add new QMOO tutorial

[kcmccormibm]

New portfolio optimization tutorial using the QMOO method introduced in Kotil, et al.

Summary

Adds a new tutorial, Quantum approximate multi-objective optimization applied to a portfolio optimization problem. The tutorial shows how to trace the risk/return/diversification Pareto front of a cardinality-constrained portfolio with QAOA. The cardinality constraint is enforced with an XY mixer, the three objectives are scalarized with the weighted-sum method, and the weights are swept to map out the Pareto front.

What's included

• Small-scale walkthrough (8-asset, exact simulation) that can be verified by brute force.
• Large-scale example (40 assets, 8 GICS sectors, K=6) sized for IBM Heron hardware, using QAP-truncation to sparsify the covariance and moocore for hypervolume and dominated-solution filtering.
• QAOA angles trained classically (exact statevector at small scale, MPS at 40 qubits); pre-trained angles ship in instances/ so the slow training step is skipped by default.
• Real market data and parameters file in datasets/tutorials and dowloaded at runtime

Testing / notes

Hardware section selects the least-busy Heron device via least_busy(min_num_qubits=156) (the 156-qubit threshold restricts selection to Heron, excluding Eagle and Nighthawk).
Adapted from Kotil et al. (2025), Nature Computational Science (arXiv:2503.22797), which develops the method for max-cut.

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• @nathanearnestnoble

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