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

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 Zuchongzhi 2.1, which has 66 qubits in a two-dimensional array in a tunable coupler architecture. The readout fidelity of 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 574, 505 (2019)] in the classic simulation, and 3 orders of magnitude more difficult than the sampling task on 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 × 10 4 years), while Zuchongzhi 2.1 only takes about 4.2 hours, thereby significantly enhancing the quantum computational advantage.

Classical Sampling of Random Quantum Circuits with Bounded Fidelity

“…Substituting ( 7), (8), and the definition of D B (M (|ψ ), p) into ( 6) and dividing by √ 2 we have…”

“…From the practical point of view, a convincing argument in favor of the quantum advantage would be an experiment demonstrating that some well-defined problem can be efficiently solved on a real quantum computer, while the solution of the same problem using the stateof-the-art algorithms cannot be obtained within a reasonable amount of time even with the best classical supercomputers in the world [3][4][5]. At the current stage, a leading candidate for such a quantum advantage experiment, which was already performed on a real quantum hardware [6][7][8], is the task of sampling from the output probability distributions of random quantum circuits (RQCs). Though initially this task was estimated [6] to take thousands of years on the fastest classical supercomputers, later the simulation time was significantly reduced due to the recent progress on tensor network (TN) based quantum simulation algorithms [9][10][11][12][13][14][15].…”

Tunable coupling of widely separated superconducting qubits: A possible application towards a modular quantum device

“…After sustained and intense effort in the improvement of qubit performance and functionality, quantum devices with tens of superconducting qubits have been realized. This has led to impressive achievements in superconducting quantuminformation processing [1][2][3]. Nevertheless, the current smallscale noisy quantum processor is still insufficient to support the pursuit of quantum advantage (e.g., solving complex problems that are intractable for classical computing) for practical applications [4] and the long-term goal of fault-tolerant quantum computing [5,6].…”

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

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a 60+ qubit quantum processor [14][15][16] should be sufficient to explore various quantum statistical properties of such phases of matter without resorting to the usual approximations [17][18][19]. This should allow us to explore their properties beyond the understanding conventional techniques provide [20][21][22].However, using these powerful noisy intermediate-scale quantum (NISQ) processors brings several serious complications associated with their use -beyond those associated with noise and imperfections.

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“…a 60+ qubit quantum processor [14][15][16] should be sufficient to explore various quantum statistical properties of such phases of matter without resorting to the usual approximations [17][18][19]. This should allow us to explore their properties beyond the understanding conventional techniques provide [20][21][22].…”