Optimizing frequency allocation for fixed-frequency superconducting quantum processors

TL;DR

This paper introduces an optimization method using mixed-integer programming to select qubit frequencies in superconducting quantum processors, significantly improving fabrication yield and scalability by avoiding frequency collisions.

Contribution

It presents a novel optimization approach for frequency allocation that enhances yield and scalability in fixed-frequency superconducting quantum processors.

Findings

Optimization increases fabrication yield.

Improves scalability of quantum processors.

Effective in avoiding frequency collisions.

Abstract

Fixed-frequency superconducting quantum processors are one of the most mature quantum computing architectures with high-coherence qubits and simple controls. However, high-fidelity multi-qubit gates pose tight requirements on individual qubit frequencies in these processors , and these constraints are difficult to satisfy when constructing larger processors due to the large dispersion in the fabrication of Josephson junctions. In this article, we propose a mixed-integer-programming-based optimization approach that determines qubit frequencies to maximize the fabrication yield of quantum processors. We study traditional qubit and qutrit (three-level) architectures with cross-resonance interaction processors. We compare these architectures to a differential AC-Stark shift based on entanglement gates and show that our approach greatly improves the fabrication yield and also increases the scalability of these devices. Our approach is general and can be adapted to problems where one must avoid specific frequency collisions.