Derek S. Wang

Derek S. Wang

Research Scientist

IBM Quantum

Biography

I explore the limits of today’s quantum computers at IBM Quantum. For my PhD, I engineered quantum optical materials with applications in quantum computing and chemical reactivity. My methods of choice lie at the intersection of theoretical and computational quantum · (simulation, chemistry, optics).

I’m open to working with researchers of all experience levels, including those who are new to my field or to research in general. See my current interests below or Questions on my mind as a potential starting point for a discussion. To get a sense for what I’ve worked on in the past, see Publications.

I get to do what I do only because of a pantheon of mentors that have guided me through many hoops. Feel free to contact me about preparing and applying for the chemistry olympiad, college, summer programs (REUs) and jobs (Google, Apple, venture capital, Rigetti), graduate school, scholarships (Goldwater, Marshall), and fellowships (NSF GRFP). I’m happy to share tips and my application materials.

Interests
  • Quantum computing
  • Quantum error mitigation
  • Many-body physics
  • Polariton chemistry
  • Materials science
Education
  • PhD in Applied Physics, 2019-2022

    Harvard

  • MSc in Theoretical and Computational Chemistry, 2018-2019

    Oxford

  • BS/MS in Materials Science and Engineering, 2014-2017

    Stanford

Questions

What I’m more interested in lies closer to the top of the list. There is roughly an inverse correlation with my level of expertise.

  • What made some research labs, small or large, so successful, and can these same principles be applied to a quantum computing-specific lab?
  • What is the best way for training someone to ask and answer new & important questions? Similarly, can we formalize what it takes to have “good taste in research”, or good meta-decision-making abilities? Is there a correlation between choosing good research problems vs. being able to solve them?
  • Which best practices for conducting research should be formalized and taught to new researchers?
  • What would the equivalent of a Hippocratic oath but for scientists look like? (My take is here.) Should scientists push for one, and how would they advocate for its adoption amidst the forces of industry research labs, publishers, and academia?
  • Apparently, the impact of research is more incremental than before, and this can be linked to “a narrowing in the use of previous knowledge." How much new knowledge is produced per unit of research output, and how much previous knowledge is used to generate it, now vs. before? More generally, how can the sum total of knowledge be quantified, so as to quantify research progress?
  • What are examples of deep technologies where interest & investment (from the government, investors, and the public) came at the right time and in the right quantities?
  • What cultural differences in research are there, and how can these gaps be bridged?
  • At which length scales are physical phenomena hardest to probe?
  • Historically, how useful has it been in industry to simulate materials and molecules from first principles? Do we expect improved simulation capabilities to make a substantial difference? (Some light research on this topic here.)
  • On the path toward large-scale & fault-tolerant quantum computers, which quantum simulations can be executed to demonstrate checkpoints?
  • Is it possible to achieve a practical quantum advantage before fault tolerance?
  • Non-equilibrium quantum dynamics is often cited as the first field where we expect a quantum advantage. What is the path from learning something fundamental about non-equilibrium quantum dynamics to practical utility?
  • How can a machine learning model be used to discover new physical phenomena, other than serving as a null hypothesis?
  • Is there a simple way to quantify the overhead of simulating non-native many-body systems on an analogue quantum computer?
  • What is the most efficient way to encode mixed fermion-boson systems on a qubit-based quantum computer?
  • Is vibrational polariton chemistry a real & measurable effect, what is its origin, and can it be harnessed under practical scenarios?

Publications

When arXiv versions of my work are available, I list only them below. Note that formally published versions may contain slight differences.

(2024). Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows. arXiv:2402.17911.

arXiv

(2023). High-fidelity, multi-qubit generalized measurements with dynamic circuits. arXiv:2312.14087.

arXiv

(2023). First-Order Phase Transition of the Schwinger Model with a Quantum Computer. arXiv:2312.12831.

arXiv

(2023). Programmable Simulations of Molecules and Materials with Reconfigurable Quantum Processors. arXiv:2312.02265.

arXiv

(2023). Simulating polaritonic ground states on noisy quantum devices. arXiv:2310.02100.

arXiv

(2023). Machine learning for practical quantum error mitigation. arXiv:2309.17368.

arXiv

(2023). Efficient long-range entanglement using dynamic circuits. arXiv:2308.13065.

arXiv

(2023). Isolated Majorana mode in a quantum computer from a duality twist. arXiv:2308.02387.

arXiv

(2023). Uncovering Local Integrability in Quantum Many-Body Dynamics. arXiv:2307.05203.

arXiv

(2023). Best practices for quantum error mitigation with digital zero-noise extrapolation. arXiv:2307.05203.

arXiv

(2023). Braiding fractional quantum Hall quasiholes on a superconducting quantum processor. arXiv:2303.04806.

arXiv

(2022). Dissociation dynamics of a diatomic molecule in an optical cavity. arXiv:2210.00470.

arXiv

(2022). Chemical reactivity under collective vibrational strong coupling. arXiv:2206.08937.

arXiv

(2022). Cavity-modified unimolecular dissociation reactions via intramolecular vibrational energy redistribution. arXiv:2109.06631.

arXiv Code

(2022). Entangled photons from composite cascade emitters. arXiv:2110.13630.

arXiv Code

(2021). A Roadmap Toward the Theory of Vibrational Polariton Chemistry. arXiv:2107.09026.

arXiv

(2021). Quantum Interfaces to the Nanoscale. ACS Nano.

PDF Paper

(2021). Defect polaritons from first principles. arXiv:2105.02655.

arXiv

(2021). Sum-frequency excitation of coherent magnons. arXiv:2011.08730.

arXiv

(2021). Spin Emitters beyond the Point Dipole Approximation in Nanomagnonic Cavities. arXiv:2012.04662.

arXiv

(2021). Hybridized Defects in Solid-State Materials as Artificial Molecules. arXiv:2012.09187.

arXiv

(2021). Light-matter interaction of a molecule in a dissipative cavity from first principles. arXiv:2002.10461.

arXiv

(2020). Nanomagnonic Cavities for Strong Spin-Magnon Coupling and Magnon-Mediated Spin-Spin Interactions. arXiv:2007.11595.

arXiv

(2020). Dipole-coupled emitters as deterministic entangled photon-pair sources. arXiv:2004.13725.

arXiv

(2020). Passive controlled-variable phase gate on photonic qubits via a cascade emitter. arXiv:2011.09302.

arXiv

(2020). From Science Student to Scientist. ChemRXiv:13050242.

ChemRXiv

(2020). A Brief Guide to Patents for Academic Scientists. engRxiv:u8hya.

engRXiv

(2019). 4-Hydroxybutyrate Promotes Endogenous Antimicrobial Peptide Expression in Macrophages. Tissue Engineering A.

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(2018). The Effect of Mechanical Loading Upon Extracellular Matrix Bioscaffold-Mediated Skeletal Muscle Remodeling. Tissue Engineering A.

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(2017). Scalable “Dip-and-Dry” Fabrication of a Wide-Angle Plasmonic Selective Absorber for High-Efficiency Solar–Thermal Energy Conversion. Advanced Materials.

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(2017). Upconverting Nanoparticles as Optical Sensors of Nano- to Micro- Newton Forces. Nano Letters.

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(2017). Direct Intracellular Delivery of Cell-Impermeable Probes of Protein Glycosylation by Using Nanostraws. ChemBioChem.

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(2016). Solubilized extracellular matrix bioscaffolds derived from diverse source tissues differentially influence macrophage phenotype. Journal of Biomedical Materials.

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(2016). Temporally resolved direct delivery of second messengers into cells using nanostraws. Lab on a Chip.

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