Abstract

A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.

Ensuring high-fidelity quantum gates while increasing the number of qubits poses a great challenge. Here the authors present a scalable strategy for optimizing frequency trajectories as a form of error mitigation on a 68-qubit superconducting quantum processor, demonstrating high single- and two-qubit gate fidelities.

Details

Title
Optimizing quantum gates towards the scale of logical qubits
Author
Klimov, Paul V. 1   VIAFID ORCID Logo  ; Bengtsson, Andreas 1   VIAFID ORCID Logo  ; Quintana, Chris 1 ; Bourassa, Alexandre 1   VIAFID ORCID Logo  ; Hong, Sabrina 1   VIAFID ORCID Logo  ; Dunsworth, Andrew 1 ; Satzinger, Kevin J. 1   VIAFID ORCID Logo  ; Livingston, William P. 1   VIAFID ORCID Logo  ; Sivak, Volodymyr 1 ; Niu, Murphy Yuezhen 1 ; Andersen, Trond I. 1 ; Zhang, Yaxing 1 ; Chik, Desmond 1 ; Chen, Zijun 1 ; Neill, Charles 1   VIAFID ORCID Logo  ; Erickson, Catherine 1 ; Grajales Dau, Alejandro 1 ; Megrant, Anthony 1   VIAFID ORCID Logo  ; Roushan, Pedram 1   VIAFID ORCID Logo  ; Korotkov, Alexander N. 2 ; Kelly, Julian 1   VIAFID ORCID Logo  ; Smelyanskiy, Vadim 1 ; Chen, Yu 1 ; Neven, Hartmut 1   VIAFID ORCID Logo 

 Google AI, Mountain View, USA (GRID:grid.420451.6) (ISNI:0000 0004 0635 6729) 
 Google AI, Mountain View, USA (GRID:grid.420451.6) (ISNI:0000 0004 0635 6729); University of California, Department of Electrical and Computer Engineering, Riverside, USA (GRID:grid.266097.c) (ISNI:0000 0001 2222 1582) 
Pages
2442
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2963023577
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.