Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR

TL;DR

This paper presents a hierarchical genetic algorithm that efficiently determines optimized pulse sequences for quantum gates in NMR systems, significantly improving efficiency and power consumption over previous methods.

Contribution

The paper introduces a novel hybrid genetic algorithm framework with hierarchical matrices for designing efficient NMR pulse sequences for quantum gates.

Findings

Achieved about 50% improvement in efficiency for Parity and Fanout gates.

Sequences require significantly less RF power.

Algorithm converges rapidly, producing sequences in about 20 minutes on a standard PC.

Abstract

We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast convergence, and running on a MATLAB platform takes about 20 minutes on a standard personal computer to derive efficient pulse sequences for any target 8X8 matrix $U$.