Modeling Quantum Algorithm Performance on Early Fault-Tolerant Quantum Computers

Abstract

Will quantum computing become useful in the early fault-tolerant era (EFTQC)? To answer this question, we must have accurate models of such quantum computers and of algorithm performance. In the EFTQC regime, qubits will be noisy, and quantum error correction will be expensive. Hence, it is also important to analyze the cost of quantum error correction needed to achieve a certain level of performance on the quantum computer. Toward these goals, we develop a modularized framework to model the circuit-level noise, the algorithmic noise, the algorithm success probability, and the links between them. Specifically, we apply our framework to study the performance of a phase estimation algorithm. We analyze the error correction cost for this algorithm as a function of the target error rate, physical error rate, and error-correcting code properties. Our work lays the foundation for assessing the interplay between hardware and algorithmic performances in the early fault-tolerant era and beyond.

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.