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Bulletin of the American Physical Society

Practical demonstration of quantum approximate optimization on Google’s superconducting qubit processor

Matthew P. Harrigan

Published Mar 2, 2020• 0 citations
OpenAlex

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The Google Quantum AI team's long-term goal is to realize a large-scale fault-tolerant quantum computer. Of the technologies available to implement the quantum processor at the core of such a system, solid-state superconducting circuits based on Josephson junctions (JJs) are among the strongest contenders. Recent demonstrations with this technology include computation beyond the capabilities of today's most powerful supercomputers [1] and execution of quantum error correction (QEC) codes that can detect and correct either bit or phase flip errors [2]. These exciting results were enabled by low-error-rate quantum processors with tens of qubits and the supporting infrastructure to run these devices. However, today's quantum computers still have orders of magnitude fewer qubits than is required for a useful fault-tolerant quantum computer; for this, an estimated million or more qubits will be needed and scaling-up today's technology to this extent is non-trivial. Here, we review the Google Quantum AI team's recent progress and future plans. Specifically, we describe our post-classical Sycamore quantum computer and discuss the Google Quantum AI roadmap to fault-tolerant quantum computing.
Quantum computational advantage is a critical milestone for near-term quantum technologies and a crucial step toward building practical quantum computers. Recent successful demonstrations of quantum computational advantage owe much to specially designed random quantum circuit (RQC) protocols that enable hardware-friendly implementation and, more importantly, pose great challenges for classical simulation. Here, we report an automated protocol-design approach for determining the optimal RQC in the Zuchongzhi quantum computational advantage experiment. Without a carefully designed protocol, the classical simulation cost of the Zuchongzhi 56-qubit 20-cycle RQC experiment would not be considerably higher than Google’s 53-qubit 20-cycle experiment, even though more qubits are involved. For Google’s latest RQC experiment using 70 qubits and 24 cycles, we estimate that the classical simulation cost can be increased by at least one order of magnitude using the proposed approach. The proposed method can be applied to generic planar quantum processor architectures and addresses realistic imperfections such as processor defects, underpinning quantum computational advantage experiments in future generations of quantum processors.