13,817 research outputs found
Deep Variational Reinforcement Learning for POMDPs
Many real-world sequential decision making problems are partially observable
by nature, and the environment model is typically unknown. Consequently, there
is great need for reinforcement learning methods that can tackle such problems
given only a stream of incomplete and noisy observations. In this paper, we
propose deep variational reinforcement learning (DVRL), which introduces an
inductive bias that allows an agent to learn a generative model of the
environment and perform inference in that model to effectively aggregate the
available information. We develop an n-step approximation to the evidence lower
bound (ELBO), allowing the model to be trained jointly with the policy. This
ensures that the latent state representation is suitable for the control task.
In experiments on Mountain Hike and flickering Atari we show that our method
outperforms previous approaches relying on recurrent neural networks to encode
the past
Enhancing variational quantum state diagonalization using reinforcement learning techniques
The development of variational quantum algorithms is crucial for the
application of NISQ computers. Such algorithms require short quantum circuits,
which are more amenable to implementation on near-term hardware, and many such
methods have been developed. One of particular interest is the so-called the
variational diagonalization method, which constitutes an important algorithmic
subroutine, and it can be used directly for working with data encoded in
quantum states. In particular, it can be applied to discern the features of
quantum states, such as entanglement properties of a system, or in quantum
machine learning algorithms. In this work, we tackle the problem of designing a
very shallow quantum circuit, required in the quantum state diagonalization
task, by utilizing reinforcement learning. To achieve this, we utilize a novel
encoding method that can be used to tackle the problem of circuit depth
optimization using a reinforcement learning approach. We demonstrate that our
approach provides a solid approximation to the diagonalization task while using
a small number of gates. The circuits proposed by the reinforcement learning
methods are shallower than the standard variational quantum state
diagonalization algorithm, and thus can be used in situations where the depth
of quantum circuits is limited by the hardware capabilities.Comment: 17 pages with 13 figures, some minor, important improvements, code
available at https://github.com/iitis/RL_for_VQSD_ansatz_optimizatio
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