5 research outputs found
Automated Gadget Discovery in Science
In recent years, reinforcement learning (RL) has become increasingly
successful in its application to science and the process of scientific
discovery in general. However, while RL algorithms learn to solve increasingly
complex problems, interpreting the solutions they provide becomes ever more
challenging. In this work, we gain insights into an RL agent's learned behavior
through a post-hoc analysis based on sequence mining and clustering.
Specifically, frequent and compact subroutines, used by the agent to solve a
given task, are distilled as gadgets and then grouped by various metrics. This
process of gadget discovery develops in three stages: First, we use an RL agent
to generate data, then, we employ a mining algorithm to extract gadgets and
finally, the obtained gadgets are grouped by a density-based clustering
algorithm. We demonstrate our method by applying it to two quantum-inspired RL
environments. First, we consider simulated quantum optics experiments for the
design of high-dimensional multipartite entangled states where the algorithm
finds gadgets that correspond to modern interferometer setups. Second, we
consider a circuit-based quantum computing environment where the algorithm
discovers various gadgets for quantum information processing, such as quantum
teleportation. This approach for analyzing the policy of a learned agent is
agent and environment agnostic and can yield interesting insights into any
agent's policy
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
Automated gadget discovery in the quantum domain
In recent years, reinforcement learning (RL) has become increasingly successful in its application to the quantum domain and the process of scientific discovery in general. However, while RL algorithms learn to solve increasingly complex problems, interpreting the solutions they provide becomes ever more challenging. In this work, we gain insights into an RL agent鈥檚 learned behavior through a post-hoc analysis based on sequence mining and clustering. Specifically, frequent and compact subroutines, used by the agent to solve a given task, are distilled as gadgets and then grouped by various metrics. This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm. We demonstrate our method by applying it to two quantum-inspired RL environments. First, we consider simulated quantum optics experiments for the design of high-dimensional multipartite entangled states where the algorithm finds gadgets that correspond to modern interferometer setups. Second, we consider a circuit-based quantum computing environment where the algorithm discovers various gadgets for quantum information processing, such as quantum teleportation. This approach for analyzing the policy of a learned agent is agent and environment agnostic and can yield interesting insights into any agent鈥檚 policy
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