Inductive learning of quantum behaviors

Inductive Learning of Quantum Behaviors

In this paper studied are new concepts of robotic behaviors deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are car...

Inductive Supervised Quantum Learning.

In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of ...

A Quantum Inductive Logic

Sequential Inductive Learning

In this paper I advocate a new model for inductive learning. Called sequential induction, this model bridges classical fixed-sample learning techniques (which are efficient but ad hoc), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). According to the sequential inductive model, learning is a sequence of decisions which are infor...

Inductive Learning with BCT

BCT (Binary Classification Tree) is a system that learns from examples and represents learned concepts as a binary polythetic decision tree. Polythetic trees differ from monothetic decision trees in that a logical combination of multiple (versus a single) attribute values may label each tree arc. Statistical evaluations are used to recursively partition the concept space in two and expand the t...