Simulating large-size quantum spin chains on cloud-based superconducting quantum computers
Simulating large-size quantum spin chains on cloud-based superconducting quantum computers
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DOI：
10.1103/physrevresearch.5.013183
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发表时间：
2022-07
影响因子：
4.2
通讯作者：
Hongye Yu;Yusheng Zhao;T. Wei
中科院分区：
文献类型：
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作者：
Hongye Yu;Yusheng Zhao;T. Wei
量子计算机有可能有效地模拟大规模量子系统，而对这些系统来说，经典方法注定会失败。尽管现有的几个量子设备现在的总量子比特数超过100个，但它们的适用性仍然受到噪声和误差的存在的困扰。因此，大型量子系统在多大程度上可以在这些设备上成功模拟仍不清楚。在这里，我们报告了在IBM的几台超导量子计算机上进行的云模拟，以模拟具有高达102量子比特的大范围系统大小的自旋链的基态。我们发现，从不同量子计算机和系统大小的实现中提取的基态能量达到了预期值，误差很小(即在百分比水平上)，包括从这些值推断热力学极限中的能量密度。我们通过组合物理驱动的变分卫星，以及高效、可扩展的能量测量和误差缓解协议来实现这一精度，包括在零噪声外推中使用参考态。通过使用102量子比特系统，在执行门误差抑制时，我们已经能够在单个电路中成功地应用多达3186个CNOT门。我们对Ansatz态中随机参数的精确、误差抑制的结果表明，一种适用于大规模XXZ模型的独立的量子经典混合变分方法是可行的。
Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than one hundred, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum systems can successfully be simulated on these devices remains unclear. Here, we report on cloud simulations performed on several of IBM's superconducting quantum computers to simulate ground states of spin chains having a wide range of system sizes up to one hundred and two qubits. We find that the ground-state energies extracted from realizations across different quantum computers and system sizes reach the expected values to within errors that are small (i.e. on the percent level), including the inference of the energy density in the thermodynamic limit from these values. We achieve this accuracy through a combination of physics-motivated variational Ansatzes, and efficient, scalable energy-measurement and error-mitigation protocols, including the use of a reference state in the zero-noise extrapolation. By using a 102-qubit system, we have been able to successfully apply up to 3186 CNOT gates in a single circuit when performing gate-error mitigation. Our accurate, error-mitigated results for random parameters in the Ansatz states suggest that a standalone hybrid quantum-classical variational approach for large-scale XXZ models is feasible.