Quantum Algorithm for Machine Learning and Circuit Design Based on Optimization of Ternary - Input, Binary-Output Kronecker-Reed-Muller Forms

Paper introduces a new spectral expansion of ternary-input binary-output functions that generalizes the binary Kronecker-Reed-Muller forms. Two binary Davio Expansions are generalized to 27 Ternary-Input Davio Expansions. New KRM spectrum has 28(n) expansions for n ternary variables. By creating an oracle for this problem we generalize the quantum Grover-based algorithms presented for the binary FPRM and KRM forms in the past. Because the method finds solutions also to incompletely specified functions, it can be used to both quantum circuit design and Machine Learning classifier design.