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Optimizing quantum gates towards the scale of logical qubits.
Klimov, Paul V; Bengtsson, Andreas; Quintana, Chris; Bourassa, Alexandre; Hong, Sabrina; Dunsworth, Andrew; Satzinger, Kevin J; Livingston, William P; Sivak, Volodymyr; Niu, Murphy Yuezhen; Andersen, Trond I; Zhang, Yaxing; Chik, Desmond; Chen, Zijun; Neill, Charles; Erickson, Catherine; Grajales Dau, Alejandro; Megrant, Anthony; Roushan, Pedram; Korotkov, Alexander N; Kelly, Julian; Smelyanskiy, Vadim; Chen, Yu; Neven, Hartmut.
  • Klimov PV; Google AI, Mountain View, CA, USA. pklimov@google.com.
  • Bengtsson A; Google AI, Mountain View, CA, USA.
  • Quintana C; Google AI, Mountain View, CA, USA.
  • Bourassa A; Google AI, Mountain View, CA, USA.
  • Hong S; Google AI, Mountain View, CA, USA.
  • Dunsworth A; Google AI, Mountain View, CA, USA.
  • Satzinger KJ; Google AI, Mountain View, CA, USA.
  • Livingston WP; Google AI, Mountain View, CA, USA.
  • Sivak V; Google AI, Mountain View, CA, USA.
  • Niu MY; Google AI, Mountain View, CA, USA.
  • Andersen TI; Google AI, Mountain View, CA, USA.
  • Zhang Y; Google AI, Mountain View, CA, USA.
  • Chik D; Google AI, Mountain View, CA, USA.
  • Chen Z; Google AI, Mountain View, CA, USA.
  • Neill C; Google AI, Mountain View, CA, USA.
  • Erickson C; Google AI, Mountain View, CA, USA.
  • Grajales Dau A; Google AI, Mountain View, CA, USA.
  • Megrant A; Google AI, Mountain View, CA, USA.
  • Roushan P; Google AI, Mountain View, CA, USA.
  • Korotkov AN; Google AI, Mountain View, CA, USA.
  • Kelly J; Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA.
  • Smelyanskiy V; Google AI, Mountain View, CA, USA.
  • Chen Y; Google AI, Mountain View, CA, USA.
  • Neven H; Google AI, Mountain View, CA, USA.
Nat Commun ; 15(1): 2442, 2024 Mar 18.
Article en En | MEDLINE | ID: mdl-38499541
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.