I explore the limits of today’s quantum computers. My Research interests include quantum simulation of molecules & materials, learning & mitigating quantum noise, and dynamic quantum circuits for near-to-far-term quantum computations. I’m open to working with researchers of all experience levels, including those who are new to my field or to research in general. For a broad overview of my current lines of inquiry, see below.
I get to do what I do only because of a pantheon of teachers and mentors that have guided me through many hoops. I am interested in democratizing these jumps, including the chemistry olympiad, university, summer research programs and jobs, graduate school, scholarships, and fellowships. To this end, I’ve compiled some informal Resources. I’m also happy to share my application materials and tips.
I am recreationally interested in Metascience, or the science of science. Feel free to reach out to me to discuss related ideas, or anything else mentioned here.
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I’m interested in designing molecules and materials that unlock technological leaps. In particular, during my PhD, I used classical first-principles methods to probe complex physics at the intersection of quantum chemistry, quantum optics, and many-body physics. Results include design rules for the generation of entangled photons from quantum optical matter, engineered point defects in solid-state materials as a qubit platform, transduction of quantum information in nanomagnonic cavities and the classical origin of cavity-modified chemical reactivity. (For an overview of these past efforts, see my PhD dissertation.) Despite theoretical advances and access to powerful classical computational clusters, many flavors of practically relevant physics remain out of reach of existing classical methods.
Now, I am interested in exploring the potential of today’s quantum computers to answer classically hard, but quantumly easy questions in quantum simulation of molecules and materials. As a start, I have explored large-scale quantum computational studies of quantum many-body dynamics, quantum chromodynamics, topological excitations, and polariton chemistry. Moving forward, I am interested in the following questions:
The main challenge to quantum computers is a complex set of quantum noise processes that corrupt calculations. Learning faithful representations of quantum noise can reveal the underlying physical processes, thus allowing one to suppress, mitigate, or even correct the noise. Naively learning the full set of noise processes is challenging in a large-scale quantum device due to the exponential number of learned parameters. Furthermore, protocols that attempt to learn the noise are themselves affected by noise. We presented a method that enhances this learning process, even in the presence of imperfections in the learning process itself.
Once the noise is learned, quantum error mitigation strategies can harness imperfect quantum computers to yield near noise-free and meaningful results despite the presence of unmonitored errors. We have developed new strategies, such as a physics-based composite QEM workflow, a practical machine learning-based method that outperforms the physics-based composite QEM workflow, and a Bayesian inference-based one for measuring properties of quantum systems with shallow shadow tomography. Follow-on questions include:
Quantum systems present two distinct modes of evolution: deterministic unitary evolution, and stochastic evolution as the consequence of quantum measurements. To date, quantum computations predominantly use unitary evolution to generate complex quantum states for information processing and simulation. However, especially for calculations that do not require quantum effects, such as the addition of two bits, classical computations are likely to be faster and more reliable. Exploiting these trade-offs has resulted in the development of dynamic quantum circuits, namely quantum circuits that collect classical information from mid-circuit measurements, classically process the results, and feed-forward operations within a single shot of the circuit. A flagship application of dynamic circuits is active quantum error correction. But there exist applications of dynamic circuits in the near term. For instance, with dynamic circuits, we have demonstrated the generation of long-range entanglement, generalized measurements, and the quantum Fourier transform algorithmic primitive with record-breaking fidelity. In the future, I’m interested in the following:
Here are some questions on my mind, some of which are accompanied by reading lists (linked on final question marks) and light notes.