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A technique for speeding up reinforcement learning algorithms by using time manipulation is proposed. It is applicable to failure-avoidance control problems running in a computer simulation. Turning the time of the simulation backwards on failure events is shown to speed up the learning by 260% and improve the state space exploration by 12% on the cart-pole(More)
A Multi-Channel Representation for Quantum Image (MCRQI) is proposed to facilitate the further image processing tasks based on the Flexible Representation for Quantum Image (FRQI). Channel Swapping Operation, One Channel Operation, are proposed as basic image processing operations on MCRQI representation. The simulation experiment results on classical(More)
A technique called Time Hopping is proposed for speeding up reinforcement learning algorithms. It is applicable to continuous optimization problems running in computer simulations. Making shortcuts in time by hopping between distant states combined with off-policy reinforcement learning allows the technique to maintain higher learning rate. Experiments on a(More)
—Circuits to achieve geometric transformations including two-point swapping, flip, coordinate swapping, orthogonal rotations and their variants on N-sized quantum images are proposed based on the basic quantum gates; NOT, CNOT and Toffoli gates. The complexity of the circuits is O(log 2 N) for two-point swapping and O(log N) for flip, coordinate swapping(More)