Family office portfolio optimization

Quarterly multi-asset rebalancing where the family's position sheet never leaves attested compute in cleartext.

Who

A multi-generational family office managing capital across public equities, private equity, real estate, fixed income, and direct investments on behalf of beneficiaries with differing time horizons, tax positions, and liquidity needs.

The problem

The CIO rebalances quarterly against dense inputs — capital-market expectations, per-branch tax positions, liquidity needs, manager reviews, ESG constraints, and the Investment Policy Statement. Traditional mean-variance optimizers stall on the discrete decisions (entry/exit thresholds, lot sizing, illiquidity premia). Worse, every external consultant, vendor, or model provider that touches the data sees the family's complete position sheet.

What ArcaQ does

The CIO selects the Portfolio Rebalance template and describes constraints in a structured form or natural language. The description is sealed into the enclave; an attested model translates it into a QUBO; the variational loop runs on the in-enclave simulator before the final circuit is dispatched to quantum hardware. The dominant results are interpreted back into concrete rebalance recommendations with explanations.

Expected result (published benchmarks)

Published QAOA portfolio-optimization benchmarks at the 50–80-asset scale show 2–8% improvement in risk-adjusted return over classical solvers, with the largest gains on problems carrying complex discrete constraints. Tax-loss-harvesting optimization across 100+ lots typically shows a further 0.5–2% after-tax improvement on the harvested portion.

Why confidentiality matters

Family wealth allocation is among the most private financial data that exists. ArcaQ ensures portfolio composition, allocations, and rebalance decisions never leave attested compute in cleartext. The quantum vendor sees a QUBO matrix; it cannot reverse-engineer the holdings the coefficients represent.

Tier fit

Atelier or Reserve; Grand Reserve for large multi-billion offices.

The performance ranges below are drawn from published academic and industry benchmarks for the relevant problem class — QAOA portfolio-optimization studies, VQE chemistry benchmarks, and quantum-annealing logistics case studies. They are not ArcaQ measurements. Results vary substantially with problem size, constraint density, and the specific algorithm and hardware used. ArcaQ-specific results will be published after hardware validation.