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arXiv · 1906.00476

Noise reduction using past causal cones in variational quantum algorithms

Omar Shehab·Isaac H. Kim·Nhung H. Nguyen·Kevin Landsman·Cinthia H. Alderete·Daiwei Zhu·C. Monroe·Norbert M. Linke
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Abstract

We introduce an approach to improve the accuracy and reduce the sample complexity of near term quantum-classical algorithms. We construct a simpler initial parameterized quantum state, or ansatz, based on the past causal cone of each observable, generally yielding fewer qubits and gates. We implement this protocol on a trapped ion quantum computer and demonstrate improvement in accuracy and time-to-solution at an arbitrary point in the variational search space. We report a $\sim 27\%$ improvement in the accuracy of the calculation of the deuteron binding energy and $\sim 40\%$ improvement in the accuracy of the quantum approximate optimization of the MAXCUT problem applied to the dragon graph $T_{3,2}$. When the time-to-solution is prioritized over accuracy, the former requires $\sim 71\%$ fewer measurements and the latter requires $\sim 78\%$ fewer measurements.

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Omar Shehab, Isaac H. Kim, Nhung H. Nguyen, Kevin Landsman, Cinthia H. Alderete, Daiwei Zhu, C. Monroe, Norbert M. Linke. 2019-06-02. Noise reduction using past causal cones in variational quantum algorithms. https://arxiv.org/abs/1906.00476

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