Quantum Computing

A comprehensive reference skill for QuTiP (Quantum Toolbox in Python), providing documentation for simulating open quantum systems, master equations, and quantum optics. Includes detailed guides for time evolution solvers, visualization tools, and advanced features like Floquet theory and HEOM methods.

This skill provides extensive guidance for working with IBM's Qiskit quantum computing framework. It covers building quantum circuits, running simulations, executing on real IBM Quantum hardware, and implementing quantum algorithms like VQE, QAOA, and Grover's search. The skill includes reference documentation for setup, primitives, transpilation, visualization, and various quantum applications in chemistry, optimization, and machine learning.

This skill provides comprehensive documentation and code examples for PennyLane, a quantum computing library for training quantum circuits like neural networks. It covers quantum machine learning, chemistry simulations (VQE), optimization algorithms (QAOA), and integration with classical ML frameworks like PyTorch, JAX, and TensorFlow.

This skill provides comprehensive documentation and code examples for Google's Cirq quantum computing framework. It covers quantum circuit design, simulation, noise modeling, hardware integration with multiple providers (Google, IonQ, Azure, AQT, Pasqal), and experiment design patterns like VQE and QAOA.