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Collateral optimization refers to the systematic allocation of financialassets to satisfy obligations or secure transactions, while simultaneouslyminimizing costs and optimizing the usage of available resources. {Thisinvolves assessing number of characteristics, such as cost of funding andquality of the underlying assets to ascertain the optimal collateral quantityto be posted to cover exposure arising from a given transaction or a set oftransactions. One of the common objectives is to minimise the cost ofcollateral required to mitigate the risk associated with a particulartransaction or a portfolio of transactions while ensuring sufficient protectionfor the involved parties}. Often, this results in a large-scale combinatorialoptimization problem. In this study, we initially present a Mixed IntegerLinear Programming (MILP) formulation for the collateral optimization problem,followed by a Quadratic Unconstrained Binary optimization (QUBO) formulation inorder to pave the way towards approaching the problem in a hybrid-quantum andNISQ-ready way. We conduct local computational small-scale tests using variousSoftware Development Kits (SDKs) and discuss the behavior of our formulationsas well as the potential for performance enhancements. We further survey therecent literature that proposes alternative ways to attack combinatorialoptimization problems suitable for collateral optimization.

Approaching Collateral Optimization for NISQ and Quantum-Inspired Computing