The k-nearest neighbor algorithm (k-NN) is a straightforward and highly effective classical machine learning method that categorises unidentified test instances by identifying the k-nearest Neighbors from the collection of m-training instances. It is widely acknowledged that integrating machine learning algorithms with quantum computing offers tremendous benefits in some applications involving higher dimensional datasets. This research examines the quantum k-nearest neighbor (QK-NN) algorithm executed within the PennyLane’s default. qubit framework. The paper assesses the benefits of incorporating quantum computing into machine learning problems by focusing on quantum circuits and quantum feature maps for quantum-enhanced classifiers. The PennyLane framework is employed to model the behaviour on four datasets viz. Iris, Wine, Rain, Indian Lever. The implementation results reveal that quantum-enhanced k-NN Combined feature maps exhibit superior performance on most of the Accuracy, Precision, Recall and F-1 score parameters compared to individual feature maps.
The k-nearest neighbor algorithm (k-NN) is a straightforward and highly effective classical machine learning method that categorises unidentified test instances by identifying the k-nearest Neighbors from the collection of m-training instances. It is widely acknowledged that integrating machine learning algorithms with quantum computing offers tremendous benefits in some applications involving higher dimensional datasets. This research examines the quantum k-nearest neighbor (QK-NN) algorithm executed within the PennyLane’s default. qubit framework. The paper assesses the benefits of incorporating quantum computing into machine learning problems by focusing on quantum circuits and quantum feature maps for quantum-enhanced classifiers. The PennyLane framework is employed to model the behaviour on four datasets viz. Iris, Wine, Rain, Indian Lever. The implementation results reveal that quantum-enhanced k-NN Combined feature maps exhibit superior performance on most of the Accuracy, Precision, Recall and F-1 score parameters compared to individual feature maps.
k-Nearest Neighbours (k-NN) is a widely used non-parametric, instance-based classification algorithm; however, its traditional implementation suffers from high computational cost, scalability limitations, and performance degradation in high-dimensional spaces. To address these challenges, this paper proposes a hybrid quantum-classical k-Nearest Neighbours (Qk-NN) model that integrates quantum feature encoding and quantum-assisted feature transformation with classical k-NN classification. The proposed approach is evaluated on the Pima Indians Diabetes Dataset using feature subsets of 5, 6, and 7 dimensions, selected due to quantum circuit scalability constraints. Quantum feature transformations are implemented through parameterized quantum circuits simulated on classical hardware in an ideal noiseless environment. Model performance is assessed using accuracy and log loss with a fixed Neighbourhood size of k = 5. Experimental results show that the Hybrid Qk-NN is superior to the classical k-NN in each feature subset. In particular, with five features, the Hybrid Qk-NN registers an accuracy of 77. 62 % while classical k-NN only achieves 71. 84 %, meanwhile, the loss values are quite lower. Quantum-assisted feature mapping capable of increasing the performance of classification is suggested by these results, and hybrid quantum-classical models are shown to be a viable bridge to the quantum machine learning world in the future.
Quantum-assisted models can surpass classical counterparts in specific domains by leveraging quantum superposition entanglement for more efficient data representation. This study investigates a Quantum neural network framework executed on various Quantum simulators, including ionq, Cirq, Pennylane and Quantinnum backends. The iris, wine, Indian liver patient and rain datasets are employed as benchmarking datasets to assess the efficacy of quantum-based classification models. The QNN utilises parameterized quantum circuits and quantum feature maps to learn complex patterns in data, optimizing classification decision boundaries through quantum optimization techniques. The study evaluates QNN performance across different quantum hardware platforms, assessing key metrics such as accuracy, recall, F1 score, and precision. Preliminary results indicate that QNN-based classification effectively captures feature dependencies through quantum correlations, offering competitive performance in data classification tasks. These findings contribute to the broader exploration of quantum-enhanced classification and the potential of quantum devices in real-world data analysis.
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving Om search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM.
This paper presents a novel quantum K-nearest neighbors (QKNN) algorithm, which offers improved performance over the classical k-NN technique by incorporating quantum computing (QC) techniques to enhance classification accuracy, scalability, and robustness. The proposed modifications focus on optimizing quantum data encoding using Hadamard and rotation gates, ensuring more effective rendering of classical data in quantum states. In addition, the quantum feature extraction process is significantly enhanced by the use of entangled gates such as IsingXY and CNOT, which enables better feature interactions and class separability. A novel quantum distance metric, based on the swap test, is introduced to calculate similarity measures between various quantum states, offering superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We assess the achievement of the proposed QKNN algorithm on three benchmark datasets: Wisconsin Breast Cancer, Iris, and Bank Note Authentication, and have noted its superior performance relative to both classical k-NN (CKNN) and Quantum Neural Network (QNN). The proposed QKNN algorithm is found to achieve prediction accuracies of 98.25%, 100%, and 99.27% ,respectively, for the three datasets, while the customized QNN shows prediction accuracies of only 97.17%, 83.33%, and 86.18%, respectively. Furthermore, we address the challenges of quantum noise by incorporating a repetition encoding-based error mitigation strategy, which ensures the stability and resilience of the algorithm in noisy quantum environments. The results highlight the potential of the proposed QKNN as a scalable, efficient and robust quantum-enhanced machine learning algorithm, especially in high-dimensional and complex datasets, when traditional approaches frequently fail.
Quantum machine learning (QML) has emerged as a promising field, combining the power of quantum computing with classical machine learning techniques to solve complex computational tasks. As the demand for efficient quantum simulations grows, multiple QML frameworks, including PennyLane, Qiskit, and TensorFlow Quantum (TFQ), have been developed to facilitate hybrid quantum-classical computations. This study aims to evaluate and compare the performance of three leading QML frameworks PennyLane, Qiskit, and TensorFlow Quantum in simulating quantum machine learning models, focusing on accuracy, execution time, and noise tolerance. The study examined three leading quantum machine learning (QML) frameworks PennyLane (v0.24), Qiskit (v0.43.2), and TensorFlow Quantum (TFQ, v0.7) each tested on a 64-bit Ubuntu Linux system with an Intel Core i7 processor, 32 GB RAM, and NVIDIA GeForce RTX 3080 GPU. Quantum algorithms like variational quantum circuits (VQCs) and quantum support vector machines (QSVMs) were simulated using these frameworks. Various quantum gates, including Pauli-X, Y, Z, Hadamard, CNOT, and Rotation gates (Rx, Ry, Rz), were used for parameter optimization and quantum superposition. The Iris and MNIST datasets were adapted for quantum encoding using Amplitude and Angle Encoding for binary classification tasks. Simulations were run on classical computers, while select circuits were executed on IBM Quantum’s Falcon r5.11 processor to compare hardware performance. PennyLane achieved the highest accuracy (92%) in quantum simulations, excelling in hybrid quantum-classical model integration. TensorFlow Quantum provided the fastest execution time, especially in classical simulations, making it suitable for rapid prototyping. Qiskit produced deeper quantum circuits but had higher error rates (6%) on real quantum hardware, highlighting noise and decoherence effects. The VQC models optimized by stochastic gradient descent (SGD) showed significant improvements in model performance, and QSVMs effectively classified both linearly separable and non-separable datasets, demonstrating the potential of quantum algorithms over classical approaches. Statistical analysis confirmed the significance of the results (p < 0.05). PennyLane is the most robust framework for hybrid quantum-classical models, while TensorFlow Quantum is suited for rapid prototyping. Qiskit, despite longer execution times, excels in hardware implementation. The choice of framework should align with the specific needs of quantum machine learning tasks.
Quantum Machine Learning signifies a possible collaboration of quantum computing and artificial intelligence, presenting potential benefits in addressing intricate computational challenges. Hybrid quantum-classical neural networks (QNNs) have developed as a formidable instrument for classification tasks, utilizing quantum feature encoding and classical optimization. This research employs a hybrid Quantum Neural Network framework utilizing Google Cirq, Classical data is encoded into quantum states via Z, ZZ, Pauli, and hybrid feature maps to use the distinctive attributes of quantum processing in representing intricate interactions among characteristics. The hybrid model combines quantum circuits for feature extraction with classical neural networks for training and assessment. The performance metrics—accuracy, precision, recall, and F1-score—demonstrate that the hybrid feature map method regularly surpasses individual feature maps, especially in multi-class and binary classification problems. However, when benchmarked against classical deep learning models such as Convolutional Neural Networks (CNNs), the CNN achieved superior results in classification accuracy and efficiency. This underscores the current limitations of quantum models while highlighting their potential for further advancements.