Quantum Computational Advantage via 60-Qubit 24-Cycle Random Circuit Sampling (2109.03494v2)

Abstract: To ensure a long-term quantum computational advantage, the quantum hardware should be upgraded to withstand the competition of continuously improved classical algorithms and hardwares. Here, we demonstrate a superconducting quantum computing systems \textit{Zuchongzhi} 2.1, which has 66 qubits in a two-dimensional array in a tunable coupler architecture. The readout fidelity of \textit{Zuchongzhi} 2.1 is considerably improved to an average of 97.74\%. The more powerful quantum processor enables us to achieve larger-scale random quantum circuit sampling, with a system scale of up to 60 qubits and 24 cycles. The achieved sampling task is about 6 orders of magnitude more difficult than that of Sycamore [Nature \textbf{574}, 505 (2019)] in the classic simulation, and 3 orders of magnitude more difficult than the sampling task on \textit{Zuchongzhi} 2.0 [arXiv:2106.14734 (2021)]. The time consumption of classically simulating random circuit sampling experiment using state-of-the-art classical algorithm and supercomputer is extended to tens of thousands of years (about $4.8\times 10^4$ years), while \textit{Zuchongzhi} 2.1 only takes about 4.2 hours, thereby significantly enhancing the quantum computational advantage.

• The paper achieves a quantum advantage by executing a 60-qubit, 24-cycle random circuit sampling task on the Zuchongzhi 2.1 processor.
• It employs a novel 4-patch calibration and a 2D transmon qubit architecture to enhance two-qubit gate fidelity and mitigate errors.
• The findings extend classical simulation limits by setting benchmarks for scalable quantum systems beyond current computational capabilities.

Quantum Computational Advantage with 60-Qubit 24-Cycle Random Circuit Sampling

The research presented introduces the latest developments in superconducting quantum computing through a paper focused on the Zuchongzhi 2.1 quantum processor. This processor features a 66-qubit architecture, which is leveraged to execute a complex random circuit sampling task involving 60 qubits across 24 cycles. A significant improvement in readout fidelity, which averages 97.74%, underpins this advancement. The results achieved with Zuchongzhi 2.1 substantiate a marked quantum computational advantage, providing computational outputs that are several orders of magnitude beyond what classical computing systems can simulate within a practical timeframe.

To contextualize the significance of these findings, the paper examines developments in both quantum and classical computational paradigms. Classical simulations made notable strides in approximating quantum computations, posing challenges to previous claims of quantum supremacy, such as those evidenced by Google's Sycamore processor. This work significantly extends the existing benchmark by presenting a task distinctly beyond the current capacities of classical supercomputers for efficient simulation, estimating tens of thousands of years would be required for comparable classical calculations.

The architecture of Zuchongzhi 2.1 is ingeniously designed and fabricated using a 2D array of transmon qubits enhanced by tunable coupler technology. A quantum circuit sampling method is employed to evaluate the processor's computational prowess, employing randomly selected single-qubit gate operations from the set $\{\sqrt{X}, \sqrt{Y}, \sqrt{W}\}$ and utilizing a refined iSWAP-like two-qubit gate for multi-qubit interactions. The two-qubit gate represents notable progress in gate duration and fidelity, contributing to the processor’s capacity to handle more intricate quantum circuits than its predecessors.

The paper also introduces a novel optimization technique termed "4-patch calibration," which addresses performance variability evident in prior configurations. This method capitalizes on a divided circuit approach, segmenting the computational task into smaller patches for localized calibration. This strategy effectively mitigates two-qubit gate error contributions, resulting in better performance alignment with predicted fidelities across the entire quantum processor circuit configuration range.

The paper's implications span both theoretical and applied aspects of quantum computing. These include bolstering the quantum advantage through hardware advancements and spotlighting methodologies for error mitigation and fidelity enhancement, which are critical for future scaled implementations. Moreover, the insights provided through Zuchongzhi 2.1's utilization offer a framework for potential applications beyond sampling, such as the realization of certified random bits and simulations necessary in quantum error correction, NISQ-era technology deployments, and potentially broader classes of quantum algorithms. The work advances the frontier of quantum computing, underscoring the necessity for ongoing enhancements in hardware capabilities and algorithmic strategies to realize practical, advantageously scalable quantum systems.

This paper firmly establishes Zuchongzhi 2.1 as a state-of-the-art processor, pushing the boundaries of quantum computational capacity and setting a benchmark for forthcoming developments in the discipline.