Estimate ground-state energies for 2-qubit Ising Hamiltonians using VQE with COBYLA.
npx skills add https://github.com/unitarylab/quantum-skills --skill vqe
Please help me install this Agent Skill. Skill: vqe Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms/quantum-machine-learning/vqe Command: npx skills add https://github.com/unitarylab/quantum-skills --skill vqe
Serves as a practical pathway to estimate ground-state energies for small quantum systems using a variational quantum eigensolver (VQE) in a 2-qubit Ising model.
Execute the example script to run a VQE on the 2-qubit Ising model.
High-intent search queries and answers about installing and using this skill.
VQE estimates ground-state energies by running a hybrid quantum-classical loop. A parameterized quantum circuit prepares a trial state, and a classical optimizer like COBYLA adjusts parameters to minimize the measured energy expectation value until convergence.
Variational quantum eigensolver optimization for small Hamiltonians works by combining a quantum circuit simulator with the COBYLA optimizer. It uses an Ry+CX ansatz to prepare trial states and evaluates statevectors to minimize energy for educational demonstrations.
Yes, you can use COBYLA to optimize ansatz parameters in a quantum circuit simulation. This Skill specifically uses COBYLA to minimize energy expectation values derived from an Ry+CX ansatz evaluated via statevector simulation.
The best way to compare approximate VQE results to exact spectra is to run a small Hamiltonian simulation that outputs both values. This Skill calculates exact ground-state energies alongside VQE optimization results for direct comparison.
Yes, running VQE demonstrations requires a quantum circuit simulator to evaluate the ansatz statevector. This Skill depends on a simulator backend to compute energy expectation values for the Hamiltonian during the optimization loop.
You should not use VQE for large-scale Hamiltonians or systems requiring deep circuits, as the Ry+CX ansatz and COBYLA optimizer here are tailored for small 2-qubit Ising models. It is intended for educational demos, not large-scale research simulations.
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