Quantum-Classical Multiple Kernel Learning

Understand

As quantum computers become increasingly practical, so does the prospect of using quantum computation to improve upon traditional algorithms.

• Kernel methods in machine learning is one area where such improvements could be realized in the near future.
• Paired with kernel methods like support-vector machines, small and noisy quantum computers can evaluate classically-hard quantum kernels that capture unique notions of similarity in data.
• Taking inspiration from techniques in classical machine learning, this work investigates simulated quantum kernels in the context of multiple kernel learning (MKL).