247 research outputs found
A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
We introduce a kind of partial observability to the projective simulation
(PS) learning method. It is done by adding a belief projection operator and an
observability parameter to the original framework of the efficiency of the PS
model. I provide theoretical formulations, network representations, and
situated scenarios derived from the invasion toy problem as a starting point
for some multi-agent PS models.Comment: 28 pages, 21 figure
Optimizing Quantum Error Correction Codes with Reinforcement Learning
Quantum error correction is widely thought to be the key to fault-tolerant
quantum computation. However, determining the most suited encoding for unknown
error channels or specific laboratory setups is highly challenging. Here, we
present a reinforcement learning framework for optimizing and fault-tolerantly
adapting quantum error correction codes. We consider a reinforcement learning
agent tasked with modifying a family of surface code quantum memories until a
desired logical error rate is reached. Using efficient simulations with about
70 data qubits with arbitrary connectivity, we demonstrate that such a
reinforcement learning agent can determine near-optimal solutions, in terms of
the number of data qubits, for various error models of interest. Moreover, we
show that agents trained on one setting are able to successfully transfer their
experience to different settings. This ability for transfer learning showcases
the inherent strengths of reinforcement learning and the applicability of our
approach for optimization from off-line simulations to on-line laboratory
settings.Comment: 21 pages, 13 figures, 1 table, updated reference list, accepted for
publication in Quantu
Noisy three-player dilemma game: Robustness of the quantum advantage
Games involving quantum strategies often yield higher payoff. Here, we study
a practical realization of the three-player dilemma game using the
superconductivity-based quantum processors provided by IBM Q Experience. We
analyze the persistence of the quantum advantage under corruption of the input
states and how this depends on parameters of the payoff table. Specifically,
experimental fidelity and error are observed not to be properly anti
correlated, i.e., there are instances where a class of experiments with higher
fidelity yields a greater error in the payoff. Further, we find that the
classical strategy will always outperform the quantum strategy if corruption is
higher than half.Comment: Persistence of the quantum advantage under corruption of the input
states is analyzed for a 3-player dilemma game implemented using
superconductivity-based quantum processor
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