Fermi-Hubbard VQE Optimizer Benchmark
Comprehensive benchmark of 30 classical optimizers on 372 instances of VQE for the Fermi-Hubbard system using the Hamiltonian variational ansatz. Best-performing optimizers are gradient-descent variants (Momentum, ADAM with finite differences), SPSA, CMA-ES, and BayesMGD. The study demonstrates that finite-difference step size has a very significant impact on VQE convergence quality.[1]
- Algorithm: VQE with Hamiltonian Variational Ansatz
- Category: simulation
- Qubits: 12
- Framework: Qiskit, PennyLane
- Reproducible: Yes
- Published:
- Fermi-Hubbard
- VQE
- optimizer-benchmark
- condensed-matter
- simulation
What algorithm does Fermi-Hubbard VQE Optimizer Benchmark use?
Fermi-Hubbard VQE Optimizer Benchmark uses the VQE with Hamiltonian Variational Ansatz algorithm, categorized under simulation.
Frequently Asked Questions
What is the Fermi-Hubbard VQE Optimizer Benchmark benchmark?
Comprehensive benchmark of 30 classical optimizers on 372 instances of VQE for the Fermi-Hubbard system using the Hamiltonian variational ansatz. Best-performing optimizers are gradient-descent variants (Momentum, ADAM with finite differences), SPSA, CMA-ES, and BayesMGD. The study demonstrates that finite-difference step size has a very significant impact on VQE convergence quality.
Is Fermi-Hubbard VQE Optimizer Benchmark reproducible?
Yes, this benchmark is reproducible.