量子化学模拟的容错资源估算：锂离子电池电解质分子案例研究
Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules
AI总结：
本研究针对锂离子电池电解质分子的容错量子化学模拟，估算融合式光子量子计算方案的资源需求，提出多魔态工厂并行制备及同步消耗魔态的架构与算法，可在不增占地的前提下将运行时间降低一个数量级。
摘要：
我们估算了在基于融合的量子计算方案中，在容错光子量子计算机上模拟锂离子电池中电解质分子所需的资源。我们聚焦于那些能为工业相关问题提供实用解决方案的分子。某些容错操作需要使用由专用“魔态工厂（MSFs）”制备的单量子比特“魔态”。并行制备和消耗魔态通常是成本极高的任务，导致容错门只能串行应用。然而，对于所研究的系统，MSF仅占量子计算机总占地面积的可忽略比例，因此可以使用多个MSF并行制备魔态。我们提出了能够适配这种能力的架构和算法技术。我们提出了一种同时消耗多个魔态的方法，有望在不增加占地面积额外开销的前提下，将计算运行时间降低一个数量级。
英文摘要：
We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer. We focus on the molecules that can provide practical solutions to industrially relevant problems. Certain fault-tolerant operations require the use of single-qubit "magic states" prepared by dedicated "magic state factories" (MSFs). Producing and consuming magic states in parallel is typically a prohibitively expensive task, resulting in the serial application of fault-tolerant gates. However, for the systems considered, the MSF constitutes a negligible fraction of the total footprint of the quantum computer, allowing for the use of multiple MSFs to produce magic states in parallel. We suggest architectural and algorithmic techniques that can accommodate such a capability. We propose a method to consume multiple magic states simultaneously, which can potentially lead to an order of magnitude reduction in the computational runtime without additional expense in the footprint.
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