  A new form of ML knowledge representation with high generalization power is
developed and implemented numerically. Initial $\mathit{IN}$ attributes and
$\mathit{OUT}$ class label are transformed into the corresponding Hilbert
spaces by considering localized wavefunctions. A partially unitary operator
optimally converting a state from $\mathit{IN}$ Hilbert space into
$\mathit{OUT}$ Hilbert space is then built from an optimization problem of
transferring maximal possible probability from $\mathit{IN}$ to $\mathit{OUT}$,
this leads to the formulation of a new algebraic problem. Constructed Knowledge
Generalizing Operator $\mathcal{U}$ can be considered as a $\mathit{IN}$ to
$\mathit{OUT}$ quantum channel; it is a partially unitary rectangular matrix of
the dimension $\mathrm{dim}(\mathit{OUT}) \times \mathrm{dim}(\mathit{IN})$
transforming operators as $A^{\mathit{OUT}}=\mathcal{U} A^{\mathit{IN}}
\mathcal{U}^{\dagger}$. Whereas only operator $\mathcal{U}$ projections squared
are observable
$\left\langle\mathit{OUT}|\mathcal{U}|\mathit{IN}\right\rangle^2$
(probabilities), the fundamental equation is formulated for the operator
$\mathcal{U}$ itself. This is the reason of high generalizing power of the
approach; the situation is the same as for the Schr\"{o}dinger equation: we can
only measure $\psi^2$, but the equation is written for $\psi$ itself.
