Efficient motional-mode characterization for high-fidelity trapped-ion quantum computing

Abstract

To achieve high-fidelity operations on a large-scale quantum computer, the parameters of the physical system must be efficiently characterized with high accuracy. For trapped ions, the entanglement between qubits are mediated by the motional modes of the ion chain, and thus characterizing the motional-mode parameters becomes essential. In this paper, we develop and explore physical models that accurately predict both magnitude and sign of the Lamb–Dicke parameters when the modes are probed in parallel. We further devise an advanced characterization protocol that shortens the characterization time by more than an order of magnitude, when compared to that of the conventional method that only uses mode spectroscopy. We discuss potential ramifications of our results to the development of a scalable trapped-ion quantum computer, viewed through the lens of system-level resource trade offs.

Qiyao (Catherine) Liang

PhD student at MIT EECS

I’m a second-year PhD student in the Electrical Engineering and Computer Science department at MIT. My primary interest is in the intersection of physics, AI, and neuroscience. I’m advised by Ila Fiete from the MIT Brain and Cognitive Science department. Some of my recent interests are understanding the mechanisms of compositional generalization in generative models, how structural and/or functional modularity emerge within artificial and biological systems, and beyond. I’m interested in a broad range of topics regarding studying the principles of artificial/biological intelligence and consciousness as emergent phenomena, via quantitative tools from physics as well as empirical studies. I completed my undergraduate studies at Duke University in physics and math, where I worked on controlling and denoising quantum computers.