Learning angles for quantum approximate optimization
The Quantum Approximate Optimization Algorithm (QAOA) and its variants are leading algorithms to help solve challenging combinatorial optimization problems.
ProofPaper ↗
Key points
- QAOA maps these problems to Ising Hamiltonians such that their ground state is the desired solution.
- Candidate solutions are then drawn from a variational quantum circuit.
- The performance of QAOA hinges on a set of variational angles.
- Experiments on quantum hardware confirm that QAOA samples with angles produced by our open-source models are indistinguishable from solver angles.
Sources (1)
- [1]Learning angles for quantum approximate optimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:02 AM
The Quantum Approximate Optimization Algorithm (QAOA) and its variants are leading algorithms to help solve challenging combinatorial optimization problems.
QAOA maps these problems to Ising Hamiltonians such that their ground state is the desired solution.
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