Incremental Data-Uploading for Full-Quantum Classification

TL;DR

This paper introduces 'incremental data-uploading', a new quantum data encoding method for NISQ devices that improves data representation and classification accuracy with minimal pre-processing, demonstrated on MNIST datasets.

Contribution

The paper presents a novel incremental data-uploading encoding pattern that enhances data representation in quantum circuits for NISQ devices, addressing limitations of existing schemes.

Findings

Improved classification accuracy on MNIST and Fashion-MNIST datasets.

Enhanced effective dimension indicating better data representation.

Reduced pre-processing requirements for quantum data encoding.

Abstract

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.