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Found 10 papers, 2 papers with code
Quantum walk processes in quantum devices
Firstly, we discuss ways to obtain graphs provided quantum circuit.
Quantum Physics
Setting up experimental Bell test with reinforcement learning
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.
Machine learning transfer efficiencies for noisy quantum walks
Quantum effects are known to provide an advantage in particle transfer across networks.
Machine learning for long-distance quantum communication
But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication?
Predicting quantum advantage by quantum walk with convolutional neural networks
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.
Benchmarking projective simulation in navigation problems
Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory.
Active learning machine learns to create new quantum experiments
We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.
Meta-learning within Projective Simulation
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.
Projective simulation with generalization
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.
Projective simulation applied to the grid-world and the mountain-car problem
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.
