Procedures to create, train, save, and reload a VQC with the Qiskit quantum machine learning framework: generating a synthetic dataset and building a parameterised quantum circuit using the RealAmplitudes ansatz as an encoding circuit to encode classical data into quantum states. A hybrid quantum/classical approach is utilised to train the variational quantum circuit on the dataset. The COBYLA optimizer and the sampling primitives from Qiskit are employed. The notebook outlines the steps for saving the trained quantum model to disc and then loading it for the incremental training. This experiment illustrates the model’s adaptability and consistency in the results. Visualisation tools are used to monitor the progress of the training and evaluate the model’s performance.. This example illustrates that the modern quantum software tools are ready for assessment in the NISQ era and can support the creation of sustainable workflows for quantum machine learning.
Procedures to create, train, save, and reload a VQC with the Qiskit quantum machine learning framework: generating a synthetic dataset and building a parameterised quantum circuit using the RealAmplitudes ansatz as an encoding circuit to encode classical data into quantum states. A hybrid quantum/classical approach is utilised to train the variational quantum circuit on the dataset. The COBYLA optimizer and the sampling primitives from Qiskit are employed. The notebook outlines the steps for saving the trained quantum model to disc and then loading it for the incremental training. This experiment illustrates the model’s adaptability and consistency in the results. Visualisation tools are used to monitor the progress of the training and evaluate the model’s performance.. This example illustrates that the modern quantum software tools are ready for assessment in the NISQ era and can support the creation of sustainable workflows for quantum machine learning.
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) 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) unites quantum computing with modern AI to achieve potential gains in expressivity, runtime, and sample efficiency. This paper synthesizes advances in quantum feature maps, variational quantum circuits (VQCs), and hybrid quantum-classical workflows deployable on noisy intermediate-scale quantum (NISQ) hardware. We outline design principles for data encoding, ansatz selection, and trainability, and summarize error-aware practices—error mitigation, shot-frugal estimation, and compiler co-design. Using synthetic but instructive experiments, we illustrate scaling behavior, noise sensitivity, end-to-end inference latency across backends, and optimizer convergence in variational training. We conclude with an agenda emphasizing domain-aligned encodings, rigorous benchmarking, and reproducible open science to validate advantage beyond asymptotic claims.
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.
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 promising paradigm that combines principles of quantum computation with classical learning techniques to address challenges posed by complex, high-dimensional, and non-linear datasets. This work presents a systematic experimental study of quantum-enhanced classification and clustering using hybrid quantum–classical models evaluated on synthetically generated datasets. Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC) are implemented using the Qiskit framework and benchmarked against established classical algorithms. The synthetic datasets are carefully designed to control linearity, dimensionality, class overlap, and nonlinear structure, enabling a detailed performance comparison under both ideal and noisy simulation conditions. The experimental results indicate that quantum feature representations can enhance class separability in non-linear scenarios, while also highlighting the practical limitations imposed by noise sensitivity and circuit depth constraints inherent to Noisy Intermediate-Scale Quantum (NISQ) devices .