Quantum Machine Learning with Jessica Pointing

The New Quantum Era - innovation in quantum computing, science and technology

In this episode of The New Quantum Era podcast, hosts Sebastian Hassinger and Kevin Roney interview Jessica Pointing, a PhD student at Oxford studying quantum machine learning.

Classical Machine Learning Context

• Deep learning has made significant progress, as evidenced by the rapid adoption of ChatGPT
• Neural networks have a bias towards simple functions, which enables them to generalize well on unseen data despite being highly expressive
• This “simplicity bias” may explain the success of deep learning, defying the traditional bias-variance tradeoff

Quantum Neural Networks (QNNs)

• QNNs are inspired by classical neural networks but have some key differences
• The encoding method used to input classical data into a QNN significantly impacts its inductive bias
• Basic encoding methods like basis encoding result in a QNN with no useful bias, essentially making it a random learner
• Amplitude encoding can introduce a simplicity bias in QNNs, but at the cost of reduced expressivity
  • Amplitude encoding cannot express certain basic functions like XOR/parity
• There appears to be a tradeoff between having a good inductive bias and having high expressivity in current QNN frameworks

Implications and Future Directions

• Current QNN frameworks are unlikely to serve as general purpose learning algorithms that outperform classical neural networks
• Future research could explore:
  • Discovering new encoding methods that achieve both good inductive bias and high expressivity
  • Identifying specific high-value use cases and tailoring QNNs to those problems
  • Developing entirely new QNN architectures and strategies
• Evaluating quantum advantage claims requires scrutiny, as current empirical results often rely on comparisons to weak classical baselines or very small-scale experiments

In summary, this insightful interview with Jessica Pointing highlights the current challenges and open questions in quantum machine learning, providing a framework for critically evaluating progress in the field. While the path to quantum advantage in machine learning remains uncertain, ongoing research continues to expand our understanding of the possibilities and limitations of QNNs.

Paper cited in the episode:
Do Quantum Neural Networks have Simplicity Bias?