Quantum feature encoding optimization

TL;DR

This paper explores optimizing data encoding methods in Quantum Machine Learning to significantly enhance model performance, demonstrating improvements through classical data manipulation and real quantum hardware experiments.

Contribution

It introduces a novel approach of adjusting data encoding strategies in QML, beyond the ansatz, and validates its effectiveness across various datasets and hardware.

Findings

Optimized encoding improves QML accuracy across datasets.

Classical data manipulation enhances quantum model performance.

Successful implementation on 100-qubit quantum hardware.

Abstract

Quantum Machine Learning (QML) holds the promise of enhancing machine learning modeling in terms of both complexity and accuracy. A key challenge in this domain is the encoding of input data, which plays a pivotal role in determining the performance of QML models. In this work, we tackle a largely unaddressed aspect of encoding that is unique to QML modeling -- rather than adjusting the ansatz used for encoding, we consider adjusting how data is conveyed to the ansatz. We specifically implement QML pipelines that leverage classical data manipulation (i.e., ordering, selecting, and weighting features) as a preprocessing step, and evaluate if these aspects of encoding can have a significant impact on QML model performance, and if they can be effectively optimized to improve performance. Our experimental results, applied across a wide variety of data sets, ansatz, and circuit sizes, with a representative QML approach, demonstrate that by optimizing how features are encoded in an ansatz we can substantially and consistently improve the performance of QML models, making a compelling case for integrating these techniques in future QML applications. Finally we demonstrate the practical feasibility of this approach by running it using real quantum hardware with 100 qubit circuits and successfully achieving improved QML modeling performance in this case as well.