cirq

Files (8)

• SKILL.md
• building.md · references/
• experiments.md · references/
• hardware.md · references/
• noise.md · references/
• review.md · references/
• simulation.md · references/
• transformation.md · references/

SKILL.md (first lines)

# Cirq - Quantum Computing with Python

Cirq is Google Quantum AI's open-source framework for designing, simulating, and running quantum circuits on quantum computers and simulators.

## When to Use This Skill

Use this skill when:
- Building, simulating, or optimizing NISQ circuits in Python
- Running jobs on Google Quantum AI processors (via `cirq-google`) or partner backends (IonQ, Azure Quantum, AQT, Pasqal)
- Modeling noise, compiling to hardware gatesets, or designing characterization experiments
- Using parameter sweeps, transformers, or the ReCirq experiment patterns

For IBM hardware use **qiskit**; for quantum ML with autodiff use **pennylane**; for physics simulations use **qutip**.

## Installation

Examples target **Cirq 1.7.0** on Python 3.11+. Keep Cirq vendor packages on the matching release; independently versioned integrations such as Azure Quantum need their own compatibility check.

```bash
uv pip install "cirq-core==1.7.0"
# Optional OpenQASM import dependency
uv pip install ply
```

For hardware integration (pin matching versions for reproducibility):
```bash
# Google Quantum Engine (requires approved GCP project access)
uv pip install "cirq-google==1.7.0"

# IonQ
uv pip install "cirq-ionq==1.7.0"

# AQT (Alpine Quantum Technologies)
uv pip install "cirq-aqt==1.7.0"

# Pasqal
uv pip install "cirq-pasqal==1.7.0"

# Azure: use a SEPARATE environment; its current extras require Cirq <1.7.
uv pip install "qdk[azure,cirq]==1.32.3" "azure-quantum==3.13.0"
```

Pin all `cirq-*` packages in an environment to the same release. The Azure
example targets its supported Cirq 1.6.1 stack separately; do not force Cirq 1.7.0
into it. Cirq 1.7.0 updates the IonQ adapter to API v0.4.

Local examples were checked with synthetic circuits. Hardware examples are
illustrative and were checked against released source and provider documentation,
without submitting cloud jobs. See [review evidence](references/review.md).

## Quick Start

### Basic Circuit

```python
import cirq
import numpy as np

# Create qubits
q0, q1 = cirq.LineQubit.range(2)

# Build circuit
circuit = cirq.Circuit(
    cirq.H(q0),              # Hadamard on q0
    cirq.CNOT(q0, q1),       # CNOT with q0 control, q1 target
    cirq.measure(q0, q1, key='result')
)

print(circuit)

# Simulate
simulator = cirq.Simulator(seed=42)
result = simulator.run(circuit, repetitions=1000)

# Display results
print(result.histogram(key='result'))
```

### Parameterized Circuit

```python
import sympy

# Define symbolic parameter
theta = sympy.Symbol('theta')

# Create parameterized circuit
circuit = cirq.Circuit(
    cirq.ry(theta)(q0),
    cirq.measure(q0, key='m')
)

# Sweep over parameter values
sweep = cirq.Linspace('theta', start=0, stop=2*np.pi, length=20)
results = simulator.run_sweep(circuit, params=sweep, repetitions=1000)

# Process results
for params, result in zip(sweep, results):
    theta_val = params['theta']
    counts = result.histogram(key='m')
    print(f"θ={theta_val:.2f}: {counts}")
```

## Core Capabilities

### Circuit Building
For comprehensive information about building quantum circuits, including qubits, gates, operations, custom gates, and circuit patterns, see:
- **[references/building.md](references/building.md)** - Complete guide to circuit construction

Common topics:
- Qubit types (GridQubit, LineQubit, NamedQubit)
- Single and two-qubit gates
- Parameterized gates and operations
- Custom gate decomposition
- Circuit organization with moments
- Standard circuit patterns (Bell states, GHZ, QFT)
- Import/export (OpenQASM, JSON)
- Working with qudits and observables

From K-Dense-AI/scientific-agent-skills, MIT.

More from K-Dense-AI/scientific-agent-skills