Quantum Circuit Partitioning for Scalable Noise-Aware Quantum Circuit Re-Synthesis

Conference · Sun Sep 15 00:00:00 EDT 2024 · 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)

DOI:https://doi.org/10.1109/qce60285.2024.10306· OSTI ID:2529434

Charrwi, Mohammad Walid ^[1]; Ioannou, Georgios ^[1]; Younis, Ed ^[2]; de Jong, Wibe Albert ^[2]; Saeed, Samah Mohamed ^[1]

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1. City College of New York, City University of New York
2. Lawrence Berkeley National Laboratory

Re-synthesis techniques are utilized to optimize the quantum circuit. To enable scalable re-synthesis a divide-and-conquer approach is adopted that partitions the circuit into smaller blocks, which are optimized independently. Several algorithms have been proposed to minimize the block number while maximizing the gate count of each block. However, they vary in their performance and may not yield the highest output fidelity. We propose a reinforcement learning-based quantum circuit partitioning framework that incorporates the physical properties of the quantum hardware to maximize the output fidelity post-quantum circuit optimization. To accelerate the training, we also propose a noise injection method that enables on-the-fly optimization in the reinforcement learning environment, independent of the adopted optimization/re-synthesis method at the block level. We evaluate our approach compared to different partitioning techniques using various quantum benchmarks executed on IBM Q Hanoi quantum computer.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-ASCR)
• DOE Contract Number: AC02-05CH11231
• OSTI ID: 2529434
• Journal Information: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Journal Name: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
• Country of Publication: United States
• Language: English