We present QAMOO, a novel quantum computing framework for multi-objective portfolio optimization that simultaneously optimizes four distinct financial objectives—expected return, portfolio volatility, transaction costs, and entropy-based diversification—within a unified quantum Hamiltonian formulation. Unlike prior quantum finance approaches that treat diversification as a post-hoc penalty or encode transaction fees as scalarized weights, QAMOO embeds a Shannon entropy term directly into the quantum cost function through amplitude encoding of the portfolio weight vector. This formulation enables joint optimization over a non-convex objective landscape that cannot be faithfully reduced to a classical convex program without exponential complexity growth. The framework employs the Quantum Approximate Optimization Algorithm (QAOA) with pre-trained parameter transfer, executed on IBM Quantum hardware featuring heavy-hex topology processors with up to 156 qubits. A custom Pareto engine discovers the complete frontier of non-dominated portfolio solutions, enabling investors to select allocations matching their individual risk preferences. Empirical evaluation on a multi-asset portfolio demonstrates a 0.21% hypervolume improvement over the state-of-the-art classical multi-objective evolutionary algorithm (NSGA-III), while requiring approximately 30% fewer optimization cycles. The quantum-derived portfolios exhibit 23% lower turnover-adjusted volatility after accounting for realistic transaction fees. QAMOO establishes a new research direction at the intersection of quantum computing and quantitative finance, providing a hardware-aware optimization paradigm that integrates entropy-driven diversification directly into quantum annealing and gate-model formulations.
Jahangir Khan (Fri,) studied this question.