Enabling Automated FPGA Accelerator Optimization Using Graph Neural Networks

Sohrabizadeh, Atefeh; Bai, Yunsheng; Sun, Yizhou; Cong, Jason

doi:10.48550/arxiv.2111.08848

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Furthermore, local qubit addressing during the evolution can be used to both extend the range of optimization parameters and the types of optimization problems (5). Further analysis could elucidate the origins of classical and quantum hardness, for example, by using graph neural network approaches (53). Finally, similar approaches can be used to explore realizations of other classes of quantum algorithm [see, e.g., (54)], enabling a broader range of potential applications.…”

Section: A B Cmentioning

confidence: 99%

Quantum optimization of maximum independent set using Rydberg atom arrays

Pichler¹,

Wang²,

Zhou³

et al. 2022

Science

172

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Realizing quantum speedup for practically relevant, computationally hard problems is a central challenge in quantum information science. Using Rydberg atom arrays with up to 289 qubits in two spatial dimensions, we experimentally investigate quantum algorithms for solving the Maximum Independent Set problem. We use a hardware-efficient encoding associated with Rydberg blockade, realize closed-loop optimization to test several variational algorithms, and subsequently apply them to systematically explore a class of graphs with programmable connectivity. We find the problem hardness is controlled by the solution degeneracy and number of local minima, and experimentally benchmark the quantum algorithm’s performance against classical simulated annealing. On the hardest graphs, we observe a superlinear quantum speedup in finding exact solutions in the deep circuit regime and analyze its origins.

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Section: A B Cmentioning

confidence: 99%