Inductive learning of quantum behaviors

In this paper studied are new concepts of robotic behaviors - determin- istic and quantum probabilistic. In contrast to classical c ircuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit con- troller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not give n as examples) are then converted not only to deterministic cares as in the classica l learning, but also to output values generated with various probabilities. The Occam Razor principle, fundamental to inductive learning, is satisfied in this approach by seeki ng circuits of reduced com- plexity. This is illustrated by the synthesis of single outp ut quantum circuits, as we extended the logic synthesis approach to Inductive Machine Learning for the case of learning quantum circuits from behavioral examples.