ABSTRACT
Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - size and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable at the current size of computers, requiring thousands of qubits, while NISQ systems have nearly one hundred at most. One common approach to improve reliability is to adjust the compilation process to create a more reliable final circuit, where the two most critical compilation decisions are the qubit allocation and qubit routing problems. We focus on solving the qubit allocation problem and identifying initial layouts that result in a reduction of error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network to process the mesh topology of the quantum computer, known as the backend, and make mapping decisions, creating a Graph Neural Network Assisted Quantum Compilation (GNAQC) strategy. We train the architecture using a set of four backends and six circuits and find that GNAQC improves output fidelity by roughly 12.7% over pre-existing allocation methods.
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- Gushu Li, Yufei Ding, and Yuan Xie. 2019. Tackling the qubit mapping problem for NISQ-era quantum devices. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 1001--1014.Google ScholarDigital Library
- Prakash Murali, Jonathan M Baker, Ali Javadi-Abhari, Frederic T Chong, and Margaret Martonosi. 2019. Noise-adaptive compiler mappings for noisy intermediatescale quantum computers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 1015--1029.Google ScholarDigital Library
Index Terms
- Graph Neural Network Assisted Quantum Compilation for Qubit Allocation
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