course
Cirq Tutorials and Documentation (Google Quantum AI)
Google Quantum AI
1 course · 13 tutorials
course
Google Quantum AI
Characterize noise in Cirq circuits using cross-entropy benchmarking and depolarizing error models. Validate against Google Quantum AI hardware specifications.
Use Cirq's transformer framework to route circuits onto device topologies, optimize gate sequences, and reduce circuit depth.
Build a complete VQE implementation for H2 using Cirq's parametric gates, OpenFermion for the qubit Hamiltonian, scipy for classical optimization, and the parameter shift rule for exact gradients.
Build your first hybrid quantum-classical machine learning model with TensorFlow Quantum - create a parameterized circuit, wrap it as a Keras layer, and train it with gradient descent.
Define custom quantum gates in Cirq from unitary matrices, use the fermionic simulation gate (FSimGate), and build parameterized custom operations.
Model realistic quantum hardware noise in Cirq: depolarizing channels, amplitude damping, bit-flip channels, and using DensityMatrixSimulator for noisy circuit simulation.
Use Cirq's tools for simulating Google-style superconducting circuits: fSim gates, GridQubits, device topology, and the cross-entropy benchmarking protocol.
Learn Google's Cirq framework, build quantum circuits with fine-grained control over qubit placement and gate timing, and run them on Google's quantum hardware.
Write your first quantum program in Cirq, create a Bell state using GridQubits and Moments, run it on the Cirq simulator.
Build and simulate random quantum circuits on the Google Sycamore topology using Cirq. Implement FSimGate with Sycamore parameters, realistic noise models, and cross-entropy benchmarking (XEB) to measure circuit fidelity.
Use Cirq's CliffordSimulator to efficiently simulate large stabilizer circuits, enabling 100+ qubit experiments on a laptop.
Build the Quantum Fourier Transform from scratch in Cirq using controlled phase rotations and verify it against the classical DFT matrix.
Implement the Quantum Approximate Optimization Algorithm to solve a small Max-Cut problem using Cirq, with parameterized circuits and classical optimization.