Cirq Quantum Computing with Python
Skill Verifiziert AktivPart of the AlterLab Academic Skills suite. 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.
To empower users to design, simulate, and run quantum circuits targeting Google Quantum AI hardware by leveraging the Cirq framework and its associated tools.
Funktionen
- Circuit building with various qubit types and gates
- Exact and noisy quantum circuit simulation
- Parameter sweeps and analysis
- Circuit transformation and optimization
- Hardware integration with multiple providers
- Noise modeling and characterization
- Quantum experiment design and execution
- Support for advanced quantum algorithms (VQE, QAOA)
Anwendungsfälle
- Targeting Google Quantum AI hardware for circuit execution
- Designing noise-aware quantum circuits
- Running quantum characterization experiments
- Simulating complex quantum circuits with various noise models
- Optimizing circuits for specific hardware backends
Nicht-Ziele
- Providing a graphical user interface for circuit design
- Abstracting away the low-level details of quantum computation
- Competing with general-purpose quantum programming languages
Workflow
- Define quantum circuit using Cirq gates and operations.
- Optionally, add noise models or transformations.
- Simulate the circuit using Cirq simulators (state vector, density matrix, noisy).
- If targeting hardware, select qubits and optimize circuit for the device.
- Submit circuit to the chosen hardware provider (Google, IonQ, Azure, etc.).
- Analyze simulation or hardware results.
Praktiken
- Circuit Design
- Simulation
- Hardware Execution
- Circuit Optimization
- Noise Modeling
- Experiments
Voraussetzungen
- Python 3.7+
- uv pip or pip package manager
- CIRQ and related packages (cirq-google, azure-quantum, etc. if using hardware integration)
Trust
- info:Issues AttentionThere are 2 open issues and 0 closed issues in the last 90 days, indicating low recent activity and response.
Execution
- info:Pinned dependenciesThe installation instructions use `uv pip install`, which typically handles dependency resolution but does not explicitly guarantee pinned dependencies via a lockfile in the provided source.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
Ähnliche Erweiterungen
Cirq
98Google 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.
Qiskit
99IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
Qutip
99Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
Simulate Cpu Architecture
100Design and simulate a minimal CPU from scratch: define an instruction set architecture (ISA), build the datapath (ALU, register file, program counter, memory interface), design the control unit (hardwired or microprogrammed), implement the fetch-decode-execute cycle, and verify by tracing a small program clock cycle by clock cycle. The capstone "computer inside a computer" exercise that composes combinational and sequential building blocks into a complete processor.
Molecular Dynamics
99Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
COBRApy Metabolic Modeling
99Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.