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- W4386719919 abstract "Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstration of near-term quantum advantage in solving optimization tasks in chemical simulation, quantum information, and machine learning. The standard model of training requires a significant amount of quantum resources, which led us to use classical shadows to devise an alternative that consumes exponentially fewer quantum resources. However, the approach only works when the observables are local and the ansatz is the shallow Alternating Layered Ansatz (ALA), thus severely limiting its potential in solving problems such as quantum state preparation, where the ideal state might not be approximable with an ALA. In this work, we present a protocol based on shallow shadows that achieves similar levels of savings for almost any shallow ansatz studied in the literature, when combined with observables of low Frobenius norm. We show that two important applications in quantum information for which VQAs can be a powerful option, namely variational quantum state preparation and variational quantum circuit synthesis, are compatible with our protocol. We also experimentally demonstrate orders of magnitude improvement in comparison to the standard VQA model." @default.
- W4386719919 created "2023-09-14" @default.
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- W4386719919 date "2023-09-09" @default.
- W4386719919 modified "2023-09-26" @default.
- W4386719919 title "Ansatz-Agnostic Exponential Resource Saving in Variational Quantum Algorithms Using Shallow Shadows" @default.
- W4386719919 doi "https://doi.org/10.48550/arxiv.2309.04754" @default.
- W4386719919 hasPublicationYear "2023" @default.
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