# Quantum Qiskit

Reference qiskit 2.x patterns for variational quantum machine learning.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill quantum-qiskit -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill quantum-qiskit -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill quantum-qiskit -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill quantum-qiskit -g -a gemini-cli -y`

## Description

Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigensolver (VQE) for chemistry, matrix-product-state circuits, and noise model integration. Use when writing Python code that imports `qiskit`, `qiskit_aer`, `qiskit_algorithms`, `qiskit_machine_learning`, or `qiskit_nature`.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Qiskit 2.x reference for variational quantum machine learning

This skill is a canonical reference for writing Python code that uses
qiskit 2.x and its ecosystem (`qiskit_aer`, `qiskit_algorithms`,
`qiskit_machine_learning`, `qiskit_nature`). It documents the API shapes
that work in qiskit 2.x today, the qiskit-1.x → 2.x migration breaks
that affect VQE and chemistry code, and a small number of common
mistakes with concrete fixes.

Section overview:

1. Imports
2. Data-encoding feature maps
3. Variational ansatz construction
4. VQC training (qiskit_machine_learning)
5. VQE for chemistry (qiskit 2.x compatible)
6. MPS-structured circuits
7. Noise model integration
8. qiskit 2.x compatibility notes
9. Common errors and fixes
10. Autoclaw integration: metric logging convention

---

## 1. Imports

```python
import numpy as np
from qiskit import QuantumCircuit
from qiskit.circuit import ParameterVector
from qiskit.circuit.library import (
    ZFeatureMap,
    ZZFeatureMap,
    StatePreparation,
    EfficientSU2,
)
from qiskit.primitives import StatevectorSampler, StatevectorEstimator  # V2 primitives
from qiskit.quantum_info import Statevector, SparsePauliOp
from qiskit_aer import AerSimulator
from qiskit_algorithms.optimizers import SPSA, COBYLA, L_BFGS_B, ADAM
from qiskit_algorithms.utils import algorithm_globals
from qiskit_machine_learning.algorithms.classifiers import VQC
```

For chemistry:

```python
from qiskit_nature.units import DistanceUnit
from qiskit_nature.second_q.drivers import PySCFDriver
from qiskit_nature.second_q.mappers import ParityMapper, JordanWignerMapper
```

Do not import from `qiskit_nature.second_q.algorithms` or
`qiskit_algorithms.VQE` under qiskit 2.x (they fail at import time, see
section 8).

---

## 2. Data-encoding feature maps

Three standard families. Each builder returns a parameterized circuit
suitable for use as the `feature_map` argument of `VQC` or for direct
contraction with a variational ansatz.

The rest: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/researchclaw/skills/builtin/domain/quantum-qiskit/SKILL.md