Metropolis-Hastings Random Walk along the Gradient Descent Direction for MIMO Detection

Author(s):  
Niranjan M Gowda ◽  
Sundar Krishnamurthy ◽  
Andrey Belogolovy
Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 269 ◽  
Author(s):  
James Stokes ◽  
Josh Izaac ◽  
Nathan Killoran ◽  
Giuseppe Carleo

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is interpreted as moving in the steepest descent direction with respect to the Quantum Information Geometry, corresponding to the real part of the Quantum Geometric Tensor (QGT), also known as the Fubini-Study metric tensor. An efficient algorithm is presented for computing a block-diagonal approximation to the Fubini-Study metric tensor for parametrized quantum circuits, which may be of independent interest.


2014 ◽  
Vol 530-531 ◽  
pp. 443-446
Author(s):  
Hai Xia Yan ◽  
Yan Jun Liu

In order to improve the speed of compressed sensing image reconstruction algorithm, a rapid gradient projection algorithm for image reconstruction is proposed. In traditional Gradient Projection algorithm, the pursuit direction is alternating, in rapid gradient projection algorithm, we use the Newton's method to calculate the gradient descent direction, thus the constraints conditions of gradient projection is satisfied. And the target function is updated in each iteration computing. The effect of approximation matrix to target function is reduced. The iteration computing times is reduced, because the algorithm works in accurate search direction. Experiment results show that, compared with the GPSR algorithm, the RGPSR algorithm improves the signals reconstruction accuracy, improves PSNR of reconstruction signals, and exhibits higher robustness under different noise intensities.


2020 ◽  
Vol 68 ◽  
pp. 6336-6349
Author(s):  
Yi Wei ◽  
Ming-Min Zhao ◽  
Mingyi Hong ◽  
Min-Jian Zhao ◽  
Ming Lei

Author(s):  
Joseph Rudnick ◽  
George Gaspari
Keyword(s):  

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