In this work we discuss the progress of commissioning the next-generation trapped-ion quantum computing system. Heating rates, coherence times, background gas collision rates, individual-addressing cross-talk and other factors contributing to the gate fidelity are discussed.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

https://export.arxiv.org/pdf/2312.00875v1.pdf

A Perspective on Protein Structure Prediction Using Quantum Computers

Next-generation trapped-ion quantum computing system

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Next-generation trapped-ion quantum computing system

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A Perspective on Protein Structure Prediction Using Quantum Computers