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

Battle Count: 50

The paper is primarily about collateral management under ISDA CSAs rather than direct trading strategy development. However, it is highly relevant to quantitative finance operations: funding cost optimization (LVA/FVA), tail risk management (CVaR), liquidity efficiency, and the quantum-inspired optimization techniques (HO-QAOA, QUBO formulations) are transferable to portfolio construction and execution optimization. The integer lot and cap-constrained optimization structure parallels constrained portfolio allocation problems. The LLM extraction pipeline could inform automated contract analysis for trading desks.

The pipeline integrates five major components: (1) a fine-tuned CSA-domain LLM with evidence-gating and span citation; (2) simulated annealing with feasibility repair; (3) spectral subset selection and micro-HO-QAOA with ancilla compilation for higher-order terms; (4) CP-SAT certification with linearized CVaR and overshoot; (5) governance artifact generation. Each component requires domain-specific expertise (ISDA legal terms, quantum circuit compilation, constraint programming, risk modeling). The interaction graph construction, ancilla management, and fallback logic add significant engineering complexity. The weighted objective calibration requires operational data inputs.

The paper emphasizes governance-grade artifacts: span citations, valuation matrix audit, weight provenance JSON, QUBO manifests (subset n, order k, depth p), CP-SAT traces (status, bounds, slacks), reproducibility hashes and seeds. However, the CSA-domain LLM training data, model architecture, and benchmarks are deferred to a separate paper. No explicit GitHub repository is provided. The evaluation uses synthetic/proxy government bond datasets rather than fully disclosed real production data.