A mapping from a graph space to quantum register space is provided, and simulations on IBM Q quantum computer are performed.
Finding optical setups producing measurement results with a targeted probability distribution is hard as a priori the number of possible experimental implementations grows exponentially with the number of modes and the number of devices.
But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication?
Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enabling in these cases quantum algorithmic applications and quantum-enhanced energy transfer.
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.
The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.
Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.
We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.