cirq
Work with Google's Cirq quantum computing framework
Also available from: davila7
Design and simulate quantum circuits using Google's Cirq framework. This skill provides comprehensive documentation for building quantum circuits, noise modeling, hardware integration, and running experiments on quantum computers and simulators.
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Using "cirq". Create a quantum circuit that creates a GHZ state with 3 qubits and measures all qubits.
Expected outcome:
- Here's how to create a GHZ (Greenberger-Horne-Zeilinger) state with 3 qubits:
- • Use cirq.LineQubit to create 3 qubits
- • Apply Hadamard to first qubit to create superposition
- • Apply CNOT gates to entangle all qubits in a chain
- • Measure all qubits with a descriptive key
- The circuit creates the state |000⟩ + |111⟩/√2, demonstrating genuine 3-qubit entanglement.
Using "cirq". How do I add noise to my quantum circuit for realistic simulation?
Expected outcome:
- Cirq provides several ways to model noise:
- • Add noise to specific gates: circuit.with_noise(cirq.depolarize(p=0.01))
- • Use noise channels: cirq.amplitude_damp(delta)(qubit)
- • Create custom noise models based on calibration data
- Noisy simulation helps predict how your circuit will perform on real quantum hardware with imperfect qubits.
Security Audit
SafeAll 343 static findings are FALSE POSITIVES. This is a pure documentation skill containing markdown files with Python code examples for Google's Cirq quantum computing library. The scanner triggered on documentation patterns (markdown code blocks, quantum gate names like CNOT/CZ, measurement key parameters) that it misinterpreted as security issues. No executable code, network connections, credential exfiltration, or malicious patterns exist.
Risk Factors
⚙️ External commands (8)
🌐 Network access (2)
🔑 Env variables (1)
Quality Score
What You Can Build
Design quantum experiments
Design and simulate experiments with parameterized circuits, noise models, and data collection for quantum research publications.
Build quantum algorithms
Implement quantum algorithms like VQE, QAOA, and QPE using Cirq's circuit building and transformation framework.
Target quantum processors
Compile and optimize circuits for specific quantum processors from Google, IonQ, Azure, and other providers.
Try These Prompts
Show me how to create a Bell state circuit with Cirq using two qubits, a Hadamard gate, and CNOT gate with measurement.
Help me add depolarizing noise to a quantum circuit and run noisy simulations to analyze error impact.
How do I configure Cirq to run circuits on Google Quantum AI hardware and select the best qubits based on calibration data?
Transform my circuit to use only the native gateset for a specific quantum processor and reduce circuit depth.
Best Practices
- Start with simulators before running on expensive quantum hardware time
- Use circuit optimization transformers to reduce depth before hardware execution
- Label measurements with descriptive keys for easier result analysis
- Save experiment results immediately after hardware runs to prevent data loss
Avoid
- Running complex circuits without first validating on simulators
- Ignoring device topology constraints when selecting qubits
- Skipping noise characterization before error mitigation
- Not using qubit calibration data for hardware qubit selection