Mandy had the pleasure of talking with Saikat Chaudhuri at the Mack Institute for Innovation Management at The Wharton School (University of Pennsylvania) for an episode of Mastering Innovation on Sirius XM Channel 132, Wharton Business Radio. They discussed how quantum computing will mean better, faster, and cheaper solutions to business challenges such as optimizing delivery routes. Predicting the properties of molecules for health care applications may lead to the development of more effective medicines. They considered addressing climate change, analyzing big data, and expanding the accessibility of quantum computing.
Transcript Excerpt
Saikat Chaudhuri: We often think about technology as though that’s the be-all and end-all. But technology is a tool that can help us solve problems. We have to actually understand what those problems are in order to be able to solve them, right? And what the requirements are. How do you think about that? How would you define these problems or do you hope your customers define those problems? Do they come and say, “Hey, we’ve got this problem,” or do you help them with it?
Mandy Birch: We definitely help them with it. It’s so early on, people are just considering what all the different potential applications are. We’re at an exciting time in the industry because previously, people thought that this was 20 or 30 years away before there will be any realistic applications. Many of our competitors still think that. They’re striving towards these mathematically perfect complete machines that are the ones that can solve some of the big problems. But being able to work early on with a customer in some of these more near-term applications is a very exciting time. We’re getting to the point now where we’ve been able to put in some different layers to make this technology more accessible to different communities of users, not just scientist.
Maybe a year ago you had to be a quantum information scientist to be able to get on the computer and start solving some problems. We’ve added some layers of abstraction with our programming language. Basically if you know Python now, you can get on our computers and begin to solve problems. We’ve opened it up to anybody who’s an AI [artificial intelligence] or ML [machine learning] expert. And we’ll be moving in that direction more and more in the future. But yes, it involves sitting down with a customer. We don’t have their data sets. We don’t have the real problems that they’re trying to solve here. We need to work very closely with our customers to get to that.
Chaudhuri: I love that. A few different things, again, you’re always giving me so much to work with, which I love.
Birch: You can tell how much I love what I do.
Chaudhuri: Absolutely. I hear it in your voice. I hear it absolutely in your voice. Do you also work with corporate customers right now who are thinking about potential solutions that they can market and sell beyond the research world and labs?
Birch: Absolutely. And some of our early adopting customers are the ones who value even a 1% improvement. They’re in a hyper-competitive space where margins are thin. So even if they can only optimize 1% better, it’s going to give them a huge competitive advantage. That’s who we’re seeing as the early adopters. And it’s also visionary people out there, because it’s so hard to get your mind around exponential technologies. It’s kind of like compounding interest. If you invest early, you’re going to be able to ride the wave to the future. But if you invest late, it’s really hard to catch up. Being able to have a vision for where you’re going in the future and be able to get those marginal improvements right now are very meaningful to our customers.
Chaudhuri: Yeah, and I can see that. There’s both a breakthrough in terms of real effectiveness, things we couldn’t do. But there’s also an element of efficiency. I think about the health care problem we talked about. And supply chains. If I can just have an improvement of a few percent, that helps. We talk about platooning, for example, with trucks and how they achieve savings in that space. We think about airline networks. There’s so many complicated systems out there that are really either low margin or even if they’re higher margin, they can benefit from the improvement on those fronts.
Birch: Exactly. And some of these more complex logistics problems are further on the horizon for being solved. But again, if you can map the problem down onto a fewer number of nodes and get a solution that’s 1% better. If your fuel bill is multimillion dollars per year and you can optimize your route by 1%, that’s a huge advantage.
Chaudhuri: Tremendous. I know for airlines these things sometimes make the difference between profit and loss and whether to keep a route or not. And for trucks and routes and all that, that makes sense. Now, clearly, as you go about this, you’re in an advanced technology space, there’s a lot of uncertainty out there and you need a lot of partners to jointly help develop potential solutions as well. How is it working with these partners? Is there a natural inherent alignment of interest or do you have to create that first somehow?
Birch: We haven’t had a whole lot of difficulty in finding people who are interested in this space. And again, it’s the people who understand the power of exponential technology where, say, if you add one quantum bit of capability, you’re not adding one marginal bit. You’re doubling your computation power. So if you go from 16 to 32 quantum bits, for instance, that is not a doubling of your competing power. It’s a 65,000-time increase because it’s exponential.