By Monica Hernandez

One-on-ones with QSA researchers

## Ivan Deutsch

University of New Mexico, Distinguished Professor, Regents’ Professor

Director Center for Quantum Information Control (QuIC)

Describe your journey to QIS.

I came across the field of study from a physics background. I became interested early on in the foundations of physics, and quantum mechanics was just this incredible, fascinating subject. I then learned that these foundational ideas about the nature of the universe had important implications for technology. The melding of quantum physics with computer science and information processing really blew my mind and drew me in. I started to learn about how quantum mechanics taught about a new way to think which could allow us to revolutionize computers, communication systems, and ultraprecise sensors. The new centers that have been developed under the National Quantum Initiative, the Quantum Systems Accelerator in particular, give us a fantastic opportunity to advance research in this field and allow us to realize the tremendous potential of quantum technology.

What do you find the most challenging and the easiest to tackle?

It’s easy working in certain areas that are your specialty because you have your own deep experience and knowledge already. What’s challenging in this field is its interdisciplinary nature. This means going out of your comfort zone and learning about other disciplines so that one can bring together ideas in a new way. But it’s also where it’s fun because you get to learn new things. You expand how you think when you take a traditional approach of your own discipline and mix it in with the perspective of others.

What excites you the most about this particular point in time?

I’ve been working in this field for almost 30 years. There were exciting ideas with a lot of rapid development in the beginning. But it took us a long time to get to where we are today, where it’s real, where you see these ideas being realized in the laboratory, industry, and national labs. It’s incredibly satisfying to see this field come to fruition. This also requires us to think of the right way to train and educate younger generations for the opportunities to come. It’s exciting, but it’s also daunting. What’s the right approach to keep creativity alive? Although there can be directed goals, you still need to be innovative, allow people to be creative, and foster that thinking. Also, being a teacher helps me in my work because I have to assess how to communicate.

Goals

We get exposure to the different kinds of capabilities and what’s possible at the QSA in ways that we might not yet have thought about or imagined individually. And so that opens the door to new directions of research and activity that are realized because of the collaborations at QSA and the connectivity between the different people and groups. New goals will grow from this synergy.

Advice for early career researchers or students

What makes our field both interesting and challenging is that it’s interdisciplinary. It doesn’t necessarily fit in one box or category. You might be studying computer science, but you need to learn some physics, or if you’re a physicist, you need to know some computer science or engineering. I’m a firm believer in a strong foundation while avoiding being too focused too early on. Later, one can specialize, but you need to have a broad perspective and a set of tools you learn because you don’t know what tools you’ll need right away. Other tools might develop later on, so get a broad education.

## Chao Yang

Berkeley Lab, Senior Scientist, Applied Mathematics

Describe your journey to QIS.

About three or four years ago, our ALD (Associate Lab Director) Kathy Yelick approached me and some other people about investigating how quantum computing can be used to solve applied math problems. At that time, I didn’t know much about quantum computing. I heard about it, of course, but I needed to learn exactly how it works and how it can be applied to solve the problems we’re interested in. So then, about a year later, I heard a talk about using quantum computing to solve quantum chemistry problems, particularly this variational quantum eigensolver (VQE) approach. Given my background in solving eigenvalue problems, I immediately got interested and started to read more about it. And then, about two years ago, I attended a workshop at the Simons Institute with a couple of talks about quantum signal processing and block encoding for solving linear algebra problems. So, this immediately got me even more interested. I started to investigate the technique that can be used to solve a linear algebra problem, particularly the sparse matrix computation problem, which I’ve been interested in. And this is when I started participating in QSA.

What do you find the most challenging and the easiest to tackle?

The most difficult part is how to think about quantum computing. It’s very different from the classical way of thinking about computing. For example, in classical computing, we always work with a vector or matrix. We can print them out and look at them. But in the quantum world, the state is in the quantum device. You can measure it and get observables out of it, but you cannot just say, I want to look at this vector. This is something that I was not used to at the very beginning, but I’m now starting to get more and more used to it.

The easiest part relates to my background and research. I frequently work with a particular type of matrices called unitary matrices. You can view quantum computing as a tensor computation or matrix computation. Hence, the connection is very natural, and it’s easy for me to understand some terminologies regarding unitary matrix decomposition, tensor product, and entanglement in terms of matrix or tensor rank. However, overall there’s a need for more tutorials and textbooks for a more applied math audience. The traditional applied math curriculum in universities doesn’t include the type of languages that researchers in quantum computing typically use, like entanglement and quantum gates.

QSA gives me a very good perspective on quantum computing, how it can be used, and how quantum algorithms can solve very large problems with low complexity. It’s a very rewarding experience to work with many researchers in this community and intellectually challenging.

Advice for early career researchers or students

Over the last 20 years, I have worked with many people in different fields. So, I got in the habit of listening to people and being genuinely curious about what they do while looking at problems from different perspectives or angles. That helped me move to this quantum computing world relatively easily. So you have to be open-minded and be able to listen to other people and have a conversation and learn the language and different views.

## Mohan Sarovar

Sandia National Laboratories, Distinguished Member of Technical Staff

Describe your journey to QIS.

A pivot in my education in the early 2000s inspired me to pursue quantum information science. I majored in electrical engineering and computer science as an undergrad. I later completed a master’s degree in these fields as well. I worked for a year as an engineer, but I also wanted to see what else was out there. During my studies, I was attending a lot of talks. One of those talks was by Lov Grover, who pioneered Grover’s search algorithm for quantum computing, and I was hooked! It was a fascinating topic because the emerging field integrated many things of interest to me: computer science, technology, fundamental physics, and engineering. So, I decided to pursue a Ph.D. at the University of Queensland, Australia, in 2003, working with Gerard Milburn and Michael Nielsen, pioneers of the field, and have been fascinated by it ever since. For example, on the same day, I can work on something very applied, such as building a device or this widget, and then work on something fundamental, like what quantum physics tells us about nature. Therefore, this field combines these foundational questions about reality and nature with practical things like building devices and computers. I don’t know any other field that requires this integration!

What do you find the most challenging and the easiest to tackle?

In contrast to when I started, one of the most difficult things these days is just keeping up with the field. The explosion in research and the number of people working in the field has led to amazing results, with breakthroughs reported frequently. And so, in the past, you might have a noteworthy result come out sporadically, so it was easier to keep up with it. These days, I find myself drowning in things I want to learn, but I don’t have enough time.

It’s an exciting time to be in the field, there’s so much progress and community involvement, and this is all great motivation to keep learning.

Goals

A lot of my attention these days is going towards developing the foundations of what I call quantum computational imaging and sensing, which is even more multidisciplinary than quantum computing because you have to bring together several aspects of existing quantum technologies in novel ways, integrating sensing with computation. In classical computing, this is fairly routine these days. For example, for edge computing, computers are at the same location where information is being collected. And one can also think of the same thing in the quantum case, where you might have a quantum computer directly processing coherent information from an electromagnetic field. I think this might be one of the killer apps of quantum computing, and I think it’s something that hasn’t received enough attention so far. I’d like to help establish the foundations of how one would build a device that would do quantum computational imaging and sensing and what some of the advantages are of having such a device.

Having something like QSA, which funds a broad breadth of quantum technologies, has been really useful. So, for example, some researchers in QSA work on quantum transduction, which is an important part of this concept of computational imaging and sensing. It’s been helpful to talk to different groups to understand where experiments are going and incorporate that into how I think about sensing. The breadth of in-house expertise at QSA in almost any aspect of quantum information has been instrumental.

Advice for early career researchers or students

These days, there’s no lack of opportunities or funding in the field, so my advice to a junior researcher is to figure out the essential questions in the field. There will be fads, phases, churn, and bustling activity in such an active field now, but that won’t sustain you in a research career. Focus on the fundamental questions that are yet to be resolved.

## Alicia Magann

Sandia National Laboratories, President Harry S. Truman Postdoctoral Fellow in National Security Science and Engineering

Describe your journey to QIS

I did my bachelor’s degree in chemical engineering, and at that time, my research focused on control systems engineering. I was very far removed from quantum computing as an undergraduate. I’m pretty sure I’d never heard of it. In the final year of my bachelor’s degree, I took an elective class on quantum mechanics. I enjoyed it so much that I wondered if I should add a physics major on top of chemical engineering and delay my graduation. While questioning what I should do, I had the good fortune of learning that my new interest in quantum mechanics shared a point of intersection with my long-standing interest in control systems engineering: quantum control. Quantum control is a subject exploring how we can control the behavior of quantum-mechanical systems in a desired manner, and in essence, it’s a mix of control systems engineering and quantum mechanics.

It became my goal to pursue quantum control research for a Ph.D. I worried a lot about whether it would be possible for me to do a Ph.D. in quantum control without a physics undergraduate degree. I decided to try, and in the end, I was fortunate that things worked out well for me. Working on quantum control introduced me to many adjacent topics, like quantum information science. As I learned bits and pieces of how quantum computers might work and what they might be useful for, I became interested in how I might connect to this exciting, growing field, given my background. This is what led to me join Sandia National Labs. Sandia is primarily an engineering lab with in-house, interdisciplinary research across many domains of quantum information science. Today, I’ve been working in quantum information science for a few years, and my research focuses on quantum control and quantum algorithms, and exploring ways to combine the two. I continue to be inspired by the potential of quantum computing to impact science and technology, and the research challenges that still need to be addressed to realize this potential.

What do you find the most challenging to learn and tackle?

It can be hard to nail down which research directions and ideas are most promising and worthwhile to pursue. How can we ensure that work isn’t only novel for the sake of being novel but also useful, relevant, and impactful? Along the same lines, if many researchers simultaneously try to solve the same important problems, how can we differentiate our contributions?

Less philosophically, two big challenges on the technical side of things stick out. One is the challenge of scaling up quantum hardware while maintaining exact and accurate control. The second is the challenge of leveraging quantum hardware for useful applications as soon as possible. How can we write algorithms that require the fewest resources possible but solve meaningful problems?

Goals

I’m struck by the scale of the quantum computing effort today. It’s an exciting time with many resources dedicated to quantum computing R&D across many different institutions. As the field of quantum computing continues to grow, there is a need for more engagement in workforce development efforts in quantum computing to train the workforce to sustain this growth. I hope to get more involved with outreach efforts and collaborations to support this.

Advice for early career researchers or students

My first recommendation is for students interested in quantum information science to take introductory courses in linear algebra and quantum mechanics. This will serve as an essential foundation to build on as students go on to learn about quantum information science. Then, if a course in quantum information science or quantum computing is available, that’s an ideal next step. In addition to courses, there are many free resources online for learning the basics of quantum information science – digging through them on your own can be overwhelming. Still, if you search around enough, you should be able to find lecture notes, tutorials, and good videos that you like and fit your level and background.

Beyond courses and self-study, I recommend connecting to someone in the field who can give you personalized suggestions on targeted reading, exercises, research opportunities, etc. There may be professors at your university working in, or adjacent to, quantum information science, and they are an ideal initial point of contact. Don’t be shy about emailing them to ask for a meeting. Tell them about yourself, your background, and your interest in learning more. If you don’t hear back, follow up!

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Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 16 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.

Sandia National Laboratories is a multimission laboratory operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. Sandia Labs has major research and development responsibilities in nuclear deterrence, global security, defense, energy technologies and economic competitiveness, with main facilities in Albuquerque, New Mexico, and Livermore, California.

The Quantum Systems Accelerator (QSA) is one of the five National Quantum Information Science Research Centers funded by the U.S. Department of Energy Office of Science. Led by Lawrence Berkeley National Laboratory (Berkeley Lab) and with Sandia National Laboratories as lead partner, QSA will catalyze national leadership in quantum information science to co-design the algorithms, quantum devices, and engineering solutions needed to deliver certified quantum advantage in scientific applications. QSA brings together dozens of scientists who are pioneers of many of today’s unique quantum engineering and fabrication capabilities. In addition to industry and academic partners across the world, 15 institutions are part of QSA: Lawrence Berkeley National Laboratory, Sandia National Laboratories, University of Colorado at Boulder, MIT Lincoln Laboratory, Caltech, Duke University, Harvard University, Massachusetts Institute of Technology, Tufts University, UC Berkeley, University of Maryland, University of New Mexico, University of Southern California, UT Austin, and Canada’s Université de Sherbrooke. For more information, please visit https://quantumsystemsaccelerator.org/

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