Global Optimum Search in Quantum Deep Learning
This addresses optimization challenges in quantum deep learning, but it appears incremental as it builds on existing quantum methods with specific enhancements.
The paper tackles the problem of finding global optima in machine learning by proposing two quantum circuit approaches, the average approach and the Partial Swap Test Cut-off method (PSTC), achieving a current cost of O(√|Θ| N) with potential for further improvement to O(√|Θ| · sublinear N).
This paper aims to solve machine learning optimization problem by using quantum circuit. Two approaches, namely the average approach and the Partial Swap Test Cut-off method (PSTC) was proposed to search for the global minimum/maximum of two different objective functions. The current cost is $O(\sqrt{|Θ|} N)$, but there is potential to improve PSTC further to $O(\sqrt{|Θ|} \cdot sublinear \ N)$ by enhancing the checking process.