Universal Quantum Electron Microscopy: A Small-Scale Quantum Computing Application with Provable Advantage

TL;DR

This paper introduces a quantum electron microscope that leverages quantum query complexity to reduce specimen damage, enabling more effective data extraction and demonstrating provable quantum advantage in imaging biological samples.

Contribution

It presents a novel design for a quantum electron microscope that utilizes quantum algorithms to minimize specimen damage while providing a provable advantage over classical methods.

Findings

Quantum microscopy reduces specimen damage compared to classical methods.

The proposed design enables Grover search for biological structures.

Quantum advantage is demonstrated through lower query complexity.

Abstract

We propose a simple design of a quantum electron microscope that ``queries'' a beam-sensitive phase object, such as a biological specimen, as part of quantum computation. Lower quantum query complexity, not the time complexity, of a quantum algorithm means less specimen damage, which translates to more data extracted from the specimen. Hence small-scale quantum computing offers provable quantum advantage in this context. A possible application of the proposed microscope is the Grover search for a true structure, out of a set of candidate structures.