Optimizing quantum gates towards the scale of logical qubits

Paul V. Klimov (), Andreas Bengtsson, Chris Quintana, Alexandre Bourassa, Sabrina Hong, Andrew Dunsworth, Kevin J. Satzinger, William P. Livingston, Volodymyr Sivak, Murphy Yuezhen Niu, Trond I. Andersen, Yaxing Zhang, Desmond Chik, Zijun Chen, Charles Neill, Catherine Erickson, Alejandro Grajales Dau, Anthony Megrant, Pedram Roushan, Alexander N. Korotkov, Julian Kelly, Vadim Smelyanskiy, Yu Chen and Hartmut Neven
Additional contact information
Paul V. Klimov: Google AI
Andreas Bengtsson: Google AI
Chris Quintana: Google AI
Alexandre Bourassa: Google AI
Sabrina Hong: Google AI
Andrew Dunsworth: Google AI
Kevin J. Satzinger: Google AI
William P. Livingston: Google AI
Volodymyr Sivak: Google AI
Murphy Yuezhen Niu: Google AI
Trond I. Andersen: Google AI
Yaxing Zhang: Google AI
Desmond Chik: Google AI
Zijun Chen: Google AI
Charles Neill: Google AI
Catherine Erickson: Google AI
Alejandro Grajales Dau: Google AI
Anthony Megrant: Google AI
Pedram Roushan: Google AI
Alexander N. Korotkov: Google AI
Julian Kelly: Google AI
Vadim Smelyanskiy: Google AI
Yu Chen: Google AI
Hartmut Neven: Google AI

Nature Communications, 2024, vol. 15, issue 1, 1-8

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-46623-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46623-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-46623-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().