论文标题
稀疏的信号重建具有L0登记线性回归中的Qubo公式
Sparse Signal Reconstruction with QUBO Formulation in l0-regularized Linear Regression
论文作者
论文摘要
稀疏信号重建的L0调查线性回归是基于二次无约束的二进制优化（QUBO）公式实现的。在此方法中，信号值被量化并表示为位序列。通过将L0-Norm转换为这些位的二次形式，由专门用于Qubo的求解器（例如量子退火器）提供并优化了完全二次的目标函数。使用商业量子退火器进行数值实验表明，在几个有限条件下，基于正交匹配追踪（OMP）和最不绝对的收缩和选择算子（Lasso）的基于正交匹配的追踪（OMP）的传统方法的性能稍好。
An l0-regularized linear regression for a sparse signal reconstruction is implemented based on the quadratic unconstrained binary optimization (QUBO) formulation. In this method, the signal values are quantized and expressed as bit sequences. By transforming l0-norm to a quadratic form of these bits, the fully quadratic objective function is provided and optimized by the solver specialized for QUBO, such as the quantum annealer. Numerical experiments with a commercial quantum annealer show that the proposed method performs slightly better than conventional methods based on orthogonal matching pursuit (OMP) and the least absolute shrinkage and selection operator (LASSO) under several limited conditions.
