Search Space Reduction for Efficient Quantum Compilation
Search Space Reduction for Efficient Quantum Compilation
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减少搜索空间以实现高效的量子编译
DOI：
10.1145/3583781.3590223
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Basu, Kanad
中科院分区：
文献类型：
--
作者：
Srivastava, Amisha;Lu, Chao;Choudhury, Navnil;Arunachalam, Ayush;Basu, Kanad
与经典计算机相比，量子计算机在整数分解、分子模拟和机器学习等某些计算任务上表现出了指数级的加速。量子计算中最具挑战性的问题之一是量子编译，它涉及将量子电路转换为符合量子硬件施加的约束的表示。然而，这种将逻辑量子比特映射到物理量子比特的过程导致了相当大的搜索空间，需要对其进行分析以获得最优映射。非最佳映射或编译策略会带来额外的硬件开销，从而导致效率低下。最近，研究人员提出了一种减少搜索空间的技术，以实现高效的量子编译。然而，这种方法关注的是只涉及物理体系结构的通用解决方案，因此，如我们的论文所示，通常无法在缩小的搜索空间中包含最优解决方案。为此，我们提出了PERM和SGO(PAS)，据我们所知，这是第一种量子编译策略，它有助于减少搜索空间，包括与现有技术相比，在增加CNOT门方面更优的解决方案。我们使用MQT基准进行的实验评估证明了该方法的有效性，与未经优化的搜索空间相比，该方法减少了高达428倍的搜索空间，与现有研究相比减少了57.1倍，同时提供了高达53.85%的额外CNOT门数节省。
Quantum computers have demonstrated exponential speedup for certain computational tasks like integer factorization, molecular simulation, and machine learning, compared to the classical computers. One of the most challenging problems in quantum computing is quantum compilation, which involves the translation of a quantum circuit into a representation that adheres to the constraints imposed by the quantum hardware. However, this process of mapping the logical qubits to physical qubits incurs a significantly large search space, which needs to be analyzed to obtain the optimal mapping. A non-optimal mapping or compilation strategy introduces additional hardware overhead, thereby rendering inefficiency. Recently, researchers have proposed a technique to reduce the search space for efficient quantum compilation. However, this approach focuses on a generic solution involving only the physical architecture, and hence, as shown in our paper, often fails to incorporate the optimal solution in the reduced search space. To this end, we propose PERM and SGO (PAS), which, to the best of our knowledge, is the first quantum compilation strategy that facilitates a reduced search space comprising a more optimal solution in terms of additional CNOT gates compared to the existing technique. Our experimental evaluation using the MQT benchmarks demonstrates the efficacy of our approach, which furnishes up to 428x reduction compared to the unoptimized search space, and 57.1x reduction compared to existing research, while providing savings in terms of additional CNOT gates by up to 53.85%.
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