  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.
