Block Coordinate Descent for Dynamic Portfolio Optimization on Finite-Precision Coherent Ising Machines

TL;DR

This paper introduces a block coordinate descent approach for dynamic portfolio optimization on finite-precision coherent Ising machines, enabling large-scale problems to be solved efficiently despite hardware limitations.

Contribution

It proposes a novel decomposition method that allows solving large DPO problems on finite-precision CIM hardware, improving scalability and solution quality.

Findings

Enables solving large DPO instances on finite-precision CIMs.

Achieves portfolios comparable to classical solvers.

Reduces runtime by solving smaller subproblems quickly.

Abstract

Coherent Ising machines (CIMs) have emerged as specialized quantum hardware for large-scale combinatorial optimization. However, for large instances that remain challenging for classical methods, some platforms support only finite-precision inputs, and the required scaling and quantization can degrade solution quality. Dynamic portfolio optimization (DPO) can be formulated as a quadratic unconstrained binary optimization (QUBO) problem, but large instances are especially vulnerable to precision loss under global scaling. We propose a block coordinate descent method that decomposes the DPO model along the time dimension and iteratively solves compact time-block subproblems on the device. Experiments on finite-precision CIM hardware show that the method enables these instances to be solved under hardware precision limits, yields portfolios competitive with classical benchmark solvers, and reduces runtime through fast CIM solution of the resulting subproblems. These results demonstrate the promise of finite-precision CIMs as a practical and scalable approach to structured large-scale combinatorial optimization.