QREChem:用于化学应用的量子资源估计软件

Matthew Otten, Byeol Kang, Dmitry Fedorov, Joo-Hyoung Lee, Anouar Benali, Salman Habib, Stephen K. Gray, Yuri Alexeev
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摘要

随着量子硬件的不断完善,越来越多的应用科学家进入了量子计算领域。然而,即使在过去几年中有了快速的改进,量子设备,特别是量子化学应用,仍然难以执行经典计算机无法计算的计算。重要的是,有一种系统的方法来估计解决具体问题所需的资源,而不是能够进行具体的计算。关于计算复杂性的标准论点为量子计算机将在量子化学问题中发挥作用提供了希望,但却掩盖了许多算法开销的真正影响。这些开销将最终决定量子计算机性能优于经典计算机的确切时间点。我们开发了QREChem,通过基于trotter的量子相位估计方法,为量子化学中的基态能量估计提供逻辑资源估计。QREChem提供资源估计,其中包括量子化学问题固有的特定开销,包括对Trotter步骤数量和必要辅助数量的启发式估计,允许更准确地估计门的总数。我们利用QREChem对不同基集中的各种小分子提供逻辑资源估计,得到T门总数在10 7 -10 15范围内的估计。我们还确定了FeMoco分子的估计,并将所有估计与其他资源估计工具进行比较。最后,我们比较了总资源,包括硬件和纠错开销,演示了对快速纠错周期时间的需求。
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QREChem: quantum resource estimation software for chemistry applications
As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of 10 7 –10 15 for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools. Finally, we compare the total resources, including hardware and error correction overheads, demonstrating the need for fast error correction cycle times.
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