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Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines

Tasar, Davut Emre, Koruyan, Kutan, Tasar, Ceren Ocal

arXiv.org Artificial Intelligence

This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classification prowess of classical SVM and Quantum Support Vector Machines (QSVM) operational on quantum hardware over the Iris dataset. The methodology embraced encapsulates an extensive array of experiments orchestrated through the Qiskit library, alongside hyperparameter optimization. The findings unveil that in particular scenarios, QSVMs extend a level of accuracy that can vie with classical SVMs, albeit the execution times are presently protracted. Moreover, we underscore that augmenting quantum computational capacity and the magnitude of parallelism can markedly ameliorate the performance of quantum machine learning algorithms. This inquiry furnishes invaluable insights regarding the extant scenario and future potentiality of machine learning applications in the quantum epoch. Colab: https://t.ly/QKuz0


Implications of Machine Learning/AI and Distributed Ledgers on Finance in the Quantum Age - Fintech Circle

#artificialintelligence

The convergence of three emergent technologies: Intelligent Learning Systems (AI, ML), Quantum Computing (QC) and Distributed Ledgers (DLT) will likely have a transforming impact on our society and shape the future of Information Technology. What will be the direction of future AI, ML technology initiatives in the financial industry, in a broad spectrum of research, development & commercialisation efforts for ML, AI and DLT in the quantum domain? As an example, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. This will have a profound impact on Investment Portfolio Management, High Frequency Trading, Loan Origination and processing, Fraud Detection, Risk Modelling and calculating credit ratings. Future use cases that can leverage these three technologies include a secure and transparent distributed personal financial data (PII) marketplace, and a secondary mortgage platform with a tokenised exchange combining DLT, post-QC cryptographic algorithms and deep learning technologies.