Logistics Full Day or Half Day Workshop

Quantum Boltzmann Machines

Classical generative models struggle with tail events and correlated demand shocks. Quantum Boltzmann machines offer a different sampling mechanism. This workshop examines where that difference matters for logistics scenario planning.

Full day (6 hours) or half day

In person or online

Max 30 delegates

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Workshop Description

Quantum Boltzmann machines for logistics scenario generation under uncertainty. Covers QBM theory, applications to promotional planning and demand shock modelling, NISQ constraints, and quantum-inspired alternatives for current deployment.

Logistics planning depends on realistic scenario generation: promotional demand spikes, new product launch trajectories, supply disruptions, and correlated demand shocks across product categories. Classical Restricted Boltzmann Machines (RBMs) and Variational Autoencoders (VAEs) struggle with high-dimensional joint distributions where tail events carry operational significance. Quantum Boltzmann Machines (QBMs), first formalised by Amin et al. (2018), exploit quantum tunnelling to sample from complex energy landscapes more efficiently than classical Markov chain Monte Carlo. On D-Wave hardware, the annealer itself acts as a Boltzmann sampler. On gate-based hardware, variational Gibbs state preparation offers an alternative path. Both approaches remain constrained by current qubit counts and coherence times, and quantum-inspired tensor network methods provide a deployable bridge. This workshop examines the theory, works through logistics-specific applications, and provides an honest assessment of where QBMs outperform classical alternatives and where they do not.

What participants cover

Classical generative model limitations: where RBMs, VAEs, and MCMC sampling underperform on high-dimensional logistics demand distributions with significant tail events
QBM theory: transverse-field Boltzmann machines, quantum tunnelling for faster mixing, the Amin et al. (2018) framework, and variational Gibbs state preparation
Logistics applications: promotional scenario generation, new product launch demand modelling, and supply chain stress testing under extreme disruption events
Hardware realities: QBM implementation on D-Wave annealers versus gate-based hardware (IBM, Quantinuum), current qubit and connectivity constraints
Quantum-inspired alternatives: tensor network sampling methods and classical Boltzmann accelerators deployable on current infrastructure
Integration architecture: positioning QBMs as scenario generators within existing planning, optimisation, and simulation systems

Preliminary Agenda

Full-day session structure with scheduled breaks. Content is configurable to your team's demand planning processes, product portfolio, and existing modelling infrastructure.

#	Session	Topics
1	Classical Generative Models and Their Limitations Where classical probabilistic methods underperform under uncertainty
2	Quantum Boltzmann Machines: Theory and Architecture From classical RBMs to quantum-enhanced sampling	Classical Restricted Boltzmann Machines (RBMs): energy-based models, contrastive divergence training, and sampling limitations Quantum Boltzmann Machines (QBMs): transverse-field terms, quantum tunnelling for faster mixing, and the Amin et al. (2018) framework Variational quantum thermalisation: preparing Gibbs states on gate-based hardware versus annealing approaches
Break, after 50 min
3	Logistics Applications: Scenario Generation Under Uncertainty Promotional planning, new product launches, and demand shocks	Scenario generation for promotional planning: sampling from joint distributions over correlated product categories New product launch modelling: generating demand trajectories with limited historical data using quantum-enhanced priors Demand shock simulation: stress-testing supply chains under extreme weather, port disruptions, and pandemic scenarios
4	Interactive Demonstration: QBM Scenario Pipeline Full-day format only	Facilitator-led walkthrough: training a small QBM on a logistics demand dataset and sampling scenarios Comparing QBM-generated scenarios against classical RBM and VAE baselines for distributional fidelity Discussion: identifying use cases from your organisation where classical generative models struggle with tail events
Break, after 60 min
5	Hardware Realities and NISQ Constraints Current capabilities and the fault-tolerant frontier	QBM on D-Wave: using quantum annealing as a sampler for Boltzmann distributions and current qubit connectivity constraints Gate-based QBMs: variational Gibbs state preparation on IBM and Quantinuum hardware, circuit depth limitations Quantum-inspired approaches: tensor network methods and classical Boltzmann sampling accelerators as near-term alternatives
6	Integration and Adoption Framework Inserting QBMs into existing planning systems	Architecture patterns: QBM as a scenario generator feeding into classical optimisation and simulation layers Data requirements: what training data quality and volume QBMs need versus classical generative models Decision criteria: when QBM exploration is justified based on problem dimensionality and distributional complexity
7	Q&A and Pilot Planning

Designed and Delivered By

Workshops are designed and delivered by QSECDEF in collaboration with sector specialists. All facilitators have direct experience in both quantum technologies and logistics systems.

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.

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.

Commission This Workshop

Sessions are configured around your demand planning processes, product portfolio, scenario requirements, and existing modelling infrastructure. Get in touch to discuss requirements and schedule a date.

Quantum 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.

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