red flags in how I treat graphs/geometry as the primary object?<p>If you’re into quantum computing, graph-based algorithms, or unusual Rust workspaces, I’d appreciate any feedback, criticism, or ideas for where this should go next.