Adaptive estimation of quantum observables
Ariel Shlosberg, Andrew J. Jena, Priyanka Mukhopadhyay, Jan F. Haase,, Felix Leditzky, Luca Dellantonio

TL;DR
AEQuO is an adaptive measurement scheme for quantum observables that optimizes data collection, improving accuracy and reducing measurement costs in quantum experiments, especially for chemistry Hamiltonians.
Contribution
The paper introduces AEQuO, a novel adaptive measurement protocol that dynamically adjusts measurement strategies based on ongoing data, enhancing efficiency over existing methods.
Findings
AEQuO improves error estimates compared to state-of-the-art methods.
AEQuO reduces the number of measurements needed in quantum experiments.
AEQuO performs well on chemistry Hamiltonians, demonstrating practical advantages.
Abstract
The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Spectroscopy and Quantum Chemical Studies
