Zichang He, JPMorganChase

Abstract

The rapid progress in quantum hardware is expected to make them viable tools for the study of quantum algorithms in the near term. To accelerate the timeline of useful algorithmic experimentation, one effective technique is to encode the quantum circuit using an error detection code and discard the samples for which an error has been detected. An under-explored property of error-detecting codes is the flexibility in the circuit encoding and fault-tolerant gadgets, which enables their co-optimization with the algorithmic circuit, which is not available for standard circuit optimization tools. In this work, we focus on the [[k+2, k, 2]] Iceberg quantum error detection code and design new flexible fault-tolerant gadgets for it, which we then co-optimize with the quantum approximate optimization algorithm (QAOA) circuit using tree search. By co-optimizing the QAOA circuit and the Iceberg gadgets, we achieve an improvement in QAOA success probability from 44% to 65% and an increase in post-selection rate from 4% to 33% at 22 algorithmic qubits. Furthermore, we demonstrate better-than- unencoded performance for up to 34 algorithmic qubits, employing 510 algorithmic two-qubit gates and 1140 physical two-qubit gates on the Quantinuum H2-1 quantum computer. In addition, we will present an analytical model to characterize the applicability of quantum error detection code on the QAOA circuit and predict its performance in the future device.

Speaker bio: Dr. Zichang He is the Applied Research Lead (Vice President) at the Global Technology Applied Research Center at JPMorganChase. He earned his Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2023. His research primarily focuses on quantum computing and its design automation. He is the recipient of the IEE Excellence in Research Fellowship at UCSB and has received two Best Student Paper Awards at IEEE conferences.