Fermi-Hubbard VQE Optimizer Benchmark
VQE with Hamiltonian Variational Ansatz · Simulation · 12 qubits · Qiskit, PennyLane
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.
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.
Largest systematic comparison of classical optimizers for VQE, revealing that finite-difference step size is the dominant factor in convergence quality.
Simulator / hardware-agnostic
Qiskit, PennyLane