  The data representation in a machine-learning model strongly influences its
performance. This becomes even more important for quantum machine learning
models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding
high dimensional data into a quantum circuit for a NISQ device without any loss
of information is not trivial and brings a lot of challenges. While simple
encoding schemes (like single qubit rotational gates to encode high dimensional
data) often lead to information loss within the circuit, complex encoding
schemes with entanglement and data re-uploading lead to an increase in the
encoding gate count. This is not well-suited for NISQ devices. This work
proposes 'incremental data-uploading', a novel encoding pattern for high
dimensional data that tackles these challenges. We spread the encoding gates
for the feature vector of a given data point throughout the quantum circuit
with parameterized gates in between them. This encoding pattern results in a
better representation of data in the quantum circuit with a minimal
pre-processing requirement. We show the efficiency of our encoding pattern on a
classification task using the MNIST and Fashion-MNIST datasets, and compare
different encoding methods via classification accuracy and the effective
dimension of the model.

Quantum Physics;Computer Vision and Pattern Recognition;Machine Learning