Design, simulate, and execute quantum circuits with a Python library.
npx skills add https://github.com/robinbarvaag/poynt --skill pennylane-robinbarvaag
Please help me install this Agent Skill. Skill: pennylane Source: https://github.com/robinbarvaag/poynt/tree/main/.github/skills/pennylane Command: npx skills add https://github.com/robinbarvaag/poynt --skill pennylane-robinbarvaag
This Skill provides a comprehensive toolkit for developing and executing quantum circuits, enabling advanced applications in quantum machine learning, quantum chemistry, and optimization.
Use the pennylane skill to build and run a simple quantum circuit with two qubits and a CNOT gate, returning the expectation value of PauliZ on the first qubit.
High-intent search queries and answers about installing and using this skill.
To compute ground state energy, you define the molecular Hamiltonian, build a VQE ansatz, and run the optimization on a quantum simulator. This approach enables you to simulate molecular structures and calculate their fundamental properties.
You can train quantum neural networks and hybrid models by integrating with classical machine learning frameworks. This integration provides the necessary tools for gradient computation to optimize your quantum circuits.
To solve optimization problems using QAOA, you build and simulate quantum circuits through a Pythonic interface. This allows you to design and execute algorithms tailored for complex optimization scenarios on various backends.
Yes, execution supports various simulators and quantum hardware backends. You can design and test your quantum circuits locally before deploying them to actual quantum devices for execution.
You can build a simple quantum circuit with two qubits and a CNOT gate, then return the expectation value of PauliZ on the first qubit. This provides a straightforward entry point for designing and simulating circuits.
K-Dense-AIOptimize quantum circuits with automatic differentiation across simulators and hardware.
JosephWoodallDesign and run quantum circuits on IBM Quantum hardware using the Qiskit SDK.
biomaps-infraTrain quantum circuits via gradients with PyTorch, JAX, and TensorFlow integration.
Lord1EgyptBuild and optimize variational quantum circuits with automatic differentiation.
