# Cirq — Quantum Circuit Framework by Google

> A Python framework by Google for creating, editing, and running quantum circuits on NISQ processors, with fine-grained control over gate placement and qubit topology.

## Install

Save in your project root:

# Cirq — Quantum Circuit Framework by Google

## Quick Use
```bash
pip install cirq
python -c "
import cirq
q = cirq.LineQubit.range(2)
circuit = cirq.Circuit(cirq.H(q[0]), cirq.CNOT(q[0], q[1]), cirq.measure(*q))
print(cirq.Simulator().simulate(circuit))
"
```

## Introduction
Cirq is a Python framework for writing, manipulating, and optimizing quantum circuits, then running them on simulators or real quantum hardware. Developed by Google Quantum AI, it gives researchers fine-grained control over gate placement and qubit topology for NISQ-era processors.

## What Cirq Does
- Defines quantum circuits with explicit qubit placement and gate scheduling
- Simulates circuits locally with state-vector and density-matrix simulators
- Compiles and runs circuits on Google's quantum processors via the Cloud API
- Provides noise models for realistic simulation of hardware behavior
- Supports circuit optimization through built-in and custom compiler passes

## Architecture Overview
Cirq models circuits as moment-based sequences of operations on typed qubits (LineQubit, GridQubit, or NamedQubit). Gates map to device-specific operations through a compilation pipeline. The framework includes simulators, device specifications, and serialization layers. Integration with Google's Quantum Computing Service allows execution on Sycamore and other processors.

## Self-Hosting & Configuration
- Install via pip: pip install cirq (or cirq-core for minimal dependencies)
- Optional extras: cirq-google, cirq-aqt, cirq-ionq for hardware backends
- Configure Google Cloud credentials for access to quantum hardware
- Use virtual environments to manage dependency versions
- Jupyter notebook support built in for interactive circuit exploration

## Key Features
- Moment-based circuit model with explicit operation scheduling
- Multiple qubit types reflecting real hardware topologies (grid, line, named)
- Built-in simulators including state-vector, density-matrix, and Clifford
- Hardware noise models for realistic NISQ device simulation
- Extensible compiler pipeline for device-specific circuit optimization

## Comparison with Similar Tools
- **Qiskit** — Broader ecosystem with more backends; Cirq offers finer control over gate placement and timing
- **PennyLane** — Focused on quantum ML with autodiff; Cirq is a lower-level circuit framework
- **PyQuil** — Targets Rigetti hardware; Cirq targets Google processors and is more actively maintained
- **Amazon Braket SDK** — Multi-hardware access via AWS; Cirq is open-source and hardware-agnostic in design

## FAQ
**Q: Can Cirq run on non-Google quantum hardware?**
A: Yes. Cirq supports AQT, IonQ, and Pasqal through optional integration packages, plus local simulation.

**Q: Is Cirq suitable for quantum machine learning?**
A: Cirq provides circuit primitives used by TensorFlow Quantum for QML, but PennyLane may be more ergonomic for pure ML workflows.

**Q: How does Cirq handle noise simulation?**
A: Cirq includes noise models that can be applied per-gate or per-device, simulating depolarization, dephasing, and readout errors.

**Q: What is the relationship between Cirq and TensorFlow Quantum?**
A: TensorFlow Quantum builds on Cirq, using Cirq circuits as the quantum computation layer within TensorFlow's ML pipeline.

## Sources
- https://github.com/quantumlib/Cirq
- https://quantumai.google/cirq

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Author: AI Open Source