In recent years investment in quantum computing has increased significantly, with quantum approaches to areas such as security and network communication expected to upend existing classical computing techniques. Researchers such as Garg and Ramakrishnan identify that at its core, quantum computing aims to "solve classically intractable problems through computationally cheaper techniques". It is perhaps unsurprising that just as research in deep learning and quantum computing have grown in parallel in recent years, many are now examining the possibilities at the intersection of these two fields: Quantum deep learning. In this article, we'll discuss at high-level existing research and applications of quantum deep learning, focusing on hybrid quantum convolutional neural networks (QCNNs). To begin, a brief definition of quantum computing compared to classical computing is provided.

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( ( Θ) N), but there is potential to improve PSTC further to O( ( Θ)· sublinear N) by enhancing the checking process.