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projects:quantum:seqnn [2026/05/21 08:28] – [Roadmap position] 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.