Quantum-Empowered
Decision Layer
QUSMOS turns complex decisions into structured optimization problems and returns explainable, cost-aware recommendations under real-world constraints.
The Decision Gap Becomes Expensive
Critical decisions live in spreadsheets and gut feel
Portfolios, plans, schedules, and operating choices are still often stitched together manually, making them slow to update, hard to audit, and easy to misalign.
Complexity grows faster than planning tools
Budgets, constraints, risks, dependencies, forecasts, and priorities interact at the same time, making it difficult to compare all feasible options.
Every unoptimized decision compounds
Small losses in cost, energy, throughput, timing, or risk can accumulate across repeated decisions and stay invisible without structured models.
Quantum-readiness creates long-term leverage
Teams that structure decision workflows today are better prepared to use future quantum acceleration without rebuilding their decision logic.
Where QUSMOS applies
QUSMOS brings a quantum-empowered decision engine to the hardest planning problems in business and industry, where complexity, constraints, and timing decide the outcome.
Portfolio & Resource Allocation
High-stakes strategic planning — R&D project selection, grant evaluations, and complex vendor prioritization.
Decision dynamics
Selecting the optimal subset of initiatives to maximize ROI and align with long-term strategic goals under hard budget and capacity limits.
Portfolio · Weighted selection
Key constraints
- • Rigid budget ceilings
- • Multi-dimensional risk profiles
- • Resource capacity limits
- • Intricate cross-project dependencies
Quantum value
Navigates exponential search spaces to identify high-value portfolios that classical heuristics consistently miss.
Controlled Environments
Commercial greenhouses, industrial HVAC, cold-chain logistics, indoor farming, and other climate-sensitive industrial systems.
Decision dynamics
Generating optimal 24–72h operating plans (temperature, humidity, CO2) that balance biological health against energy price volatility.
Greenhouse · 24–72h plan
Key constraints
- • Non-linear biological variables
- • Mechanical switching costs
- • Energy inertia
- • Strict plant / system safety limits
Quantum value
Plant-first, energy-aware strategies mastering multi-objective optimization with physical limits in time-coupled systems.
Energy & Grid Systems
Smart grids, Virtual Power Plants (VPPs), large-scale demand response, and microgrid management across generation, storage, and load assets.
Decision dynamics
Day-ahead, intraday, and rolling-horizon planning focused on minimizing system costs and carbon emissions while respecting physical grid constraints.
Grid · Rolling horizon
Key constraints
- • Instantaneous supply-demand balance
- • Variable tariffs
- • Battery health (SOC)
- • Strict CO2 targets
Quantum value
Uniquely suited for complex, time-coupled, multi-asset optimization where intervals are heavily interdependent.
Industrial Scheduling
Factory floor production scheduling, machine allocation, and synchronization of preventive maintenance cycles across shifts and lines.
Decision dynamics
Determining the most efficient sequence of tasks across limited machines and personnel to eliminate bottlenecks and maximize OEE.
Factory · Sequence & OEE
Key constraints
- • Machine capacities
- • Job precedence
- • Labor shifts
- • Setup / tooling changeover downtime
Quantum value
Resolves intricate interdependencies in NP-hard domains to find global optima classical solvers cannot reach.
From messy workflows to optimized decisions
Decision context
Messy workflows, goals, rules, risks, dependencies, forecasts, and preferences are mapped into a structured decision model.
Optimization-ready logic
Objectives, constraints, penalties, trade-offs, and feasible choices are translated into a solver-ready formulation.
Explainable recommendation
Quantum-inspired, hybrid, and future quantum methods search the constrained decision space and return recommendations teams can review, compare, and integrate.
Model the workflow → formulate the decision → optimize the recommendation
Why QUSMOS exists
QUSMOS is building the decision layer for the quantum era.
A world where every high-stakes decision is modeled, optimized, and explainable — quantum-ready by design.
Turn messy workflows into structured models, optimize them with quantum-ready methods, and deliver recommendations teams can trust.
The team building QUSMOS
Deep-tech team. Practical execution. Physics · AI · Optimization · Product · Go-to-Market.
Hamzeh Alavirad, Ph.D.
Founder · Scientific & Optimization Lead
PhD in theoretical physics with 10+ years of C-level and BD experience. Turns quantum-ready optimization into a focused industrial product.
LinkedIn →
Prof. Dr. Thomas Bolz
Co-Founder · Chief Commercial Officer
Entrepreneurship, AI, and digital-business experience. Sharpens positioning, partnerships, and revenue logic for scalable B2B.
LinkedIn →
Maryam Bahrami Zanjani, M.Sc.
Co-Founder · Quantum Modeling & Optimization
Owns the modeling layer where industrial complexity becomes optimization IP, in QUBO/Ising-style formulations.
LinkedIn →
Mahdi Khorrami, M.Sc.
Co-Founder · AI/ML & Platform Engineering
Turns AI and optimization into deployable software: digital twin, surrogate modeling, solver orchestration, platform.
LinkedIn →Let’s talk
Have a complex decision workflow, platform partnership, or technical collaboration in mind? Tell us what you are working on. We typically reply within 2 business days.
- ✓ Tailored product demo available
- ✓ Technical deep-dive on request
- ✓ Built to integrate with existing systems