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projects:quantum:seqnn [2026/05/21 08:27] – [The idea] kymkiprojects:quantum:seqnn [2026/05/21 08:45] (current) – [Introduction] kymki
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 ===== Introduction ===== ===== Introduction =====
  
-Kalman filtering is fascinating! How can it be applied to QML models to provide some sort of "self-estimation" of state? This was the fundamental question here that drove me to go on a rather extensive exploration of this topic. In this post I will describe the current position of Self-Estimating Quantum Neural Networks (SEQNN).+Kalman filtering is fascinating and has a wide area of application. How can it be applied to QML models to provide some sort of "self-estimation" of state? This was the fundamental question here that drove me to go on a rather extensive exploration of this topic. In this post I will describe the current position of Self-Estimating Quantum Neural Networks (SEQNN).
  
 SEQNN, is an attempt to treat the parameters of a variational quantum model as latent states to be estimated sequentially, rather than as weights managed by a global optimizer. SEQNN, is an attempt to treat the parameters of a variational quantum model as latent states to be estimated sequentially, rather than as weights managed by a global optimizer.
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 The important difference is not only "which update rule is used." Rather, when the model is written as an estimator, uncertainty, identifiability, calibration, and measurement attribution become terms we can use to reason with and build our algorithms around. Is that helpful? Lets see what the outcome is here. The important difference is not only "which update rule is used." Rather, when the model is written as an estimator, uncertainty, identifiability, calibration, and measurement attribution become terms we can use to reason with and build our algorithms around. Is that helpful? Lets see what the outcome is here.
  
-===== Roadmap position =====+===== Roadmap position (needs improvements) =====
  
 ^ Stage ^ Status ^ ^ Stage ^ Status ^