非马尔可夫算法冷却中冷却步数与几何实现成本的权衡
Trade-off between Cooling-Step Count and Geometric Implementation Cost in Non-Markovian Algorithmic Cooling
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中文总结 AI 辅助
该研究针对单量子比特热浴算法冷却，探究储能器记忆对冷却步数与几何实现成本权衡的影响，通过模拟和ibm_kawasaki Heron r2处理器实验，发现抑制储能器记忆会减少冷却步数但增加几何成本，为算法冷却的储能工程提供资源视角。
中文摘要 AI 辅助
量子冷却对可靠量子计算至关重要，但存在冷却性能与实现资源间的权衡。尽管储能器记忆可提升冷却性能的特定方面，但其相关资源成本，尤其是电路实现方面的成本，仍未得到充分理解。本文研究了在单量子比特热浴算法冷却中，储能器记忆如何影响以冷却步数量化的冷却性能与几何实现成本之间的权衡。我们采用伪模映射，将非马尔可夫耗散的杰恩斯-卡明斯动力学表示为重复碰撞电路，并评估其几何实现成本。通过基于矩阵的模拟以及在ibm_kawasaki Heron r2处理器上的实现，我们确定了一种权衡：抑制储能器记忆会减少冷却步数，但通常会增加几何协议成本。本研究为算法冷却的储能工程提供了基于资源的视角。
英文摘要
Quantum cooling is important for reliable quantum computation but involves a trade-off between cooling performance and implementation resources. Although reservoir memory can improve particular aspects of cooling performance, the associated resource cost, particularly for circuit implementation, remains insufficiently understood. Here, we investigate how reservoir memory affects the trade-off between cooling performance quantified by the cooling-step count and geometric implementation cost in single-qubit heat-bath algorithmic cooling. Using a pseudomode mapping, we represent the non-Markovian damped Jaynes--Cummings dynamics by a repeated collision circuit and evaluate its geometric implementation cost. Using matrix-based simulations and an implementation on the ibm_kawasaki Heron r2 processor, we identify a trade-off: suppressing reservoir memory reduces the cooling-step count but generally increases the geometric protocol cost. Our work provides a resource-based perspective on reservoir engineering for algorithmic cooling.
发表机构
- The University of Tokyo（东京大学）
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