A Weightless Neural Node Based on a Probabilistic Quantum Memory

The success of quantum computation is most commonly associated with speed up of classical algorithms, as the Shor's factoring algorithm and the Grover's search algorithm. But it should also be related with exponential storage capacity such as the super dense coding. In this work we use a probabilistic quantum memory proposed by Trugen berger, where one can store $\mathbf 2^n$ patterns with only $\mathbf n$ quantum bits (qbits). We define a new model of a quantum weightless neural node with this memory in a similar fashion as to the classical Random Access Memory (RAM) node is used in classical weightless neural networks. Some advantages of the proposed model are that the memory of the node does not grow exponentially with the number of inputs and that the node can generalise.