Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators

TL;DR

This paper introduces a novel quantum machine learning approach using Kerr-nonlinear Parametric Oscillators, leveraging higher excited states to achieve high expressibility with fewer resources, promising efficiency in the NISQ era.

Contribution

It proposes using higher excited states of KPOs for expressive quantum learning, reducing resource requirements compared to traditional qubit-based methods.

Findings

Higher expressibility with a single KPO mode than six qubits.

Efficient quantum learning without extensive data reuploading.

Potential for resource-efficient quantum machine learning in NISQ devices.

Abstract

Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model in the conventional quantum computing architecture. However, the data reuploding tends to require large amount of quantum resources, which motivates us to find an alternative strategy for realizing the expressive quantum machine learning efficiently. In this paper, we propose quantum machine learning with Kerr-nonlinear Parametric Oscillators (KPOs), as another promising quantum computing device. The key idea is that we use not only the ground state and first excited state but also use higher excited states, which allows us to use a large Hilbert space even if we have a single KPO. Our numerical simulations show that the expressibility of our method with only one mode of the KPO is much higher than that of the conventional method with six qubits. Our results pave the way towards resource efficient quantum machine learning, which is essential for the practical applications in the NISQ era.