Where confidential quantum optimization earns its place.
ArcaQ is built for combinatorial optimization where the input data is itself the asset. Each use case below keeps that data inside attested compute end to end — the quantum vendor sees mathematics, never what it means.
← ArcaQ overviewFinance
Family office portfolio optimization
Quarterly multi-asset rebalancing where the family's position sheet never leaves attested compute in cleartext.
Quant fund strategy optimization
Rebalancing across hundreds of positions where the firm's strategy parameters — its alpha — never leave the enclave.
High-net-worth tax-aware strategy
Asset-location and harvesting optimization across account types, with tax positions never shared in full.
Private equity portfolio operating decisions
Allocating partner attention, follow-on capital, and exit timing across portfolio companies — kept confidential.
M&A target screening
Multi-criteria selection of a pursuit portfolio while target lists and valuation models stay sealed.
Operations
Energy & R&D
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.