An anonymous reader shares a report:These are still early days for quantum computing, far too soon to talk about domain-specific quantum systems. But if there are areas hungrier than ever for what quantum is best at — dense optimization problems at scale — the future cannot arrive fast enough. More specifically, the golden grail for quantum computing — the “traveling salesman” problem — could revolutionize the transportation industry in particular, in addition to the world’s largest retailers dependent on accurate shipping data. Quantum capabilities in this arena are so critical that the first production quantum systems at scale could be purpose-designed and optimized simply for this type of problem. While these days we don’t think of Amazon’s delivery aspects much since the carriers are so often the focus, the combined capability of vast search coupled with near-real-time delivery dates matched to location took Amazon years to get right — and was a billion-plus dollar effort in compute time.
Peter Chapman says “infinite compute” can be brought to bear to refine the entire process that happens the moment you search for “USB drive” on Amazon, confirm your shipping location, and select only products that arrive tomorrow. The density of calculations required — pulling from warehouse availability to planes, trains, and automobiles and their various routes through your own hometown — is staggering. “It’s the ultimate traveling salesman problem,” he laughs. Chapman should know what this takes because he led the development of many of the technologies that became the fast, reliable Amazon Prime service. As director of engineering, his team of 240 engineers took Amazon from requiring customers to search and select a product and wait until checkout to find out how long delivery would take. “That meant a lot of abandoned carts and a bad user experience,” he says.
With global products, shipping routes, customers, carriers, product availability and warehouse locations, the order was so tall, it took rearchitecting Amazon infrastructure to do it at reasonable enough scale. “There is a practical limit to the computational resources you can apply to this, even at Amazon. We could easily consume 100x the compute but Amazon couldn’t afford it,” Chapman says. “There is infinite need for compute for this problem so we had to find the right tradeoffs in optimization and find what you can get for a certain amount of money spent — and we’re talking billions here. Our goal was to make sure it wasn’t $20 billion.” He adds that the cost of these systems were growing faster than the top line of Amazon’s sales.