On Depth, Robustness and Performance Using the Data Re-Uploading Single-Qubit Classifier

Abstract

Quantum machine learning (QML) is a new field in its infancy, promising performance enhancements over many classical machine learning (ML) algorithms. Data reuploading is a QML algorithm with a focus on utilizing the power of a singular qubit as an individually capable classifier. Recently, there have been studies set out to explore the concept of data re-uploading in a classification setting, however, important aspects are often not considered in experiments, which may hinder our understanding of the methodology's performance. In this work, we conduct an analysis of the single-qubit data re-uploading methodology, in relation to the effect that system depth has on classification and robustness performances against the influence of environmental noise during training. This is aimed towards bridging together previous works in order to solidify the concepts of the methodology, and provide reasonable insight into how transferable the methodology is when applied to non-synthetic data. To further demonstrate the findings, we also analyse the results of a case study using a subset of MNIST data. From this work, our experimental results support that an increase in system depth can lead to higher classification performances, as well as improved stability during training in noisy environments, with the sharpest performance improvements seemingly occurring between 1-3 uploading layer repetitions. Leading on from our experimental results, we suggest areas for further exploration, to ensure we can maximize classification performance when using the data re-uploading methodology.

Efficient Quantum Image Classification Using Single Qubit Encoding

“…A speculative suggestion here may be to investigate whether applying additional filters may mimic the effect of data reuploading, which is suggested to improve expressivity within the qubit [52], and thus may provide some robustness to noisy environments with additional layers, in particular the amplitude channel [37]. Exploring modifications may aid in the robustness of the proposed method, and perhaps decrease any drop in classification as seen in figure 9 within noisy environments.…”

“…What makes this proposal particularly appealing is its capability to encode an arbitrary amount of data into a complex feature space, whilst requiring the use of a single qubit only. The proposal of this work was examined further in [37], where the single-qubit classifier was still found to remain effective for a multitude of tasks, even in noisy quantum environments.…”

“…Amplitude damping models energy relaxation within a qubit that occurs via interactions with the environment over time. More information on this can be found in [37,38]. Phase damping models environmental noise that affects the representation of quantum information, without changes being made to the status of excitation within the qubit itself.…”

QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

“…high-dimensional data in a small number of qubits using sequential rotation operations [25][26][27]. Technically, both these approaches can exploit arbitrarily small quantum hardware to solve ML tasks of any scale (a single qubit can be used to build a classification model involving 1,000 features).…”

“…The lower-dimensional data is used as inputs to the Baseline QNN model [17,23,24]. In (B), classical features are loaded repeatedly in a singlequbit using a series of rotation operations [25][26][27].…”