Design, simulate, and execute quantum circuits with Google's Cirq framework.
npx skills add https://github.com/tassiovale/claude-code-kit --skill cirq-tassiovale
Please help me install this Agent Skill. Skill: cirq Source: https://github.com/tassiovale/claude-code-kit/tree/main/skills/cirq Command: npx skills add https://github.com/tassiovale/claude-code-kit --skill cirq-tassiovale
This Skill enables the design, simulation, and execution of quantum circuits, allowing users to work with Google Quantum AI hardware, model noise, and conduct quantum experiments.
To create a simple quantum circuit, execute the following command in your Python environment: import cirq; q0, q1 = cirq.LineQubit.range(2); circuit = cirq.Circuit(cirq.H(q0), cirq.CNOT(q0, q1), cirq.measure(q0, q1)); simulator = cirq.Simulator(); result = simulator.run(circuit, repetitions=1000); print(result.histogram(key='result')).
High-intent search queries and answers about installing and using this skill.
Design and simulate quantum circuits using Python by importing the Cirq library, defining LineQubits, applying gates like H and CNOT into a Circuit object, and running it with the Simulator to get measurement histograms.
Yes, you can run quantum circuits on actual quantum hardware platforms. Cirq supports execution on Google Quantum AI processors and partner backends including IonQ, Azure Quantum, AQT, and Pasqal.
Noise modeling simulates quantum circuit effects to mirror actual quantum hardware behavior. This allows accurate hardware integration, error mitigation, and realistic quantum experiment results during the simulation phase.
Yes, you need to install the Cirq library in your Python environment. It provides the comprehensive library of gates and operations required to build, simulate, and execute quantum circuits.
The best way to perform a parameter sweep is to design the quantum circuit in Cirq, simulate it using the built-in simulator to validate parameters, and then execute it on quantum hardware to gather results for analysis.
Cirq distinguishes itself through direct integration with Google Quantum AI hardware and partner backends like IonQ and Pasqal, combined with advanced noise modeling capabilities for accurate quantum simulation and error mitigation.
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