Quantum-enhanced algorithms for classical target detection in complex environments

TL;DR

This paper explores quantum algorithms for classical target detection in complex environments, focusing on radar image analysis, and identifies potential quantum speedups and implementation challenges.

Contribution

It introduces novel quantum algorithm extensions for target detection, including quantum analog-digital conversion and quantum statistical methods, enhancing classical radar processing.

Findings

Quantum covariance matrix estimation enables efficient quantum implementation.

Quantum algorithms can potentially speed up target detection in cluttered environments.

Identification of bottlenecks like data loading and conversion for quantum radar algorithms.

Abstract

Quantum computational approaches to some classic target identification and localization algorithms, especially for radar images, are investigated, and are found to raise a number of quantum statistics and quantum measurement issues with much broader applicability. Such algorithms are computationally intensive, involving coherent processing of large sensor data sets in order to extract a small number of low profile targets from a cluttered background. Target enhancement is accomplished through accurate statistical characterization of the environment, followed by optimal identification of statistical outliers. The key result of the work is that the environmental covariance matrix estimation and manipulation at the heart of the statistical analysis actually enables a highly efficient quantum implementation. The algorithm is inspired by recent approaches to quantum machine learning, but requires significant extensions, including previously overlooked `quantum analog--digital' conversion steps (which are found to substantially increase the required number of qubits), `quantum statistical' generalization of the classic phase estimation and Grover search algorithms, and careful consideration of projected measurement operations. Application regimes where quantum efficiencies could enable significant overall algorithm speedup are identified. Key possible bottlenecks, such as data loading and conversion, are identified as well.