Learn CIRQ with Real Code Examples

Simulation cost grows exponentially with number of qubits

Noise simulation slows down performance

Parallel simulation supported via multiprocessing

Gate optimization can improve execution efficiency

Circuit depth affects execution on real hardware

Keep cloud API tokens secure

Avoid unnecessary runs on paid quantum devices

Ensure reproducibility with fixed random seeds

Validate circuit logic before running on hardware

Use classical post-processing for sensitive computations

Monitor simulator or hardware job execution

Track measurement outcomes and statistics

Visualize circuit states and probabilities

Audit experiments and logs

Compare simulator and hardware results

Follow Python and Cirq coding best practices

Document circuits and algorithms

Maintain reproducibility for experiments

Simulate circuits before hardware execution

Optimize circuits for minimal depth and gate usage