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IEEE International Conference for Women in Computing (InCoWoCo)

Assessment of the Most Effective Approach for Feature Acquisition Utilizing Variational Quantum Classifiers for Real-World Datasets

Ravuri Mohana Ramana, S. V. N. Sreenivasu, P. E. Satyanarayana, Seetharam K, Patri Vasundhara, B.Mahaboob

Published Nov 14, 2025• 0 citations
Publisher

Abstract

Quantum-enhanced methods have recently attracted significant attention for addressing a wide range of machine learning problems. Many approaches exploit the hybrid resources of noisy quantum devices in combination with classical computing, particularly for supervised learning. Among these, the variational quantum classifier (VQC) offers potential quantum advantage by mapping classical data into high-dimensional quantum feature spaces that are difficult to simulate classically. While several quantum data-encoding methods have been explored, the efficient mapping of discrete attributes—such as postal codes, age groups, or categorical identifiers—has received limited attention, despite their importance in real-world classification tasks. In this work, we introduce an efficient encoding strategy based on Quantum Random Access Codes (QRACs) for embedding discrete features into compact quantum representations. QRACs enable multiple bits of information to be encoded into fewer qubits, thereby reducing resource requirements while preserving feature separability. We present several QRAC-based schemes, analyze their theoretical properties, and evaluate their performance through algebraic simulations. Empirical studies on real-world datasets, implemented on both simulators and quantum hardware, demonstrate that QRA-Cenhanced VQCs achieve faster training, reduced parameter counts, and competitive classification accuracy. These results highlight QRACs as a practical tool for advancing the applicability of quantum machine learning to structured datasets.

DOI
10.1109/incowoco68239.2025.11406952

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Quantum-enhanced methods have recently attracted significant attention for addressing a wide range of machine learning problems. Many approaches exploit the hybrid resources of noisy quantum devices in combination with classical computing, particularly for supervised learning. Among these, the variational quantum classifier (VQC) offers potential quantum advantage by mapping classical data into high-dimensional quantum feature spaces that are difficult to simulate classically. While several quantum data-encoding methods have been explored, the efficient mapping of discrete attributes—such as postal codes, age groups, or categorical identifiers—has received limited attention, despite their importance in real-world classification tasks. In this work, we introduce an efficient encoding strategy based on Quantum Random Access Codes (QRACs) for embedding discrete features into compact quantum representations. QRACs enable multiple bits of information to be encoded into fewer qubits, thereby reducing resource requirements while preserving feature separability. We present several QRAC-based schemes, analyze their theoretical properties, and evaluate their performance through algebraic simulations. Empirical studies on real-world datasets, implemented on both simulators and quantum hardware, demonstrate that QRA-Cenhanced VQCs achieve faster training, reduced parameter counts, and competitive classification accuracy. These results highlight QRACs as a practical tool for advancing the applicability of quantum machine learning to structured datasets.
Recent days have witnessed significant interests in applying quantum-enhanced techniques for solving a variety of machine learning tasks. Variational methods that use quantum resources of imperfect quantum devices with the help of classical computing techniques are popular for supervised learning. Variational quantum classification (VQC) is one of such methods with possible quantum advantage in using quantum-enhanced features that are hard to compute by classical methods. Its performance depends on the mapping of classical features into a quantum-enhanced feature space. Although there have been many quantum-mapping functions proposed so far, there is little discussion on efficient mapping of discrete features, such as age group, zip code, and others, which are often significant for classifying datasets of interest. We first introduce the use of quantum random-access coding (QRAC) to map such discrete features efficiently into limited number of qubits for VQC. In numerical simulations, we present a range of encoding strategies and demonstrate their limitations and capabilities. We experimentally show that QRAC can help speeding up the training of VQC by reducing its parameters via saving on the number of qubits for the mapping. We confirm the effectiveness of the QRAC in VQC by experimenting on classification of real-world datasets with both simulators and real quantum devices.
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning.
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning1.
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC) to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. The proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances to classify real-world datasets, and thus its possibility to use near-term quantum computers for machine learning.
Quantum Machine Learning (QML) has emerged as a compelling research domain that unites quantum computing principles with classical learning theory to address computationally demanding classification problems.This paper investigates two principal QML architectures-Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN)-within the context of binary classification.We present an end-to-end hybrid quantum-classical pipeline applied to the UCI Mushroom dataset, comprising 22 categorical attributes, and demonstrate that quantumenhanced models can achieve competitive classification performance under present-day hardware constraints.The pipeline integrates Quantum Random Access Code (QRAC) encoding for dimensionality reduction, ZZFeatureMapbased quantum feature mapping, RealAmplitudes parameterized ansatz, and classical optimizer-driven parameter updates using the COBYLA algorithm.The complete system is deployed through a Flask web interface and containerized with Docker for reproducibility.Experimental results on a quantum simulator indicate a classification accuracy of 87.4%, precision of 0.891, and recall of 0.863, establishing the practical viability of NISQ-era quantum classifiers for structured categorical data.The paper further analyzes convergence behavior, circuit depth effects, and comparative performance against classical baselines, providing insights into the strengths and limitations of current QML architectures for real-world classification deployment.
Quantum machine learning (QML) integrates parameterized quantum circuits with classical optimization to address supervised learning tasks on near-term quantum hardware. This paper presents a compact mushroomclassification framework based on Quantum Random Access Coding (QRAC) and a Variational Quantum Classifier (VQC). The system uses the UCI mushroom dataset with 8,124 samples and 22 categorical attributes, transforms symbolic mushroom characteristics using label encoding, compresses the feature representation into eight quantum features/qubits through QRAC, and applies a ZZFeatureMap with a RealAmplitudes ansatz for binary edible/poisonous classification. The trained model and encoders are stored for reuse and connected to an interactive web interface that accepts mushroom attributes and returns a safety-oriented prediction. The demonstrated interface reports 62.71% accuracy and provides clear edible or poisonous outputs. The work highlights the role of data encoding, qubit reduction, model persistence, and user-facing deployment in practical QML on NISQ-era systems. Keywords— Quantum machine learning, Variational Quantum Classifier, QRAC, Qiskit, ZZFeatureMap, RealAmplitudes, mushroom classification, NISQ.