Improving quantum state detection with adaptive sequential observations

TL;DR

This paper explores how adaptive, sequential observations and transformations can improve quantum state detection accuracy, demonstrating advantages through models and ion qubit examples, and providing software tools for strategy exploration.

Contribution

It introduces adaptive transformation strategies during quantum measurements, showing improved detection performance over traditional fixed methods.

Findings

Adaptive transformations improve detection accuracy in some regimes.

Temporal structure in photon detection can be exploited for better state discrimination.

Software tools are provided for exploring adaptive measurement strategies.

Abstract

For many quantum systems intended for information processing, one detects the logical state of a qubit by integrating a continuously observed quantity over time. For example, ion and atom qubits are typically measured by driving a cycling transition and counting the number of photons observed from the resulting fluorescence. Instead of recording only the total observed count in a fixed time interval, one can observe the photon arrival times and get a state detection advantage by using the temporal structure in a model such as a Hidden Markov Model. We study what further advantage may be achieved by applying pulses to adaptively transform the state during the observation. We give a three-state example where adaptively chosen transformations yield a clear advantage, and we compare performances on an ion example, where we see improvements in some regimes. We provide a software package that can be used for exploration of temporally resolved strategies with and without adaptively chosen transformations.