展示HN：Q⊗DASH – 用于基于图的量子计算的Rust/Python量子算子框架
我发布了一个实验性的量子计算框架，源于我自己的算子/图形工作：Q⊗DASH（MetatronQSO）。
<p>Rust核心库：metatron-qso-rs
Python包：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># 这是什么
<p>MetatronQSO是一个以Rust为主的量子算子框架，带有Python绑定。它专注于基于图和算子的算法：量子游走、变分量子特征求解器（VQE）、量子近似优化算法（QAOA）风格的电路及相关实验。
<p>核心理念：
- 用于状态演化、电路、游走和变分算法的Rust库
- 基于PyO3的Python SDK，反映相同的概念
- 后端抽象（目前是本地模拟器，未来可通过特征支持硬件提供商）
- 一个非平凡的默认几何结构（类似Metatron立方体的图形），而不是玩具线性/网格图
<p>目标不是“又一个Qiskit包装器”，而是一个自包含的算子核心，可以灵活地融入到你自己的模型中。
<p># 架构（高层次）
<p>工作空间组织为多个库，关键的有：
- metatron-qso-rs：核心量子库（状态、算子、电路、游走、VQE/QAOA、示例二进制文件）
- metatron_qso_py：Python绑定
- 后端/遥测库，用于插入执行后端并暴露基本指标
<p>一切都是常规的Rust + Cargo，Python轮子通过maturin构建。
<p># 当前状态
<p>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
仓库中有文档/说明，解释算子模型和后端设计
<p>目前处于早期阶段，但它可以编译、运行示例，并且如果你对Rust感到熟悉（或愿意通过Python来操作），可以用于实验。
<p># 我希望得到的反馈
<p>Rust API的表面是否感觉符合习惯且易于组合？
对于Python用户：当前的绑定层是否是你可以实际使用的，还是你期待更高层次的抽象？
后端抽象（目前是本地模拟器，未来是硬件）是否以易于扩展的方式构建？
在我将图形/几何视为主要对象的处理方式上，有没有明显的红旗？
<p>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，我将非常感激任何反馈、批评或对下一步方向的建议。
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.