Hybrid LLM and Higher-Order Quantum Approximate Optimization for CSA Collateral Management

TL;DR

This work tackles CSA-driven collateral optimization under ISDA terms, where discrete lots, haircuts, rounding, and caps create rugged search landscapes. It proposes a certifiable hybrid pipeline that combines an evidence-gated CSA-extracting LLM, a quantum-inspired explorer using micro-HO-QAOA on higher-order QUBOs (with $n\le 16$, $k\le 4$), and CP-SAT as a single arbiter for feasibility certification, all under a weighted, risk-aware objective $J$. The approach yields substantial gains over strong classical baselines (approximately 9–11% improvements across harnesses) while producing governance-grade artifacts for auditability, including QUBO manifests, weight provenance, and CP-SAT traces. By encoding caps and RA/MTA interactions as higher-order couplings and leveraging a spectral subset strategy, the method effectively navigates multi-asset coordination challenges in government-bond datasets and multi-CSA scenarios, enabling scalable, auditable enterprise deployment.

Abstract

We address finance-native collateral optimization under ISDA Credit Support Annexes (CSAs), where integer lots, Schedule A haircuts, RA/MTA gating, and issuer/currency/class caps create rugged, legally bounded search spaces. We introduce a certifiable hybrid pipeline purpose-built for this domain: (i) an evidence-gated LLM that extracts CSA terms to a normalized JSON (abstain-by-default, span-cited); (ii) a quantum-inspired explorer that interleaves simulated annealing with micro higher order QAOA (HO-QAOA) on binding sub-QUBOs (subset size n <= 16, order k <= 4) to coordinate multi-asset moves across caps and RA-induced discreteness; (iii) a weighted risk-aware objective (Movement, CVaR, funding-priced overshoot) with an explicit coverage window U <= Reff+B; and (iv) CP-SAT as single arbiter to certify feasibility and gaps, including a U-cap pre-check that reports the minimal feasible buffer B*. Encoding caps/rounding as higher-order terms lets HO-QAOA target the domain couplings that defeat local swaps. On government bond datasets and multi-CSA inputs, the hybrid improves a strong classical baseline (BL-3) by 9.1%, 9.6%, and 10.7% across representative harnesses, delivering better cost-movement-tail frontiers under governance settings. We release governance grade artifacts-span citations, valuation matrix audit, weight provenance, QUBO manifests, and CP-SAT traces-to make results auditable and reproducible.