Year: 2024
Type: preprint
Abstract: This paper addresses frequency crowding in SNAIL-based superconducting quantum modules. First, we present design constraints by describing a physical model for realizable gates within a module, and building a fidelity model using error budgeting derived from device characteristics. Second, we tackle the allocation problem by analyzing the impact of frequency crowding on gate fidelity as the radix of the module increases. We explore whether the heuristic gate fidelity can be optimized with a discrete set of qubit frequencies while adhering to defined separation thresholds. By leveraging a combination of analytical and numerical techniques, we demonstrate scalable frequency allocation strategies that minimize spectator-induced errors. Our results further indicate that removing edges leads to improved gate fidelities while maintaining sufficient connectivity, suggesting that edge density is not a limiting factor for NISQ-scale benchmarks. The findings have implications for designing robust, high-fidelity quantum systems with practical constraints on hardware and connectivity.
Source: arXiv (Cornell University)
Authors: McKinney, Evan, Yusuf, Israa G., Falstin, Girgis, Agarwal, Gaurav, Hatridge, Michael, Jones, Alex K.
Institutions: University of Pittsburgh, Yale University, Syracuse University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Army Research Office
OpenAlex ID: W4403809959