Finance
Portfolio optimization is an NP-hard problem. Quantum makes it tractable.
Allocating capital across assets to maximize risk-adjusted return is one of the oldest problems in finance — and one of the hardest. TARX Quantum Engine finds allocations that classical optimizers miss.
The Cost of Suboptimal Allocation
2-5%
annual alpha lost to poor allocation
1M+
asset combos for 20-asset portfolio
15%
of hedge funds use quantum research
Mean-variance optimization breaks down with more than ~15 assets. Classical solvers use approximations that leave returns on the table. The firms already exploring quantum optimization — JPMorgan, Goldman, Citadel — are building edge before the hardware catches up.
TARX Optimizer
QAOA encodes the Sharpe ratio objective as a Hamiltonian and finds the allocation state that maximizes risk-adjusted return. Quantum interference naturally suppresses high-risk, low-return allocations while amplifying efficient frontier solutions.
Live Demo
Allocate $100K across 5 assets for maximum Sharpe ratio.
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