Evolutionary Optimization of State Selective Field Ionization for Quantum Computing

TL;DR

This paper demonstrates that in situ optimization of state selective field ionization in quantum computing can be achieved using genetic algorithms, improving detector performance despite complex atomic state dynamics and experimental deviations.

Contribution

It introduces a genetic algorithm-based method for optimizing field ionization profiles directly during experiments, addressing computational and experimental challenges.

Findings

Successful in situ optimization of field profiles

Generation of novel ionization results

Consistency with existing theoretical analyses

Abstract

State selective field ionization detection techniques in physics require a specific progression through a complicated atomic state space to optimize state selectivity and overall efficiency. For large principle quantum number n, the theoretical models become computationally intractable and any results are often rendered irrelevant by small deviations from ideal experimental conditions, for example external electromagnetic fields. Several different proposals for quantum information processing rely heavily upon the quality of these detectors. In this paper, we show a proof of principle that it is possible to optimize experimental field profiles in situ by running a genetic algorithm to control aspects of the experiment itself. A simple experiment produced novel results that are consistent with analyses of existing results.