Aaron Young is a researcher at the University of Colorado in the Kaufman group where he looks “under the hood” of quantum physics aiming to understand how nature can be exploited to build technological innovation the Quantum Computing space.
QZ: How did your interest in Quantum Computing start?
I was fortunate enough to be a mechanical engineering student at Caltech when two exciting things happened. First, in celebration of the 50th anniversary of Richard Feynman’s Nobel prize, several leading researchers in quantum information science gave talks that were geared towards a public audience. This introduced me to some of the work that was happening in the field. Second, LIGO announced the first detection of gravitational waves. While I had no context at the time for understanding the physics of gravitational waves, I was interested in how a large mechanical object could possibly measure the displacements LIGO claimed to measure – less than one ten-thousandth of the diameter of a proton. This led me down a deep rabbit hole, ultimately guiding me to the whole field of quantum optomechanics, which was largely developed to address the needs of LIGO. I was surprised and excited to find that this field, including researchers at Caltech, had since branched off and found applications in quantum information science. Despite my total lack of background knowledge, Professor Oskar Painter graciously allowed me to join his research group, where I completed a thesis project which attempted to store the state of a superconducting qubit in the long-lived phononic modes of a mechanical oscillator.
QZ: Please tell us about your work and what you are aiming to achieve with your research.
We combine tools developed in the field of optical clocks over the last few decades with more recent work involving optical tweezers and quantum gas microscopy. The former allows for control over extremely long-lived internal degrees of freedom of large ensembles of atoms, and the latter provides access to controls and observables at the single-atom level. The result is an experimental platform that lets us rapidly prepare large ensembles of atomic qubits, where we are able to accurately control and read out the state of each individual atom and, eventually, also control the interactions between those atoms. With these tools we hope to engineer entangled states that are useful for metrology, or interesting Hamiltonians in the context of quantum simulation.
QZ: Give everyone a single human readable sentence to describe your work.
We use lasers to build crystals a single atom at a time, and control those atoms to both perform extremely precise measurements of time, and to simulate interesting quantum behaviors.
QZ: You are currently pursuing your PhD, what do you want to do afterwards? Academia? Start-up or join a company?
I am certain that I want to continue doing research, but the field is moving so quickly it is not entirely clear what the best opportunities will be when I graduate. There is currently a lot of industry and privately funded research happening, and if that proves to be sustainable could be an exciting direction.
QZ: What is your favourite Quantum Toolset? (i.e. programming language, toolset, framework)
I have been using QuTiP (Quantum Toolbox in Python) and ARC (Alkali.ne Rydberg Calculator) a fair bit recently, and both are great for quickly coming up with predictions and models for what we expect to see experimentally. That being said, for the purposes of learning there is no real substitute for just writing things from scratch. For that, Mathematica is particularly good for quick and dirty calculations where you just want to think about math and physics, and not algorithms or computer science. There’s of course a whole zoo of specialized tools for what I would classify as the engineering that goes into building an experiment – including physics engines like COMSOL and Zemax – but those tools can vary wildly depending on the details of the experiment.
QZ: What aspect of Quantum has surprised you the most?
There are a few things I have encountered in physics that, at first glance, genuinely seem like they shouldn’t work. One good example is quantum error correction – if you aren’t allowed to directly measure what state you’re in, it really seems like the prospects for correcting an error in that state are hopeless. Another example that bothered me a fair bit when I was first learning this stuff is that composite particles can change their statistics. Things like Cooper pairs, or even normal atoms, are nominally made of fermions, but can behave like bosons.
QZ: Where do you see your research work having its biggest impact?
In the near term, I think there are measurements we can do that can inform the design of next-generation optical clocks. These clocks provide some of the most precise measurements we can make of any physical quantity, so if you can map a quantity you’re interested in onto a measurement of time, there’s a good chance that these clocks will also be the best probes of that quantity. For example, people are interested in, and in some cases already doing, geodesy, searches for dark matter, and gravitational wave astronomy, all with optical clocks.
Our experiment can provide access to controls and observables with single-atom resolution in a system that is otherwise similar in many ways to state of the art optical lattice clocks. As a result there are many measurements we can do that could shed light on the effects that currently limit the best optical clocks, and potentially provide a path towards mitigating those effects.
In the longer term, this platform should be a good testbed for simulating interesting quantum many body physics, and potentially even performing circuit-based quantum computing at a small to intermediate scale. It is entirely unclear, at least to me, what the dominant platform for large-scale quantum computing will be, or if such a platform can even exist. That being said, I think the bottom-up approach we follow for studying large quantum systems will continue to be a fruitful one. There are really two competing requirements when developing quantum technologies. For a practical device, solid-state technologies are vastly preferable due to ease of manufacturing and scalability. However, part of the allure of studying quantum many body systems is that they are tremendously complicated, and so any additional complications or noise associated with being in a solid-state system can quickly add up and obscure the underlying physics. Studying these solid-state systems as nature provides them is an interesting endeavor in its own right, however, we follow an alternative approach: by starting with an extremely well-understood system, namely individual atoms which have been exhaustively and painstakingly characterized for use in atomic clocks, and assembling a many-body system from only those well-known constituents, there is a much clearer link between any interesting emergent physics that we are able to see and the underlying mechanisms that give rise to that behavior.
QZ: What is the most challenging part about working in the Quantum Computing domain?
The quick pitch for quantum computing is that since quantum many body systems get complicated very quickly, it may be possible to harness these systems to perform complicated calculations. This is a double-edged sword, since to get a foothold to take advantage of these systems you need to make simplifying arguments without leaving yourself with a system that’s uninteresting. As a result the biggest challenge is not to build large complicated quantum systems, those are all around us, but to build systems that are just complicated enough, and compatible with clever simplifying arguments. In the domain of quantum computing, this means coming up with algorithms that arrange for things like interference between many paths. In the domain of quantum simulation and quantum many body physics, this often means looking for symmetries and simplifications that allow you to reframe the system in terms of quasiparticles that obey a different, more straightforward set of rules than the underlying system itself.
QZ: When do you see Quantum Computing as getting mass traction and mainstream appeal?
It seems like it’s garnering mainstream appeal now. As a whole I think this is great, as long as the excitement doesn’t die out. There are plenty of fields in physics that are entirely scientifically sound, but experience a boom and collapse when expectations outrun scientific progress. That being said, I think it’s very unlikely that interest in quantum computing could ever die out completely (unless something extremely dramatic happens, like someone proving P=NP or something like that).
While I suspect that we are still very far away from quantum computing gaining widespread use outside of the domain of physics research, there are many near-term applications of quantum technologies as a whole, particularly in the realm of sensing and communication.
QZ: How can people learn and get into Quantum Computing?
Given that quantum computing is still very much the domain of academic or industry research labs, I think a formal education in physics, math, or electrical engineering is still the most direct route to getting involved. For physics students early in their career, I want to stress that you really can’t go wrong just by asking around and seeing what relevant research is happening at your school. That being said (and as someone who started college as a film major), I certainly recognize that other routes are available. One of the few nice things to come out of the lockdown is that there is an ever-increasing wealth of resources online, including things like the MIT open courseware. For a broader overview of the state of current research (particularly in atomic physics) the Virtual AMO Seminar (VAMOS) and Quantum Science Seminar (QSS) both host fantastic weekly talks from leading researchers in the field that can be viewed for free and online.
QZ: What career advice can you give about getting into the QC space.
I often joke that if quantum computing takes off and results in a second computing revolution, which it may well do, then anyone even remotely interested in quantum technologies will be able to get a lucrative job. Since that outcome requires no planning, the case to plan for is if that doesn’t happen. However, even in the latter case, there are exciting applications of quantum technologies right now, including things like quantum encryption, communication, simulation, and sensing. I would strongly encourage people to explore these technologies in addition to quantum computing. If the pessimist in me is right, then these subfields will still yield useful technologies, and if the pessimist in me is wrong, then any techniques and technologies developed in these subfields will be immediately transferrable, and it will be an extremely exciting time for all of us.
A big thank you to Aaron who agreed to the interview with us. We really enjoyed learning about the work going in your lab and love the “James Bond Q” style equipment, especially the surreal green glow.