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Machine learning and quantum computing are two technologies that are causinga paradigm shift in the performance and behavior of certain algorithms,achieving previously unattainable results. Machine learning (kernelclassification) has become ubiquitous as the forefront method for patternrecognition and has been shown to have numerous societal applications. Whilenot yet fault-tolerant, Quantum computing is an entirely new method ofcomputation due to its exploitation of quantum phenomena such as superpositionand entanglement. While current machine learning classifiers like the SupportVector Machine are seeing gradual improvements in performance, there are stillsevere limitations on the efficiency and scalability of such algorithms due toa limited feature space which makes the kernel functions computationallyexpensive to estimate. By integrating quantum circuits into traditional ML, wemay solve this problem through the use of quantum feature space, a techniquethat improves existing Machine Learning algorithms through the use ofparallelization and the reduction of the storage space from exponential tolinear. This research expands on this concept of the Hilbert space and appliesit for classical machine learning by implementing the quantum-enhanced versionof the K nearest neighbors algorithm. This paper first understands themathematical intuition for the implementation of quantum feature space andsuccessfully simulates quantum properties and algorithms like Fidelity andGrover's Algorithm via the Qiskit python library and the IBM Quantum Experienceplatform. The primary experiment of this research is to build a noisyvariational quantum circuit KNN (QKNN) which mimics the classification methodsof a traditional KNN classifier. The QKNN utilizes the distance metric ofHamming Distance and is able to outperform the existing KNN on a 10-dimensionalBreast Cancer dataset.

QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces