Dec 1 – 3, 2021
America/New_York timezone

Non-Boolean Quantum Amplitude Amplification and ML applications

Dec 3, 2021, 11:00 AM


Prasanth Shyamsundar (Fermi National Accelerator Laboratory)


This work generalizes the quantum amplitude amplification (Grover’s) and amplitude estimation algorithms to work with non-Boolean oracles, leading to two new algorithms. Unlike Boolean oracles, the eigenvalues of a non-Boolean oracle are not restricted to be ±1. 1) The non-Boolean amplitude amplification algorithm preferentially amplifies the amplitudes of the eigenstates based on a given objective function. 2) The quantum mean estimation algorithm estimates the expected value of a unitary operator under a given state with a quadratic speedup over the corresponding classical algorithm. 3) I will briefly discuss how these algorithms allow for training quantum neural networks in an inherently quantum manner.

Primary author

Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

Presentation materials