Rene: Hi! Welcome to QuBites, your bite-sized pieces of quantum computing. My name is Rene from Valorem Reply and today we’re going to talk about how you should be prepare your data for quantum computing. For this [topic] I’m very honored to have a special expert guest today, Andrew Fletcher. Hi Andrew. Welcome to the show!

Andrew: Hi Rene! Thank you, glad to be here today.

Rene: Can you tell us a little bit about yourself and your background, and how it relates to quantum computing and data?

Andrew: Yes, absolutely. So, I’ve been in and around data and analytics for quite some time now and really watched the progression from being involved in just executive reporting dashboards through to data warehouses along the way to advanced analytics, data science, machine learning and now with the advent of quantum computing I think we’re beginning to see a new frontier.

Rene: Awesome, well that’s impressive experience. Can you tell us what are the current benefits of large data analytics and what is the return on investment [in data platform modernization] we’re already seeing with clients?

Andrew: We’re seeing a number of benefits for our client and really the benefits moving from just reactive reporting of what happened yesterday to prescriptive - “based on what happened, here’s what we think might happen and we might need to prepare for this” - all the way to predictive. And the ROI comes in a variety of ways. We see customers [that are now] really beginning to recognize data is an asset that can be utilized. Just like infrastructure assets or buildings or vehicles, those types of things. In many cases customers who have taken advantage of that [approach] have begun to see data almost as a product that they’re now reselling managed services to their customers. [For example] warranty information as managed services. [For example], we see utilities [companies] may know more about the air conditioning units in the machines than the manufacturer of those machines because they now are able to monitor and combine the data for the [deeper insights] that are available. So, [there are] a number of ways that companies are able to monetize that data asset. Either finding the insights or actually turning the data and selling it back to their customers because they’ve been able to uncover insights and opportunities within the data.

Rene: That makes a lot of sense and I think at some companies it’s a main asset actually, all the data they’ve gathered throughout all the years. Of course [there’s new data sources to tap into like] social media and self-driving cars. All the tons of data that’s being captured, is a key component [in modern business operations and a] totally valuable asset. And when we think about quantum computing and all the advantages and benefits we’re going to see, what do you think are some of the challenges [for] these companies have large amounts of data? What are some of the challenges [in] applying quantum computing to [your business data]? And quantum-inspired optimization, how can that help [businesses now]?

Andrew: Well, we’ve seen a couple things. In a lot of cases our clients are asking us: “here’s my data, can you sprinkle some data science on it?” And it just doesn’t quite work that way. The data really has to be prepared and looked at in a way that allows it to be prepared for inventory and insight extraction. When we talk about quantum-inspired optimization issues, it comes back to probably one of the most vexing problems and that was the traveling salesmen problem. And now we can push that along into kind of a vehicle routing problem. So, when we look at, in that case, trying to find the shortest path that [would allow us to] visit X number of places only one time and then return to where we started, it becomes a very difficult problem to solve. [With quantum-inspired optimization] we can extract that problem to the supply chain. We can extract it to any sort of operational efficiencies that companies are going to gain whether it is scrap materials or [something else]. We’re seeing it in banking and capital markets, where the number of variables today have just gown exponentially. There was a time where our data scientists often could look at the data set and the answer would begin to come to them because they recognized [the patterns/possibilities]. There were a fairly insignificant amount of variables, so they could just see it. But today the variables have expanded so large that quantum computing is the vehicle we have to prepare for, [in order to begin looking] at the problems in a new manner. Using quantum physics to begin to apply [deeper insights] to problems that we’re wrestling with today.


Rene: Makes sense. And in the end, you probably also need to think about how you can transform your data in such a way that you can formulate it in this kind of energy saving, cost saving function, which is called objective functions. Like how can you, I think it’s called massaging the data, prepare the data and massage it so it fits into these kinds of [quantum] formulas that you would use? So, what action items can clients take today [to prepare their data for quantum computing]?

Andrew: Well, we’re seeing a couple things and I think really what you’re talking about is the data wrangling piece. We bring it in and we model it and that’s [primarily] how they can begin to prepare for quantum computing as it becomes more main stream. [Its about] focusing on how to model the exponential problems differently. Applying the properties of quantum physics to information management. Looking at their data [and] thinking of algorithms and models in a new way, so that there’s quantum-inspired solutions that we can begin to apply to the data sets. So, I think that’s the first piece to prepare [for quantum computing].

I think the second piece is to really find relevant business use cases within your business. So don’t just do the scatter approach. [Instead], let’s narrow it and find a use case where you really think there’s a high-value opportunity to be solving and applying your energies towards.

I think the third area [of data preparation for quantum] really is to think about a quantum workforce. The workforce today that would be involved with quantum computing is different than the workforce that we had 5 years ago, in terms of writing C# or just writing algorithms. Today’s quantum workforce needs to be composed of mathematicians, statisticians, data scientists; folks that understand and did well in math in school and really have maybe less of a programmer background and more of a statistician/mathematician background. I think the other thing that [companies with large amounts of data] can do [to prepare for quantum computing] is they can look to vendors. They can think about joining the Microsoft Quantum Enterprise Acceleration Program. So, there’s a number of ways that they can begin not only with their data but with their entire workforce and with their problem solving to begin to prepare for this.

Rene: That makes a lot sense and one of the partners that already has access to Azure Quantum to quite an elevated level [is Valorem] right? So, we can also help to solve these challenges.

Andrew we’re already at the end of the show. Thank you so much for joining us today and sharing your insights. I very much appreciate it.

Andrew: Well Rene I appreciate you having me today. There’s much work to be done but we’re certainly seeing that we can get there from here, so it’s been a pleasure speaking with you.

Rene: Thank you. And thanks everyone for joining us for another episode of QuBites. Watch our blog [and] follow our social media channels to hear all about the next episodes and when we release them. Take care, be safe and see you soon. Bye, bye.


Andrew: Bye.