4,856 research outputs found
Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
The exploration of quantum algorithms that possess quantum advantages is a
central topic in quantum computation and quantum information processing. One
potential candidate in this area is quantum generative adversarial learning
(QuGAL), which conceptually has exponential advantages over classical
adversarial networks. However, the corresponding learning algorithm remains
obscured. In this paper, we propose the first quantum generative adversarial
learning algorithm-- the quantum multiplicative matrix weight algorithm
(QMMW)-- which enables the efficient processing of fundamental tasks. The
computational complexity of QMMW is polynomially proportional to the number of
training rounds and logarithmically proportional to the input size. The core
concept of the proposed algorithm combines QuGAL with online learning. We
exploit the implementation of QuGAL with parameterized quantum circuits, and
numerical experiments for the task of entanglement test for pure state are
provided to support our claims
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
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