13 citations · 16 across the 6 of their papers we have counts for
6 papers
A Fully Analog Pipeline for Portfolio Optimization
James S. Cummins, Natalia G. Berloff
Portfolio optimization is a ubiquitous problem in financial mathematics that relies on accurate estimates of covariance matrices for asset returns. However, estimates of pairwise c…
Complex Vector Gain-Based Annealer for Minimizing XY Hamiltonians
James S. Cummins, Natalia G. Berloff
This paper presents the Complex Vector Gain-Based Annealer (CoVeGA), an analog computing platform designed to overcome energy barriers in XY Hamiltonians through a higher-dimension…
Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division
Daniele Veraldi, Davide Pierangeli, Silvia Gentilini +10
Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machi…
Efficient Computation Using Spatial-Photonic Ising Machines: Utilizing Low-Rank and Circulant Matrix Constraints
Richard Zhipeng Wang, James S. Cummins, Marvin Syed +11
We explore the potential of spatial-photonic Ising machines (SPIMs) to address computationally intensive Ising problems that employ low-rank and circulant coupling matrices. Our re…
Vector Ising Spin Annealer for Minimizing Ising Hamiltonians
James S. Cummins, Natalia G. Berloff
We introduce the Vector Ising Spin Annealer (VISA), a framework in gain-based computing that harnesses light-matter interactions to solve complex optimization problems encoded in s…
Ising Hamiltonian Minimization: Gain-Based Computing with Manifold Reduction of Soft-Spins vs Quantum Annealing
James S. Cummins, Hayder Salman, Natalia G. Berloff
We investigate the minimization of Ising Hamiltonians, comparing the performance of gain-based computing paradigms based on the dynamics of semi-classical soft-spin models with qua…