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cirqlisted

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.
Yuuqq/research-grade-skills · ★ 0 · AI & Automation · score 73
Install: claude install-skill Yuuqq/research-grade-skills
# 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. ## Installation ```bash uv pip install cirq ``` For hardware integration: ```bash # Google Quantum Engine uv pip install cirq-google # IonQ uv pip install cirq-ionq # AQT (Alpine Quantum Technologies) uv pip install cirq-aqt # Pasqal uv pip install cirq-pasqal # Azure Quantum uv pip install azure-quantum cirq ``` ## 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() 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.histog