How Global Payment Technology Company Mastercard Is Preparing To Deliver Value With Quantum Computing
- Mastercard is educating and promoting awareness vertically and horizontally throughout the organization.
- The oft-mentioned quantum use cases do not apply; Mastercard is looking into use cases that support its core business.
- They are navigating the hype and looking at both near-term and long-term timelines, hoping to start delivering value within two to four years.
When you think about Mastercard, you think about global payments. But, what exactly is a global payment? Every financial transaction involves data routing, and every transmission has associated costs. The obvious cost to consider is money, but other important costs include time and energy. Mastercard is interested in completing transactions in the least time at the least cost while consuming the least energy, and quantum computing might become a viable solution for that.
Steve Flinter, Vice President, Artificial Intelligence & Machine Learning & Head of Emerging Tech, Mastercard Foundry R&D, leads a team researching quantum computing and other emerging technologies that may become critical to maintaining Mastercard’s competitiveness in the global payments market.
“I work forMastercard in our R&D division and I, among other things, lead up our research areas around emerging technologies,” Steve explains in an episode of “The Qubit Guy” podcast. “And that includes obviously things like quantum, but also areas that we focus on like 5G, looking at new forms of payments. So anything that we think is going to be relevant to Mastercard or our customers now or in the foreseeable future.”
Steve discussed how Mastercard got started in quantum computing, the composition of the team working on quantum computing, how the organization is preparing for quantum computing, what the company might end up using quantum computing for, and how the enterprise is monitoring developments in quantum networking.
“Hello, quantum world!”
How does a company with the size and reach of Mastercard decide to start researching any emerging technology? Specifically, how did they decide to start investing time and money into a nascent technology such as quantum computing? Was there a directive from the upper echelons, or did the project have a grassroots origin?
“I think in our case, it was probably a little bit more bottoms up,” Steve replied. “Although there had been some interest in the upper echelons, especially coming from an executive vice president, the real impetus to get started was external. They found out about a research project that IBM was organizing in Ireland. It was co-funded by the government and included local universities and startups.”
“We started with this multi-part collaboration with other leading players, and we’ve built and grown the team from that,” Steve added.
The Composition of the R&D Team
If you try to guess the composition of Mastercard’s quantum computing R&D team, you would be forgiven for listing physicists and financial experts. Instead, the team consists mainly of data scientists and mathematicians. However, this is true for the side of the organization interested in quantum computing applications.
“I work with other colleagues in other parts of the organization who are looking at other applications of quantum technology, so quantum networking and so on,” Steve said, “and they bring specific expertise around cryptography and networking type technology.”
That’s not to say that physics has no role at all. However, these other backgrounds are more highly sought after for the specific research they’re pursuing.
One of the most important things any enterprise can do these days is to educate employees at all levels and promote awareness of what quantum computing is and what it can do. Mastercard is not only taking this approach with senior executives and its R&D team but also with its technologists and engineers across the organization.
“What technologies are coming out of the tech space that we think is going to be impactful for us and for our customers,” is the focus, “and how should we think about that and invest in that as an organization.”
The internal audience includes product managers and product owners. While the R&D team explores relevant use cases for quantum computing and how to implement them, they also need to help the product teams understand how to think about these applications.
“The classical way of thinking about approaching problems from a software or engineering or computer science approach may not always be the right ones,” according to Steve, “and so there’s definitely a degree of education and awareness we need to raise there.”
Promoting awareness includes more than just providing internal training and educational resources. Thought is given to generating interest, especially among developers, and encouraging their individual quantum journeys whether they directly support R&D efforts. Some developers are naturally interested in quantum computing, and that “appetite” is nurtured and nourished.
Mastercard is deftly navigating the quantum hype and focusing on relevant use cases and realistic timelines. They are mindful of the long term and can consider roadmaps, but they concentrate on the short-to-medium term. Part of the challenge of exploring quantum computing is establishing a sense of urgency without overselling and underdelivering.
“I think for Mastercard and for some of our customers, that two to three to four-year horizon is where we try and place the work that we’re doing,” Steve said, “what is happening within that time horizon that can potentially deliver value?”
One interesting comment is that the generic field of “quantum finance” does not apply to Mastercard. The widespread use cases for quantum computing in finance are derivatives pricing, portfolio optimization, arbitrage trading, and the like. Still, those pertain to investment banks and securities markets, not payment transactions.
Loyalty and rewards programs
Mastercard’s most relevant use case might be its loyalty rewards program, which is classified as an optimization problem, not a “quantum finance” problem.
“Mastercard…has a very large loyalty and rewards business,” Steve noted, “it may not be known to all of your listeners, but it’s certainly a key part of our business, and we’re one of the biggest players globally in that space.”
Optimizing loyalty rewards is more challenging than it might sound. The problem is not as simple as determining whether or not a reward is warranted. Which is the most appropriate reward for each specific customer? Or, perhaps, which loyalty event is the most suitable? The problem, it turns out, consists of many optimization sub-problems.
Another relevant optimization problem is routing transactions through Mastercard’s network of networks. Information moves between merchants, retailers, issuers, and banks, plus there’s more than one kind of payment network. These numbers are growing over time, continuously adding to the computational challenge of finding the best way to move money. Adding a form of payment grows the routing problem exponentially or combinatorially.
Machine learning and fraud detection
The R&D team is also exploring quantum machine learning (QML).
“Mastercard is using machine learning in lots and lots of different avenues, not least which would be fraud detection, fraud reduction,” according to Steve, “so there are applications for where there can be specific quantum approaches to some of those areas.”
A recurring theme is seeking out use cases that are not only aligned with Mastercard’s core business but also with its near-term timeline of two to four years. While they monitor potential applications with longer horizons, such as 5, 10, or 20 years, their focus is on finding real value as early as possible.
“We’re not interested in putting quantum into production just for its own sake, just to show that we can do it,” Steve noted, “it’s much more around can we demonstrate that we can solve a problem through a quantum driven process that is meaningfully and notably better than the alternative that we could do through traditional CPU, GPU type computing.”
And, “better” is not just about results. “Better” might mean saving time, reducing energy costs, or other benefits. The important thing is achieving a measurable commercial benefit.
Mastercard faces technical challenges, as well. Most quantum computing these days involves running Python in Jupyter notebooks. Mastercard, however, has to implement solutions into a 24/7 pipeline. Also, Mastercard has mountains of data. Consequently, another challenge is structuring big data problems in ways that can potentially harness quantum computation.
Mastercard also tracks developments in cybersecurity and cryptography, including NIST’s progress with cryptographic standards and post-quantum cryptography (PQC). But, compared to their interest in optimization problems (loyalty rewards, network routing) and machine learning (fraud detection, fraud reduction), their interest in quantum cryptography is more monitoring than researching. Their strategy includes using quantum-resistant or quantum-proof encryption, but they recognize the 10–20 year timeline involved.
“We published a standard last year,” Steve added, “around contactless payments, and that standard took a deliberate set of decisions around the cryptographic schemes that it was using so that it would be quantum-resistant by the time that standard was fully implemented and rolled out.”
Because standards take a long time to write, Mastercard’s system architects and security architects are developing them with future quantum computing potential in mind. But, they recognize that the threat to cryptosystems is not immediate. So, they’ve got eyes and ears on long-term considerations, but their heads are mostly down, focusing on near-term opportunities.
This article is based on an episode of The Qubit Guy podcast, which explores business and technical questions that impact the quantum computing ecosystem. Hosted by Classiq CMO Yuval Boger, the interview podcast features thought leaders in quantum computing.
Originally published at https://www.classiq.io.