Fast gradient-free optimization of excitations in variational quantum eigensolvers
Jonas J\"ager, Thierry Nicolas Kaldenbach, Max Haas, Erik Schultheis

TL;DR
ExcitationSolve is a novel, fast, gradient-free optimizer tailored for variational quantum eigensolvers, leveraging physical excitation operators to efficiently find molecular ground states with improved convergence and noise robustness.
Contribution
It introduces ExcitationSolve, a globally-informed, gradient-free optimizer compatible with excitation-based unitaries, enhancing quantum chemistry calculations on quantum computers.
Findings
Outperforms existing optimizers in convergence speed.
Achieves chemical accuracy in a single parameter sweep.
Produces shallower adaptive ans"atze and is noise-robust.
Abstract
Finding molecular ground states and energies with variational quantum eigensolvers is central to chemistry applications on quantum computers. Physically motivated ans\"atze based on excitation operators respect physical symmetries, but existing quantum-aware optimizers, such as Rotosolve, have been limited to simpler operator types. To fill this gap, we introduce ExcitationSolve, a fast quantum-aware optimizer that is globally-informed, gradient-free, and hyperparameter-free. ExcitationSolve extends these optimizers to parameterized unitaries with generators of the form exhibited by excitation operators in approaches such as unitary coupled cluster. ExcitationSolve determines the global optimum along each variational parameter using the same quantum resources that gradient-based optimizers require for one update step. We provide optimization strategies for both fixed and…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography
