TY - GEN
T1 - Accelerating Quantum Approximate Optimization Algorithm using Machine Learning
AU - Alam, Mahabubul
AU - Ash-Saki, Abdullah
AU - Ghosh, Swaroop
N1 - Funding Information:
Fig. 6: Prediction errors in (a) p = 2; (b) p = 3; (c) p = 4; and (d) p = 5 QAOA instance control parameter predictions for the test data-set (264 graphs) in the two-level approach. Acknowledgement: This work is supported by SRC (2847.001), and NSF (CNS-1722557, CCF-1718474, CNS-1814710, DGE-1723687 and DGE-1821766).
Publisher Copyright:
© 2020 EDAA.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/3
Y1 - 2020/3
N2 - We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a parametric quantum circuit and a classical optimizer iterates in a closed loop to solve hard combinatorial optimization problems. The performance of QAOA improves with increasing number of stages (depth) in the quantum circuit. However, two new parameters are introduced with each added stage for the classical optimizer increasing the number of optimization loop iterations. We note a correlation among parameters of the lower-depth and the higher-depth QAOA implementations and, exploit it by developing a machine learning model to predict the gate parameters close to the optimal values. As a result, the optimization loop converges in a fewer number of iterations. We choose graph MaxCut problem as a prototype to solve using QAOA. We perform a feature extraction routine using 100 different QAOA instances and develop a training data-set with 13, 860 optimal parameters. We present our analysis for 4 flavors of regression models and 4 flavors of classical optimizers. Finally, we show that the proposed approach can curtail the number of optimization iterations by on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of graphs.
AB - We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a parametric quantum circuit and a classical optimizer iterates in a closed loop to solve hard combinatorial optimization problems. The performance of QAOA improves with increasing number of stages (depth) in the quantum circuit. However, two new parameters are introduced with each added stage for the classical optimizer increasing the number of optimization loop iterations. We note a correlation among parameters of the lower-depth and the higher-depth QAOA implementations and, exploit it by developing a machine learning model to predict the gate parameters close to the optimal values. As a result, the optimization loop converges in a fewer number of iterations. We choose graph MaxCut problem as a prototype to solve using QAOA. We perform a feature extraction routine using 100 different QAOA instances and develop a training data-set with 13, 860 optimal parameters. We present our analysis for 4 flavors of regression models and 4 flavors of classical optimizers. Finally, we show that the proposed approach can curtail the number of optimization iterations by on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of graphs.
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U2 - 10.23919/DATE48585.2020.9116348
DO - 10.23919/DATE48585.2020.9116348
M3 - Conference contribution
AN - SCOPUS:85087409413
T3 - Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
SP - 686
EP - 689
BT - Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
A2 - Di Natale, Giorgio
A2 - Bolchini, Cristiana
A2 - Vatajelu, Elena-Ioana
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
Y2 - 9 March 2020 through 13 March 2020
ER -