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Qec Construct

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Muuuun
qec-construct

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Applies whenever the project goal is a new or better code/construction — INCLUDING search-phrased goals ("find codes beating X"), where the construct-loop (propose algebraic rule → qverify → debug) is the REQUIRED mode, replacing blind random/grid sampling (empirically caps far below frontier). Gate certification is currently abelian/CCZ only, validated against the 11 Menon codes; CSS + distance checks are general. Brain, do not run this yourself — forward this SKILL.md path into every code-construction experiment spawn task; experiment/tool_impl run the loop.

Overview

PublisherMuuuun
Repositoryluxas
Skill nameqec-construct
Stars
1.2K
Forks
18
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Muuuun on GitHub. Read the source before you install it.

Installation

Install the Qec Construct AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Muuuun/luxas/tree/main/skills/qec-construct
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Muuuun/luxas.git /tmp/luxas
mkdir -p .claude/skills
cp -r /tmp/luxas/skills/qec-construct .claude/skills/qec-construct
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qec Construct in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qec Construct on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qec Construct is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

qec-construct — a QEC verifier-in-the-loop

Routing (brain: read this section, forward the rest)

Applies when RESEARCH.md's goal is a new or improved QEC code / code family / transversal-gate construction — construct, invent, design, discover, improve distance/FOM, and search-phrased variants ("find codes beating X") equally.

Does NOT apply to magic-state cultivation, lattice surgery, decoder, or imaging projects: the gate check is abelian/CCZ-only and those are out of scope regardless of wiring.

Brain's only two obligations (do not run the loop yourself):

  1. Read this file BEFORE writing notes/plan.md.
  2. Every code-construction/code-search experiment spawn task MUST (a) include the literal path skills/qec-construct/SKILL.md with the instruction to read it in the Design phase and wrap scripts/qverify as a tool, and (b) frame the experiment as a construct-loop (propose a parametrized ALGEBRAIC construction → qverify → debug the failure), never as random/grid sampling over supports — sampling is the mode that capped at FOM ~10.4 vs frontier 45.5.

The one rule: CONSTRUCT, don't search

Broad/random/hill-climb search over code supports is a known dead end for this problem (empirically caps far below the frontier; the good codes are rare isolated optima). Your job is to propose a parametrized ALGEBRAIC construction — a group + a generating rule for the supports + (for a new gate) the cup-product / Leibniz conditions — and debug it against the sound verifier, not to sample points and hope.

If you find yourself enumerating random supports, stop: that is the wrong mode.

The verifier: scripts/qverify (this is your Lean)

Call it at high frequency. Input a construction spec (JSON), get a SOUND verdict — including which condition failed, so you can fix it.

echo '{"family":"abelian","group_shape":[3,4,5],
       "supp_a":[[0,1,3],[0,3,0],[2,1,3],[2,3,1]],
       "supp_b":[[1,1,4],[1,2,2],[2,1,1],[2,2,4]],
       "supp_c":[[0,0,4],[2,0,1]],
       "frontier_fom":14.4}' | scripts/qverify

What it checks, cheapest first:

  • CSS validity (H_X H_Z^T = 0), n, k — exact GF(2), instant. Fails loudly if your construction isn't even a code.
  • Cheap sound screen — a fast heuristic upper bound UB on d_Z (~sub-second, ~100x faster than exact). Reports FOM_upper = k*UB^3/n. If FOM_upper < frontier the code provably can't beat it → reject (never rejects a real winner, since UB ≥ d_Z). Otherwise → promote.
  • Gate (CCZ) check (supply partition_a/b/c) — STCP/Leibniz conditions hold? logical CCZ non-trivial? K_CCZ (extractable gates). Abelian path is validated against all 11 published Menon codes.
  • "exact":true — sound exact distance via ILP-to-optimality. Only this certifies distance. Run it only on promoted candidates.

The loop (this is the whole method)

  1. Propose a construction (group + support-generating rule + preorientation).
  2. qverify it (cheap). If css or gate fails → read which condition failed → fix the construction (this is the Menon-style derivation-debug; e.g. the naive cup-product conditions force d=2 — find the offset/structure that escapes it).
  3. Keep the cheap screen as your fitness: iterate the construction rule to push FOM_upper up while keeping the gate valid.
  4. On a promoted candidate, run "exact":true to certify.

The soundness gate — DO NOT violate

A code counts as a new frontier code ONLY if a single qverify run shows: css_valid ✓, gate.preserves_codespace/stcp_conditions_hold ✓, K_CCZ ≥ 1, and distance_exact.status == "optimal" with fom_exact strictly above the frontier. Estimated / biased-sampled / timed-out distances never certify a win. If you report a win on anything less, it is a hallucination, not a result.

Where the new codes likely are

The abelian families are heavily optimized (you will mostly re-find Menon). The upside is the under-explored non-abelian region ("family":"general", "group":{"kind":"dihedral","n":..} or {"kind":"perm","generators":[..]}). The CSS + distance + cheap-screen verifiers ARE general and sound for non-abelian. But two honest caveats: (1) the non-abelian CCZ gate check is NOT yet implemented in qverify — so a non-abelian code is CSS/distance-verified but cannot be gate-CERTIFIED here yet; implementing a group-agnostic direct-circuit coboundary-invariance check (validated against verify_stcp before ship) is the open next step. (2) Non-abelian is a high-risk region, not virgin territory: Tiew2026 already derived non-abelian weight-4 cup-product gate conditions, yet experts (Tiew, Menon) converged on abelian for deep reasons — the cup product is graded-commutative (abelian-aligned), and abelian gives Fourier/character analysis, analytic distance bounds, and hardware locality. Betting on non-abelian bets that their choice was driven by "harder to analyze" (which this skill's verifiers bypass) rather than "structurally worse" (which they don't).

Honest boundary

This skill gives you a sound, cheap, debuggable verifier and the construct-don't- search discipline. It does not guarantee a better code — proposing the right construction is still the hard part. But it is the machine that lets a construction loop actually converge instead of hallucinating, and it attacks the two real walls: no cheap classical proxy exists for quantum distance (so the screen measures the quantum quantity directly, cheaply, soundly), and the gate-validity that used to be guessed is now checked exactly.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qec Construct AI skill do?

Verifier-in-the-loop CONSTRUCTION of quantum error-correcting codes with transversal non-Clifford gates (CCZ/T). Applies whenever the project goal is a new or better code/construction — INCLUDING search-phrased goals ("find codes beating X"), where the construct-loop (propose algebraic rule → qverify → debug) is the REQUIRED mode, replacing blind random/grid sampling (empirically caps far below frontier). Gate certification is currently abelian/CCZ only, validated against the 11 Menon codes; CSS + distance checks are general. Brain, do not run this yourself — forward this SKILL.md path into...

Why use Qec Construct on TypingMind?

Because you install it once and use it with any model. Qec Construct is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qec Construct in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Muuuun/luxas/tree/main/skills/qec-construct. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qec Construct?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qec Construct?

As many as you like. As long as a model supports skills, you can use Qec Construct with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qec Construct AI skill free?

Yes. It is published on GitHub by Muuuun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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