We exploit recent results from random matrix theory to develop an algorithm for improving the quality of the estimated low-rank signal matrix that explicitly ...

We exploit recent results from random matrix theory to develop an algo- rithm for improving the quality of the estimated low-rank signal matrix that explicitly ...

We exploit recent results from random matrix theory to develop an algorithm for improving the quality of the estimated low-rank signal matrix that explicitly ...

This survey explores modern approaches for computing low-rank approximations of high-dimensional matrices by means of the randomized SVD, randomized subspace ...

The authors investigate combining low-rank matrix factorization and (uniform scalar) quantization. Through theoretical analysis and experiments they demonstrate ...

Apr 20, 2021 · In this work, we present an efficient rank-compression approach for the classical simulation of Kraus decoherence channels in noisy quantum circuits.

Missing: Exploiting improve noisy

Sep 4, 2024 · This survey explores modern approaches for computing low-rank approximations of high-dimensional matrices by means of the randomized SVD, ...

Apr 18, 2014 · We exploit recent results from random matrix theory to exactly characterize the large matrix limit of the optimal weighting coefficients and ...

Design a random matrix N—that depends on the input matrix A—but that still satisfies these three conditions. • Choose N such that A + N has better sparsity, etc ...