Maximizing profit per mile
Solve the Multi-Vehicle Routing Problem by crawling dense graphs of weight, volume, and distance to find the routes that maximize margin.
Logistics is a problem of scale and constraints. Traditional computing hits a wall when fuel costs, vehicle features, and delivery windows all interact. A quantum computer can navigate these “Traveling Thief” and “Bin Packing” problems with unprecedented efficiency.
Solve the Multi-Vehicle Routing Problem by crawling dense graphs of weight, volume, and distance to find the routes that maximize margin.
Determine the optimal way to bundle packages and pack deliverable inventory to maximize profit per container, while respecting every regulatory and vehicle constraint.
Use advanced modeling to extend your demand foresight from minutes to days, allowing for proactive fleet positioning.
Allocate drivers and carriers based on a maximally even apportionment of road surfaces, reducing wear and improving driver satisfaction.
Optimization compute shouldn't require a standing contract or a research team. Tap the network when your route tables change and step away when they don't.
Bring your routing data, time windows, and capacity constraints. We'll put a quantum solver against the problem and report back what it finds.
Whether you’re a developer, infrastructure provider, or protocol foundation, we would love to learn more about your needs in the time of quantum supremacy and work together to ensure the seamless transition to a world with quantum computers.