4 research outputs found
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of
physical systems that meaningfully encode information about the systems. The
representations learnt by most current machine learning techniques reflect
statistical structure present in the training data; however, these methods do
not allow us to specify explicit and operationally meaningful requirements on
the representation. Here, we present a neural network architecture based on the
notion that agents dealing with different aspects of a physical system should
be able to communicate relevant information as efficiently as possible to one
another. This produces representations that separate different parameters which
are useful for making statements about the physical system in different
experimental settings. We present examples involving both classical and quantum
physics. For instance, our architecture finds a compact representation of an
arbitrary two-qubit system that separates local parameters from parameters
describing quantum correlations. We further show that this method can be
combined with reinforcement learning to enable representation learning within
interactive scenarios where agents need to explore experimental settings to
identify relevant variables.Comment: 24 pages, 13 figure
Experimental quantum speed-up in reinforcement learning agents
Increasing demand for algorithms that can learn quickly and efficiently has
led to a surge of development within the field of artificial intelligence (AI).
An important paradigm within AI is reinforcement learning (RL), where agents
interact with environments by exchanging signals via a communication channel.
Agents can learn by updating their behaviour based on obtained feedback. The
crucial question for practical applications is how fast agents can learn to
respond correctly. An essential figure of merit is therefore the learning time.
While various works have made use of quantum mechanics to speed up the agent's
decision-making process, a reduction in learning time has not been demonstrated
yet. Here we present a RL experiment where the learning of an agent is boosted
by utilizing a quantum communication channel with the environment. We further
show that the combination with classical communication enables the evaluation
of such an improvement, and additionally allows for optimal control of the
learning progress. This novel scenario is therefore demonstrated by considering
hybrid agents, that alternate between rounds of quantum and classical
communication. We implement this learning protocol on a compact and fully
tunable integrated nanophotonic processor. The device interfaces with
telecom-wavelength photons and features a fast active feedback mechanism,
allowing us to demonstrate the agent's systematic quantum advantage in a setup
that could be readily integrated within future large-scale quantum
communication networks.Comment: 10 pages, 4 figure
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. Such representations ignore redundant features and treat parameters such as velocity and position separately because they can be useful for making statements about different experimental settings. Here, we capture this notion by formally defining the concept of operationally meaningful representations. We present an autoencoder architecture with attention mechanism that can generate such representations and demonstrate it on examples involving both classical and quantum physics. For instance, our architecture finds a compact representation of an arbitrary two-qubit system that separates local parameters from parameters describing quantum correlations.ISSN:2632-215