Noise reduction using past causal cones in variational quantum algorithms
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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 ∼ 27% improvement in the accuracy of the variational calculation of the deuteron binding energy and ∼ 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 ∼ 71% fewer measurements and the latter requires ∼ 78% fewer measurements.
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