Eight quantum algorithm approaches applied to logistics demand forecasting. From kernel methods to variational circuits, mapped against classical baselines with honest NISQ hardware assessments.
Full-day workshop on quantum-enhanced demand forecasting for logistics. Covers eight algorithm approaches including VQCs, quantum kernels, QBMs, and amplitude estimation applied to SKU-level demand planning, safety stock, and promotional uplift modelling.
Logistics demand forecasting at SKU level involves hundreds of correlated features: promotional calendars, weather, competitor pricing, supplier lead times, and seasonal patterns that shift year to year. Classical methods (ARIMA, Prophet, gradient boosting) handle many of these well, but certain problem structures may benefit from quantum approaches. This workshop examines eight specific quantum algorithms and how they map to forecasting sub-problems. Quantum kernel methods (Havlicek et al. 2019) can detect non-linear seasonal patterns in high-dimensional feature spaces. Variational quantum circuits produce probabilistic demand distributions rather than point forecasts. Quantum Boltzmann machines generate synthetic demand scenarios for stress testing. Amplitude estimation quantifies tail risks for safety stock calculations. Each approach is benchmarked against classical baselines with explicit accuracy comparisons, and participants work through a hands-on model build using PennyLane. Current NISQ hardware limits circuit depth to roughly 100 qubits with meaningful noise, so the workshop is honest about which approaches are research-stage versus near-term deployable.
Full Day Workshop structure with scheduled breaks. Content is configurable to your organisation's forecasting maturity, product portfolio complexity, and data infrastructure.
| # | Session | Topics |
|---|---|---|
| 1 | Classical Forecasting Limits and Quantum Opportunity Where traditional time-series methods break down in logistics demand planning | |
| 2 | Quantum Algorithms for Demand Forecasting Eight approaches from kernel methods to variational circuits |
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| Break, after 60 min | ||
| 3 | Feature Engineering and Data Pipelines for Quantum Models Encoding logistics data into quantum-compatible representations |
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| 4 | Interactive Demonstration Quantum forecasting model construction on a simulated logistics dataset |
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| Break, after 90 min | ||
| 5 | NISQ Constraints and Honest Benchmarks What current hardware can and cannot do for production forecasting |
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| 6 | Integration and Adoption Framework Connecting quantum forecasting research to production planning systems | |
| 7 | Q&A and Action Planning | |
Workshops are designed and delivered by QSECDEF in collaboration with sector specialists. All facilitators have direct experience in both quantum technologies and logistics systems.
Workshop design and delivery
QSECDEF brings world-leading expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our advisory membership spans 600+ organisations and 1,200+ professionals working at the intersection of quantum technologies and critical infrastructure security.
Domain expertise and operational validation
Logistics workshops are co-delivered with sector specialists who bring direct operational experience in logistics organisations. This ensures workshop content is grounded in regulatory, operational, and technical realities specific to the sector.
Sessions are configured around your forecasting maturity, product portfolio complexity, promotional calendar structure, and existing data infrastructure. Get in touch to discuss requirements and schedule a date.
Contact UsQuantum technologies are evolving quickly and new developments emerge regularly. This page was last updated on 15/03/2026. For the most current information about course content and suitability for your organisation, we recommend contacting us directly.
