Study of Feature Importance for Quantum Machine Learning Models (2202.11204v4)

Abstract: Predictor importance is a crucial part of data preprocessing pipelines in classical and quantum machine learning (QML). This work presents the first study of its kind in which feature importance for QML models has been explored and contrasted against their classical machine learning (CML) equivalents. We developed a hybrid quantum-classical architecture where QML models are trained and feature importance values are calculated from classical algorithms on a real-world dataset. This architecture has been implemented on ESPN Fantasy Football data using Qiskit statevector simulators and IBM quantum hardware such as the IBMQ Mumbai and IBMQ Montreal systems. Even though we are in the Noisy Intermediate-Scale Quantum (NISQ) era, the physical quantum computing results are promising. To facilitate current quantum scale, we created a data tiering, model aggregation, and novel validation methods. Notably, the feature importance magnitudes from the quantum models had a much higher variation when contrasted to classical models. We can show that equivalent QML and CML models are complementary through diversity measurements. The diversity between QML and CML demonstrates that both approaches can contribute to a solution in different ways. Within this paper we focus on Quantum Support Vector Classifiers (QSVC), Variational Quantum Circuit (VQC), and their classical counterparts. The ESPN and IBM fantasy football Trade Assistant combines advanced statistical analysis with the natural language processing of Watson Discovery to serve up personalized trade recommendations that are fair. Here, player valuation data of each player has been considered and this work can be extended to calculate the feature importance of other QML models such as Quantum Boltzmann machines.

• The paper introduces a hybrid quantum-classical framework to assess feature importance in QML models using real-world datasets.
• It uses established methods like Permutation Importance and Accumulated Local Effects tailored for quantum computing challenges.
• Findings reveal diverse feature importance metrics, highlighting the complementary strengths of QML and classical approaches.

Examination of Feature Importance in Quantum Machine Learning Models

The paper "Study of Feature Importance for Quantum Machine Learning Models" advances the understanding of feature importance in the burgeoning field of Quantum Machine Learning (QML). Feature importance is a critical aspect of data preprocessing in machine learning, and its significance is equally pivotal for QML models where classical data transitions into quantum states. This paper is unprecedented in its attempt to explore and contrast feature importance in QML with its Classical Machine Learning (CML) counterparts.

The research introduces a hybrid quantum-classical architecture, utilizing QML models in tandem with classical algorithms to estimate feature importance on real-world datasets. Notably, the experimental setup involved ESPN Fantasy Football data, processed using Qiskit statevector simulators, and IBM's quantum hardware, specifically the IBM Mumbai and IBM Montreal systems.

A key revelation of this paper is the significant variability observed in the feature importance magnitudes of quantum models compared to classical models. This variability underscores the unique data manipulations occurring within the high-dimensional Hilbert space in which QML operates. The paper highlights the complementary nature of QML and CML approaches, as evident in the diversity of feature importance metrics.

Methodology and Implementation

The research utilized Quantum Support Vector Classifiers (QSVC) and Variational Quantum Circuits (VQC) alongside their classical equivalents. Two primary algorithms for estimating feature importance were deployed: Permutation Importance (PI) and Accumulated Local Effects (ALE).

Permutation Importance calculates feature relevance by reshuffling predictor values and assessing changes in model accuracy, while ALE examines the effect of predictors in local value windows. These methods were integrated into QML frameworks to address challenges specific to quantum computation such as limited qubit resources and exponential data growth.

For the experiments, the IBM Trade Assistant’s Fantasy Football dataset was used, comprising 146 features and labeled with binary trade valuation scores. To navigate the constraints of current quantum hardware, the researchers devised a tiering method, grouping features into 10-feature tiers processed across quantum circuits.

Impact and Implications

A critical observation was the enhanced diversity in feature importance when applying QML approaches. This diversity, analyzed through a new set of diversity measures developed in the paper, not only emphasizes the possible complementary applications of QML and CML for predictive modeling but highlights the potential for quantum models to offer insights not evident through classical methods alone.

Theoretical implications extend to developing new unsupervised machine learning algorithms adapted to the quantum computing paradigm. The practical applications point towards enhancing tools like ESPN's Fantasy Football Trade Assistant, suggesting broader prospects for QML in commercial applications if quantum computational capacity continues to grow.

Future Directions

Looking forward, the paper sets a foundational platform for further exploration of feature importance across other quantum models like Quantum Boltzmann Machines and Quantum Generative Adversarial Networks. Moreover, enhancing the quantum hardware capabilities (in terms of quantum volume and number of qubits) will be vital for handling larger datasets and more complex feature spaces.

With continuous advancements in quantum technology, there is significant promise in the scalability of these methods. Future studies should focus on optimizing quantum circuits for higher efficiency and expanding their applicability to diverse datasets beyond those used in this research.

In conclusion, this paper contributes a novel approach to calculating and validating feature importance within QML models, marking an essential step toward the integration of quantum algorithms into mainstream machine learning practices. The methodologies reported herein pave the way for following research trajectories aimed at realizing more sophisticated and applicable QML solutions.