Daochen Wang
[last initial] [first name] at gmail dot com
I'm a thirdyear PhD student at QuICS,
Maryland,
where I research quantum information and computation. I'm fortunate to be coadvised by
Andrew Childs and
Carl Miller.
Name in Chinese: 王道辰.
GitHub ·
LinkedIn


Research
I'm interested in structures beneath quantum speedups, algorithm design, and realworld applications.
Works below listed in order of first appearance online (most recent to least recent). Research award: NSF QISENET.
*: equal contribution
^{†}: alphabetical order, following convention in
TCS


1. Quantum algorithms for reinforcement learning with a generative model
Daochen Wang,
Aarthi Sundaram,
Robin Kothari,
Ashish Kapoor,
Martin Roetteler
ICML 2021:
slides,
poster
[QISENET: slides]
We quantify the quantum speedups achievable for reinforcement learning
in terms of calls to a generative model of the underlying
Markov decision process.
arXiv version in preparation.


2. Quantum exploration algorithms for multiarmed bandits
Daochen Wang*,
Xuchen You*,
Tongyang Li,
Andrew M. Childs
AAAI 2021
[GMU: slides]
[QTML 2020: slides, talk]
[MSR: slides]
Identifying the best arm in a quantum
multiarmed bandit (I illustrate such an object on the left)
can be done quadratically faster by quantum computation.


3. Symmetries, graph properties, and quantum speedups
Shalev BenDavid,
Andrew M. Childs,
András Gilyén,
William Kretschmer,
Supartha Podder,
Daochen Wang^{†}
FOCS 2020:
short slides,
short talk,
long talk
[APS 2021: slides]
[QIP 2021: talk]
[MSR: slides]
[Property Testing Review]
We characterise how a problem's symmetries determine whether quantum computation can substantially
speed up its solution; it turns out graph symmetries play the key role.
Subsumes our earlier work.


4. Efficient quantum measurement of Pauli operators
in the presence of finite sampling error
Ophelia Crawford*,
Barnaby van Straaten*,
Daochen Wang*,
Thomas Parks,
Earl Campbell,
Stephen Brierley
Quantum 2021
[QTurn 2020]
[QCTIP 2020: talk]
The number of measurements needed to estimate the expectation value of an observable can be
reduced by a few orders of magnitude via simultaneous measurements.


5. Simulating quantum circuits by classical circuits
Daochen Wang
arXiv 2019 (under review)
[Poster]
I extract a notion of "psimulation" from
a breakthrough paper in 2018
and then construct explicit classical circuits that can psimulate any quantum circuit.


6. Variational quantum computation of excited states
Oscar Higgott,
Daochen Wang,
Stephen Brierley
Quantum 2019
[>100 citations]
[QCTIP 2019]
Penalising overlaps between quantum states
enables the
variational quantum eigensolver to compute excited states at little extra cost.


7. Accelerated variational quantum eigensolver
Daochen Wang,
Oscar Higgott,
Stephen Brierley
Physical Review Letters 2019
[Poster]
Given greater coherence times, the
variational quantum eigensolver can be made faster by making it
behave more like
quantum phase estimation.


8. Driving Rabi oscillations at the giant dipole resonance in xenon
Stefan Pabst,
Daochen Wang,
Robin Santra
Physical Review A 2015
Supershort yet superintense pulses of light can drive electrons up and down between standard bound states
of negative energy and a pseudobound state of positive energy.


OpenFermion: the electronic structure package for quantum computers
Jarrod R. McClean,
Kevin J. Sung, Ian D. Kivlichan, Yudong Cao, Chengyu Dai, E. Schuyler Fried,
Craig Gidney,
Brendan Gimby,
Pranav Gokhale,
Thomas Häner, Tarini Hardikar, Vojtěch Havlíček,
Oscar Higgott, Cupjin Huang,
Josh Izaac,
Zhang Jiang, Xinle Liu, Sam McArdle, Matthew Neeley,
Thomas O'Brien, Bryan O'Gorman, Isil Ozfidan,
Maxwell D. Radin,
Jhonathan Romero,
Nicholas Rubin,
Nicolas P. D. Sawaya, Kanav Setia, Sukin Sim, Damian S. Steiger, Mark Steudtner,
Qiming Sun, Wei Sun,
Daochen Wang, Fang Zhang, Ryan Babbush
Quantum Science and Technology 2020
[GitHub]
I contributed code that allows you to automatically retrieve molecular geometries from the
PubChem database  try: geometry_from_pubchem('water').


I have worked with great mentors at great companies during my PhD


I was teaching assistant for the following class

