gala-noise-modeling
Model and simulate noise in Gala — typed noise models, QuEST noise channels, error mitigation
Skill by gala-pl
Chalk Labs
Need help deploying gala-noise-modeling?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/gala-pl-gala-noise-modeling
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/gala-noise-modeling
Tools it pre-approves 10
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
Bash(cargorun--bingala*)ReadWriteEditGlobGrep
What's inside
- Skill name
gala-noise-modeling- Description
- Model and simulate noise in Gala — typed noise models, QuEST noise channels, error mitigation
- License
- Apache-2.0
- Allowed tools
- Bash(cargo, run, --bin, gala, *), Read, Write, Edit, Glob, Grep
- Instructions
- 227 lines in SKILL.md · view on GitHub
Before installing
- Pre-approves some shell commands
Files (1)
- SKILL.md
SKILL.md (first lines)
## Task
$ARGUMENTS
## Overview
Gala treats **noise models as typed first-class values** (`gala.noise`). This enables compile-time noise-aware development and seamless transition from ideal → noisy simulator → hardware.
## Noise Model Types
```gala
import gala.noise.{ NoiseModel, depolarizing, amplitude_damping, phase_damping, thermal_relaxation, custom }
```
### Built-in Models
```gala
// Single-qubit depolarizing: ρ → (1-p)ρ + p/3 (XρX + YρY + ZρZ)
depolarizing(prob: Float) -> NoiseModel
// Amplitude damping (T1 decay): |1⟩ → |0⟩ with prob γ
amplitude_damping(gamma: Float) -> NoiseModel
// Phase damping (T2 dephasing): off-diagonal decay
phase_damping(gamma: Float) -> NoiseModel
// Combined T1/T2 thermal relaxation
thermal_relaxation(t1: Float, t2: Float, gate_time: Float) -> NoiseModel
```
### Composite Models
```gala
// Per-qubit noise (different per qubit)
let noise = NoiseModel::per_qubit([
depolarizing(0.001), // q0
depolarizing(0.002), // q1
amplitude_damping(0.01), // q2
])
// Per-gate noise (different per gate type)
let noise = NoiseModel::per_gate({
"cx": depolarizing(0.01),
"rz": phase_damping(0.001),
"measure": NoiseModel::readout_error(0.02, 0.03), // P(0|1), P(1|0)
})
// Spatially correlated (crosstalk)
let noise = NoiseModel::correlated([
(depolarizing(0.001), [0, 1]), // Correlated on q0,q1
])
```
## Using Noise Models
### Noisy Simulator Backend
```gala
fn main() {
let noise = depolarizing(0.01)
let backend = backend.noisy(noise)
let result = run(algorithm, on: backend, shots: 10000)
}
```
### CLI
```bash
# Depolarizing
gala run src/main.gala --noise depolarizing:0.01 --shots 10000
# Amplitude damping
gala run src/main.gala --noise amplitude_damping:0.05
# From file
gala run src/main.gala --noise-file noise_model.toml
```
### Noise Model File (TOML)
```toml
# noise_model.toml
[global]
depolarizing = 0.001
[per_qubit]
0 = { depolarizing = 0.0005 }
1 = { amplitude_damping = 0.01 }
[per_gate]
"cx" = { depolarizing = 0.01 }
"measure" = { readout_error = [0.02, 0.03] }
```
## Error Mitigation
### Zero-Noise Extrapolation (ZNE)
```gala
import gala.mitigation.{ zne_extrapolate, richardson_extrapolate }
fn mitigate_with_zne(circuit: fn() -> Measured<Bool> prob, noise_scales: [Float]) -> Float {
// Run at different noise levels (stretch gates)
let expectations = noise_scales.map(|scale| {
let scaled_noise = scale_noise(base_noise, scale)
let backend = backend.noisy(scaled_noise)
run(circuit, on: backend, shots: 1000).expectation()
})
// Extrapolate to zero noise
richardson_extrapolate(noise_scales, expectations)
}
```
### Readout Error Mitigation
```gala
import gala.mitigation.{ readout_mitigation, calibration_matrix }
// Calibrate once per backend
let cal_matrix = calibration_matrix(backend.hardware("ionq.aria"))
// Apply to results
fn mitigate_readout(raw: Measured<Bool>, cal: Matrix) -> Measured<Bool> {
readout_mitigation(raw, cal)
}
```From gala-pl/gala, Apache-2.0.
More from gala-pl/gala
- 0.584score
gala-diff
gala-pl
Work with Gala's native differentiation — parameter-shift for quantum params, reverse-mode autodiff for classical, chain-rule composition
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-hardware-backends
gala-pl
Run Gala programs on quantum hardware — backend selection, capability checking, QIR emission, vendor SDKs
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-language-basics
gala-pl
Learn Gala language fundamentals — syntax, types, effects, linear qubits, basic quantum operations
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-performance
gala-pl
Optimize Gala program performance — gate count, circuit depth, gradient cost, compilation time
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-python-interop
gala-pl
Call Gala from Python — PyO3 bridge, Jupyter kernel, PyTorch integration, NumPy arrays
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-quantum-algorithms
gala-pl
Implement quantum algorithms in Gala — QFT, Grover, phase estimation, adders, oracles
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-vqa-programming
gala-pl
Build variational quantum algorithms in Gala — encoders, ansätze, optimizers, native grad
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-ast
gala-pl
Work with Gala's AST — add node types, implement visitors, maintain CST/AST separation
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-codegen-classical
gala-pl
Work with Gala's classical codegen — Cranelift (debug) and LLVM (release) for orchestration layer
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-driver
gala-pl
Work with Gala's compilation driver — salsa DB orchestration, incremental queries, pipeline wiring
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
debug-compiler
gala-pl
Debug a compiler error or unexpected behavior in Gala source code
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-cookbook
gala-pl
Gala recipes — common patterns, idioms, algorithms, VQA templates, error handling
SkillAFreshness: A★ 1Claude Code +9
Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 23, 2026 (7 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/gala-pl/gala
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from gala-pl
- 0.584score
gala-diff
gala-pl
Work with Gala's native differentiation — parameter-shift for quantum params, reverse-mode autodiff for classical, chain-rule composition
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-language-basics
gala-pl
Learn Gala language fundamentals — syntax, types, effects, linear qubits, basic quantum operations
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-performance
gala-pl
Optimize Gala program performance — gate count, circuit depth, gradient cost, compilation time
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-python-interop
gala-pl
Call Gala from Python — PyO3 bridge, Jupyter kernel, PyTorch integration, NumPy arrays
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-quantum-algorithms
gala-pl
Implement quantum algorithms in Gala — QFT, Grover, phase estimation, adders, oracles
SkillAFreshness: A★ 1Claude Code +9 - 0.581score
gala-hardware-backends
gala-pl
Run Gala programs on quantum hardware — backend selection, capability checking, QIR emission, vendor SDKs
SkillAFreshness: A★ 1Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get gala-noise-modeling running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
gala-noise-modeling skill. Agent Mart. https://agentmart.dev/skills/gala-pl-gala-noise-modeling (last updated 2026-10-24)