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.
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.
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.
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-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.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Quantum Machine Learning (QML) is a newly emerging research area at the intersection of classical machine learning (CML) and quantum computing (QC). Data is becoming voluminous rapidly, so it is challenging for classical computers to train Machine Learning (ML) models over massive datasets. The hope is that quantum physics' intrinsic features, such as entanglement, superposition, and interference, could be exploited as resources for training ML models on big datasets that would otherwise be relatively impossible for classical computers. It is theoretically proven that quantum computers have an exponential-time advantage over their classical counterparts in solving several problems, e.g., complex large dimensional matrix multiplication, factorization problem, unstructured database search, etc. QML models attempt to find a quantum advantage over their classical counterparts. Variational Quantum Classifiers (VQC) are hybrid quantum neural networks to perform the task of classification using QML models. VQC models in the present NISQ (Noisy Intermediate Scale Quantum — 50 to 100 qubits) era can produce comparable and even better results than Classical models. In this article, we examined the performance of a VQC while performing a simple binary classification task. In this article, we use a VQC to evaluate this method's performance empirically. We constructed a VQC to predict the label of fresh input for the typical Iris dataset comprising pairings of target outputs and training inputs. Our quantum classifier can reasonably predict species labels using only four qubits. These levels are fairly good compared to the accuracy levels attained by classical classifiers.
Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.