Unpil Baek is a Ph.D. student in physics at the University of California, Berkeley, working in Birgitta Whaley’s group. He is interested in simulating strongly-correlated electrons with near- and intermediate-term quantum computers. He is also interested in combining classical machine learning techniques with variational quantum-classical algorithms to improve accuracy and reduce resource costs.
Past research
Unpil worked briefly on superconducting qubit experiments in Quantum Nanoelectronics Lab led by Irfan Siddiqi. He developed a custom FPGA-based hardware for superconducting qubit control with Dr. Gang Huang from Lawrence Berkeley National Laboratory. He also designed and built several 80/20 frames for the new dilution refrigerators in the lab.
For his undergraduate senior thesis, Unpil worked with Robert Littlejohn on the presymplectic reduction of general relativity under the local Lorentz invariance.
Ph.D. in Physics, 2017-present
University of California, Berkeley
M.A. in Physics, 2018
University of California, Berkeley
B.A. in Physics and Applied Mathematics, 2017
University of California, Berkeley
Korean, Japanese, Mandarin
Basic Spanish, German
Responsibilities include:
Variational algorithms for strongly correlated chemical and materials systems are one of the most promising applications of near-term quantum computers. We present an extension to the variational quantum eigensolver that approximates the ground state of a system by solving a generalized eigenvalue problem in a subspace spanned by a collection of parametrized quantum states. This allows for the systematic improvement of a logical wavefunction ansatz without a significant increase in circuit complexity. To minimize the circuit complexity of this approach, we propose a strategy for efficiently measuring the Hamiltonian and overlap matrix elements between states parametrized by circuits that commute with the total particle number operator. This strategy doubles the size of the state preparation circuits but not their depth, while adding a small number of additional two-qubit gates relative to standard variational quantum eigensolver. We also propose a classical Monte Carlo scheme to estimate the uncertainty in the ground state energy caused by a finite number of measurements of the matrix elements. We explain how this Monte Carlo procedure can be extended to adaptively schedule the required measurements, reducing the number of circuit executions necessary for a given accuracy. We apply these ideas to two model strongly correlated systems, a square configuration of H4 and the π-system of hexatriene (C6H8).
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