4 papers
Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries
Natsuto Isogai, Satoshi Yoshida, Mio Murao
Whether quantum computation can reduce the amount of data required for conventional machine learning is a central problem in quantum machine learning. In probably approximately cor…
A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features
Natsuto Isogai, Mio Murao, Hayata Yamasaki
Quantum-inspired classical algorithms have dequantized several quantum machine learning routines by replacing quantum linear-algebra subroutines with classical counterparts. Howeve…
Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm
Natsuto Isogai, Hayata Yamasaki, Sho Sonoda +1
Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks f…
Advantage of Quantum Machine Learning from General Computational Advantages
Hayata Yamasaki, Natsuto Isogai, Mio Murao
An overarching milestone of quantum machine learning (QML) is to demonstrate the advantage of QML over all possible classical learning methods in accelerating a common type of lear…