Logistics
From last-mile routing to 3D cargo loading — find out exactly where quantum and hybrid approaches can improve your logistics operations compared to existing solvers.
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Where hybrid quantum algorithms show potential in logistics
We don't promise "quantum wins everything." We target the specific NP-hard corners of logistics where quantum components can offer better scaling, resilience under disruption, or improved handling of complex constraints.
Vehicle Routing
Context
Last-mile delivery, multi-depot routing, re-routing after delays — all under capacity constraints and time windows. As fleet size and constraint count grows, classical solvers explore a shrinking fraction of the solution space.
Quantum approach
We map routing problems to QUBO / Ising formulations and apply QAOA or Quantum Annealing to explore solution spaces that classical branch-and-bound struggles with at scale. Benchmarked against your current solver on your own routes.
3D Cargo & ULD Loading
Context
Packing containers, ULDs, or pallets in 3D — respecting weight limits, center-of-gravity constraints, stacking rules, and hazardous goods segregation — is a combinatorial problem that scales exponentially with item count.
Quantum approach
We encode loading constraints into QUBO formulations and benchmark quantum / hybrid solvers against your current packing software on real cargo manifests.
Fleet & Crew Rostering
Context
Matching drivers and crew to routes and shifts under rest-period rules, skill requirements, vehicle compatibility, and peak-demand coverage creates constraint densities that make exhaustive classical search impractical at scale.
Quantum approach
Hybrid decomposition splits the scheduling problem into a classical master problem and a quantum sub-solver. We benchmark against your current scheduling system on realistic roster sizes.
Supply Chain Network Design
Context
Choosing where to locate warehouses and hubs, how to allocate demand, and how to build resilience against disruptions — is a large-scale combinatorial problem under cost and service-level constraints.
Quantum approach
We map facility-location and network-design to QUBO and use QAOA or Quantum Annealing, benchmarked against classical MIP solvers.
Demand Forecasting & Anomaly Detection
Context
Forecasting demand and detecting anomalous shipments in imbalanced datasets — where positive cases are rare and training data is limited. Classical models often overfit in these regimes.
Quantum approach
Hybrid Quantum Neural Networks (HQNN) and Quantum Support Vector Machines (QSVM) with quantum kernels. Hybrid models maintain predictive quality when training data shrinks, outperforming classical counterparts.
Predictive Maintenance for Fleet & Assets
Context
Predicting failures in trucks, ground equipment, forklifts, conveyors, and reefer containers from sparse sensor streams — where failure events are rare and labels are scarce.
Quantum approach
QSVM with quantum feature maps and HQNN applied to sensor windows. Benchmarked against your existing maintenance models on the same data splits.
A decision-ready report from every run
Every Studio run produces a research-paper-style PDF that includes:
- Use case mapping and assumptions
- Algorithm choice and rationale
- Side-by-side benchmark quantum / hybrid vs your classical baseline
- Metrics accuracy, runtime, cost, variance across seeds
- Scaling outlook as hardware improves
A report format aligned with how logistics problems are actually validated by operations teams.Download the sample report
Use case · capacitated vehicle routing with time windows (40 vehicles, 156 stops, 4-hour delivery windows)
[Sample two-page PDF spread shown on larger screens]
What happens when you bring us a logistics problem
Five structured steps that turn a business question into a quantum solution — with code and a PDF you can actually use.
Use Case
Describe your problem.
"Re-route 40 vehicles after three depot closures, under 4-hour time windows." Quantum Assistant maps it to QUBO.
Output: a downloadable PDF report and reusable code. Total time on a template: under an hour.
Three ways to start, depending on where you are
Whether you're testing a hypothesis or running a regulated benchmark, you can engage with us at the level that matches your stage.
Explore
For whom
Quants, R&D engineers, individual researchers
What you get
Self-serve Studio access, Quantum Assistant, 20+ templates
Output
Runnable code + PDF report
Validate
For whom
R&D teams with a defined problem and data
What you get
4–8 week POC: your data, honest benchmark vs your model, research-grade report
Output
Decision-ready benchmark
Deploy
For whom
Innovation leads, CTOs, heads of operations
What you get
3–6 month pilot: full pipeline, team enablement, QPU runs, integration
Output
Production-ready hybrid module + trained team
What a logistics Proof of Concept looks like
Eight weeks from a defined problem to a decision-ready benchmark. No black box, no vendor lock-in.
Scoping
We review your problem, your data, and your current solver. We agree on a measurable success criterion — solution quality, runtime, cost, or robustness under disruption.
Build & benchmark
We map your problem to one or more quantum or hybrid approaches, run them in Studio, and compare against your classical baseline on your data.
Decision-ready report
You receive a research-grade PDF: methods, results, honest scaling outlook, and a clear recommendation on whether this problem class is worth deploying further. Plus the runnable code.
We don't promise quantum advantage on every problem. The point of a pilot is to honestly find out whether your problem falls into the zone where quantum or hybrid methods give a measurable edge.
Common questions from logistics teams
No. The Quantum Assistant guides you through every step — describing your problem, picking an algorithm, running it, reading results. You stay in your operations language.