Spooky Action Could Help Boost Quantum Machine Learning

In my earliest days of  physics was mystified with the spooky part, now it turns out to the key to the value?

Spooky Action Could Help Boost Quantum Machine Learning

IEEE Spectrum, Charles Q. Choi, March 7, 2022

Researchers at Los Alamos National Laboratory and Louisiana State University suggest quantum entanglement, or "spooky action at a distance," might help clear a path toward quantum machine learning (ML) by overcoming the no-free-lunch theorem. The theorem posits that any ML algorithm is only as good as any other when their performance is averaged over many problems and training datasets. This implies that modeling a quantum system could require a volume of training data that must grow exponentially as the modeled system expands. Researchers suggested entangling extra or "ancilla" quantum bits with the quantum system that a quantum computer intends to model, enabling the quantum ML circuit to interact with many quantum states in the training data concurrently. They used quantum-hardware startup Rigetti's Aspen-4 quantum computer to confirm these findings ... '