TL;DR
This paper develops a classical filtering approach for discrete projective quantum measurements, enabling adaptive learning of quantum system dynamics and noise, thus bridging classical control techniques with quantum measurement data.
Contribution
It introduces a generalized analysis of filtering for quantum systems with projective measurements, demonstrating classical nonlinear filtering convergence properties in this context.
Findings
Classical filtering techniques can be applied to quantum projective measurements.
Convergence properties of nonlinear classical filters hold for quantum systems.
Framework enables adaptive learning of quantum system parameters.
Abstract
Adaptive filtering is a powerful class of control theoretic concepts useful in extracting information from noisy data sets or performing forward prediction in time for a dynamic system. The broad utilization of the associated algorithms makes them attractive targets for similar problems in the quantum domain. To date, however, the construction of adaptive filters for quantum systems has typically been carried out in terms of stochastic differential equations for weak, continuous quantum measurements, as used in linear quantum systems such as optical cavities. Discretized measurement models are not as easily treated in this framework, but are frequently employed in quantum information systems leveraging projective measurements. This paper presents a detailed analysis of several technical innovations that enable classical filtering of discrete projective measurements, useful for…
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