L0-regularized compressed sensing with Mean-field Coherent Ising Machines
arxiv(2024)
摘要
Coherent Ising Machine (CIM) is a network of optical parametric oscillators
that solves combinatorial optimization problems by finding the ground state of
an Ising Hamiltonian. As a practical application of CIM, Aonishi et al.
proposed a quantum-classical hybrid system to solve optimization problems of
L0-regularization-based compressed sensing (L0RBCS). Gunathilaka et al. has
further enhanced the accuracy of the system. However, the computationally
expensive CIM's stochastic differential equations (SDEs) limit the use of
digital hardware implementations. As an alternative to Gunathilaka et al.'s CIM
SDEs used previously, we propose using the mean-field CIM (MF-CIM) model, which
is a physics-inspired heuristic solver without quantum noise. MF-CIM surmounts
the high computational cost due to the simple nature of the differential
equations (DEs). Furthermore, our results indicate that the proposed model has
similar performance to physically accurate SDEs in both artificial and magnetic
resonance imaging data, paving the way for implementing CIM-based L0RBCS on
digital hardware such as Field Programmable Gate Arrays (FPGAs).
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