Quantum Machine Learning: Fad or Future?

TL;DR

This paper investigates the potential of quantum machine learning to overcome classical limitations in model size, convergence, and noise robustness, by testing quantum approaches inspired by Farhi et al. using TensorFlow Quantum.

Contribution

It replicates and extends prior quantum machine learning work, evaluating its advantages and limitations compared to classical methods.

Findings

Quantum approaches show promise in convergence speed.

Potential for improved robustness to noisy data.

Limitations still prevent quantum ML from mainstream adoption.

Abstract

For the last few decades, classical machine learning has allowed us to improve the lives of many through automation, natural language processing, predictive analytics and much more. However, a major concern is the fact that we're fast approach the threshold of the maximum possible computational capacity available to us by the means of classical computing devices including CPUs, GPUs and Application Specific Integrated Circuits (ASICs). This is due to the exponential increase in model sizes which now have parameters in the magnitude of billions and trillions, requiring a significant amount of computing resources across a significant amount of time, just to converge one single model. To observe the efficacy of using quantum computing for certain machine learning tasks and explore the improved potential of convergence, error reduction and robustness to noisy data, this paper will look forth to test and verify the aspects in which quantum machine learning can help improve over classical machine learning approaches while also shedding light on the likely limitations that have prevented quantum approaches to become the mainstream. A major focus will be to recreate the work by Farhi et al and conduct experiments using their theory of performing machine learning in a quantum context, with assistance from the Tensorflow Quantum documentation.