Learn CIRQ with Real Code Examples

Simulate a Bell state circuit

Run Grover's search algorithm on a simulator

Implement variational quantum circuits for optimization

Test algorithms on Google Sycamore hardware

Visualize circuit diagrams and measurement results

Check qubit definitions and dimensions match

Ensure simulator or backend is properly instantiated

Validate gate compatibility with NISQ hardware

Debug circuit errors using simplified simulator runs

Check measurement results and correct indexing

Simulate circuits locally before running on hardware

Use noise models to test algorithm robustness

Compare results between simulator and hardware

Visualize measurement distributions

Validate algorithm correctness step by step

Run experiments on local simulator

Execute jobs on Google Quantum Engine

Test hybrid classical-quantum optimization pipelines

Batch multiple circuits for parallel execution

Integrate with ML workflows for hybrid tasks

Cirq core library for circuits and gates

Cirq Simulator for classical simulation

Cirq Google Engine interface for hardware execution

Cirq contrib modules for visualization

Cirq optimizers for circuit simplification

Google Quantum Engine for real-device access

Python scientific libraries (NumPy, SciPy, Matplotlib)

Classical optimization frameworks

TensorFlow Quantum for hybrid ML pipelines

Jupyter notebooks for interactive experimentation

Start with local simulation before hardware execution

Visualize circuits to debug quickly

Modularize gates and subcircuits for reuse

Cache results for reproducibility

Use Cirq optimizers for circuit efficiency

Handling noise on NISQ devices

Optimizing circuit depth and gate count

Scaling simulations to larger qubit numbers

Interpreting quantum measurement outcomes

Integrating quantum and classical computation