Optimizing Quantum Gates For Error Correction in Superconducting Qubits (Google AI)


A new technical paper titled “Optimizing quantum gates towards the scale of logical qubits” was published by researchers at Google AI and UC Riverside.

“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.”

Find the technical paper here. Published March 2024.

Klimov, P.V., Bengtsson, A., Quintana, C. et al. Optimizing quantum gates towards the scale of logical qubits. Nat Commun 15, 2442 (2024). https://doi.org/10.1038/s41467-024-46623-y

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