5 papers
Convergence of kernel and neural-network methods for Hamilton--Jacobi--Bellman equations on unbounded domains
Yumiharu Nakano
We derive error bounds from empirical squared residuals for two non-monotone, mesh-free approximations of Hamilton--Jacobi--Bellman equations on unbounded spatial domains: collocat…
Fermionic-Adapted Shadow Tomography for dynamical correlation functions
Taehee Ko, Mancheon Han, Hyowon Park +1
Dynamical correlation functions are essential for characterizing the response of the quantum many-body systems to the external perturbation. As their calculation is classically int…
Classical optimization algorithms for diagonalizing quantum Hamiltonians
Taehee Ko, Sangkook Choi, Hyowon Park +1
Diagonalizing a Hamiltonian, which is essential for simulating its long-time dynamics, is a key primitive in quantum computing and has been proven to yield a quantum advantage for…
Interpolation-based coordinate descent method for parameterized quantum circuits
Zhijian Lai, Jiang Hu, Taehee Ko +2
Parameterized quantum circuits (PQCs) are ubiquitous in the design of hybrid quantum-classical algorithms. In this work, we propose an interpolation-based coordinate descent (ICD)…
Quantum random power method for ground state computation
Taehee Ko, Hyowon Park, Sangkook Choi
We present a quantum-classical hybrid random power method that approximates a ground state of a Hamiltonian. The quantum part of our method computes a fixed number of elements of a…