Solve linear systems of equations using the HHL algorithm on a quantum computer.
npx skills add https://github.com/unitarylab/quantum-skills --skill hhl
Please help me install this Agent Skill. Skill: hhl Source: https://github.com/unitarylab/quantum-skills/tree/main/algorithms/linear-systems/hhl Command: npx skills add https://github.com/unitarylab/quantum-skills --skill hhl
Solve linear systems of equations on a quantum computer using the HHL algorithm.
Run the 2x2 HHL example script to solve Ax = b and view the results.
High-intent search queries and answers about installing and using this skill.
The HHL algorithm solves linear systems by using quantum phase estimation to extract the eigenstructure of a Hermitian matrix, then applying controlled rotations to invert the eigenvalues, yielding exponential speedups for suitable sparse systems.
Yes, the HHL implementation serves as a reusable subroutine for quantum simulation and optimization workflows, providing a reference implementation that can be integrated to solve Ax = b as a component within larger quantum algorithms.
Run the provided 2x2 HHL example script to solve Ax = b and view the results. This quick start script demonstrates the core quantum linear system workflow and requires a quantum simulation backend plus numerical libraries like numpy.
The HHL algorithm is applicable to Hermitian systems of moderate size where the eigenstructure allows efficient phase estimation. It is best suited for demonstrations, teaching, and moderate-scale integration rather than arbitrary large-scale problems.
Yes, you need numpy for numerical libraries and a quantum simulation backend to run the HHL workflow. These dependencies are required to execute the quantum phase estimation and matrix inversion subroutines that solve the linear system.
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