[challenge]: How to build a Quantum-Native Learning Algorithm?

Released by

Junkai Wang

Contact email

WangTheoPhys@outlook.com

Method

Quantum Circuit Simulation; Quantum Artificial Intelligence

Challenge issue

The apparent goal of Quantum Artificial Intelligence (Q4AI) is to use quantum computers for AI in order to realize (a polynomial or exponential) speed-up. However, in my opinion, the final objective of this track is to explore what constitutes a "quantum-native" learning algorithm on a quantum computer and to identify the specific characteristics such an algorithm would possess.

I think this road have been greatly inspired by the recent work of Prof.John Preskill, Prof.Dong-Ling Deng, and other people. We need to successfully implement a learning algorithm and achieve non-linearity within quantum systems—perhaps even exploring non-linearities that could theoretically extend beyond standard quantum mechanics, like, quantum field theory. (I got this belief from Prof. Di Luo)

No files, tests, or entry points are named. Start by reviewing the repository's Python implementation and the challenge statement to determine a concrete learning algorithm and measurable definition of quantum-native behavior; the issue does not specify what completion or validation should look like.