Dr. Brian La Cour Answers Whurley’s Questions About Quantum Emulation and His Dreams of Being an Entomologist

I’m psyched this week to share my interview with Dr. Brian La Cour, Director of the Center for Quantum Research at the University of Texas’s Applied Research Laboratories (ARL). Dr. La Cour earned his Ph.D. in Physics from UT, specializing in statistical physics, and his M.S. degrees in Physics and Mathematics from the University of New Orleans, specializing in mathematical statistics. Since 2001 he has been a staff researcher at ARL, where he heads basic research in the Signal and Information Sciences Laboratory. His current research interests are quantum computing, contextuality, and quantum foundations. He’s already published several papers this year, including “Using Quantum Emulation for Advanced Computation,” which was one of the main reasons I wanted to talk to him. Turns out, he’s also got a great sense of humor and gives a fabulous interview, as you’ll soon see.

Did you always know you would be a quantum physicist?

No. When I was in middle school I dreamed of being an entomologist. I was absolutely obsessed with insects! Those dreams were squashed when I later realized that most of what they do is study how to kill bugs. I took a physics class my senior year of high school, got a “D” in it, and decided that was the career for me. At that time I had an epiphany of sorts that everything must be deterministic. Later, in college, I took a modern physics class and learned, much to my dismay, that quantum physics implies a fundamentally random universe! I think that’s what started my love/hate relationship with quantum mechanics.

The underlying mechanics of quantum computers and how that style of computation is applied to problems can be difficult to wrap one’s head around. What’s one of your favorite analogies for explaining a particular piece of that puzzle?

Every popular description of quantum computing starts off by drawing a distinction between classical and quantum bits (qubits). Qubits are said to be a “superposition” of logical 0 and 1 states. At this point, one starts waving hands, making strange intonations, and invoking half-dead cats to convey that this is something very different and utterly alien to our common experience. But superpositions are all around us. A guitar chord is a superposition. Indeed, any qubit state can be represented by a combination of sine and cosine waves. The trick is what happens when you measure them.

What hard problems still need solving before we’ll see a practical quantum computer?

Understanding and controlling noise to allow full scalability is the greatest outstanding challenge. Many physicists have the view that this is “just an engineering problem.” To be sure, enormous strides have been made, but it’s not clear to me that we have a clear path forward. I anticipate a long road ahead.

What are the primary aims of the research you’re doing now?

Most of my research focuses on identifying classical analogues to concepts in quantum mechanics and engineering them to practical advantage. Over the past few years we’ve looked at what I like to call “quantum emulation.” Basically, we use analog voltage signals to represent quantum states and analog electronics to manipulate them. The qubits are signals with octavely spaced frequencies, like the “A” notes on a piano. When they are combined (multiplied together), they form a signal with an exponentially larger number of frequencies, like the individual keys on a piano. A single signal can therefore represent a lot of information. The drawback is that you need a lot a bandwidth to do it.

How is quantum emulation different from quantum simulation? Are there advantages to emulation?

The words have different meanings to different people. The dictionary definition is an endeavor that seeks to “equal or exceed” someone (or something) else. I like that. The example I often use is the spring-mass problem in classical physics. You can simulate a mass bouncing on a spring by solving a differential equation on a digital computer. You can also emulate it using an electronic circuit with capacitors and inductors. The math is the same; the physics is totally different. Are there advantages to emulation? Well, are there advantages to a quartz crystal watch over a wind-up clock?

What will quantum emulation devices be particularly well suited for?

Since it’s an entirely classical system, we are not bound by the usual rules of quantum mechanics. So, for example, we can use “quantum parallelism” to evaluate a Boolean function over all, say, 256 single-byte inputs and then use filters to project onto the solution space, all using just a single function call. A classical algorithm would require up to 256 such calls, while a quantum computer (using Grover’s algorithm) would require 16. So, you can use these devices to do unstructured searches more efficiently. More importantly, these devices can be put on a single chip and run at room temperature with low size, weight, and power. There’s no need for exotic cryogenics, bulky vacuum chambers, or fancy vibrational isolation. Of course, this comes at the cost of scalability, but as few as 10 qubits would suffice to show a computational advantage.

What problems do you run into with software that’s a simulation of a quantum computer but running on a traditional computer?

Classical simulations will experience an exponential slow down as the number of simulated qubits increases. Memory quickly becomes an issue. A typical laptop can handle about 30 or so qubits, requiring about a Gigabyte of RAM. Going to 40 qubits requires about a Terabyte of RAM and a supercomputer. I think IBM holds the record of simulating 50 qubits (presumably to set the goal post back for Google, which is pursuing a 49-qubit device).

Do you think quantum computers will set the stage for breakthrough research in materials science?

Most of the great technological advances in human history have been through progress in material science (stone, bronze, iron, steel, plastic). People speak now of the Information Age, and this is usually taken to be synonymous with digital information. Modern computers are built on our ability to finely manipulate matter into complex forms (integrated circuits). We can now rearrange individual atoms to spell out company logos, so we’ve really come a long way from knapping flint stones. Quantum computing points to the next direction: exquisitely fine control of matter and energy. We’re making atoms do flips and somersaults like a little quantum flea circus! And with exquisite control comes a continuum, vice digital, representation of information. Post-Moore’s law computing will be about finesse, not brute force.

What is a myth about quantum computing that you would like to dispel?

A lot of people have tried to identify the “mojo” that makes quantum computing work. What we have endeavored to show in our work is that “uniquely quantum” concepts like superposition, interference, entanglement, and contextuality all have classical analogues. What makes quantum systems unique (we believe) is the ability to scale up to large numbers of qubits.

What are the challenges that could potentially derail the promise of near term quantum computing?

Scalability is the big unknown. We have no reason to believe that qubits cannot be scaled, but at the same time it’s not as simple as cramming more transistors onto a chip, à la Moore’s law. It’s largely unknown how to properly model errors in large quantum systems, and we simply do not know at this point how the difficulty will scale. The current efforts by Google and IBM to push into the 50-qubit realm will certainly be telling, but they will not be the final answer.

Was there anyone whose work inspired you to pursue a life in the sciences? Is there someone working in quantum today that inspires you?

One of my professors from the University of New Orleans, Charles Head, was a bit of a maverick and really inspired me to challenge conventional wisdom. He has forever corrupted me. My absolute quantum heroes today are a little-known couple from Mexico, Luis de la Peña and his wife, Anna María Cetto. Their work on emergent quantum physics is completely mind-blowing.

What are some of the most interesting projects you’ve seen others working on in quantum computing?

I’m very interested in the recent work on Coherent Ising Machines being performed by the Yamamoto groups in Stanford and Japan. It’s an optical approach to the sorts of optimization problems D-Wave has looked at. It’s not clear that what they’re doing actually gives a quantum advantage, but the physics is straightforward enough that one can model it and, to some extent, understand what’s going on. We hope to investigate this approach further and see if there’s any quantum physics that might have been “left on the floor,” so to speak, that we might leverage to better computational advantage.

What applications of quantum computing are you most excited for?

I’m excited for what quantum computing will teach us about quantum physics. We’re delving into the bones of quantum mechanics now, and what was armchair philosophy decades ago is now of the utmost practical concern. When is quantum cryptography truly secure? What are the fundamental differences between classical and quantum computing? I think our pursuit of quantum technology will lead to a deeper and more profound understanding of the physical world itself, and that’s bigger than any application. As I like to tell my students, this is a great time to be a quantum physicist!