Procedures for Evaluating Classical, Quantum, and Hybrid Machine Learning Algorithms

Quantum machine learning (QML), an emerging discipline with applications in various domains, has the potential to dramatically improve deep learning while reducing model complexity. Quantum computers are well suited for machine learning (ML) tasks, especially because ML models are rapidly growing and require large amounts of data to learn. For example, classical computers that train large language and foundation models typically require supercomputers. If a need exists to double the number of model parameters, one will have to double the size of the supercomputer needed to train it. A quantum computer's parameter or state space grows exponentially with the number of qubits. This means doubling the number of model parameters only requires adding one quantum bit. Another advantage is that quantum computers can use entanglement and interference to potentially learn patterns with less data. Currently, no existing solutions compares the performance of classical and quantum machine learning algorithms for a workflow comprised of multiple ML tasks. This poster presents a QML workspace to explore and verify these QML algorithms with different available quantum frameworks such as Qisklt, CUDA Quantum, and PennyLane in a consistent, reproducible, and theoretically sound way and provide a comparison with similar classical machine learning algorithms.