10 papers
Quantum thermodynamics and semidefinite optimization: Boltzmann, Fermi-Dirac, and Bose-Einstein frameworks
Michele Minervini, Nana Liu, Dhrumil Patel +1
Here we argue how quantum thermodynamics offers a unifying interpretation for a wide class of semidefinite programs (SDPs) that arise in quantum information. Three SDP variable con…
Improved sample complexity bound for sample-based Lindbladian simulation
Siheon Park, Youngjin Seo, Byeongseon Go +3
We establish improved sample-complexity bounds for sample-based Lindbladian simulation based on the Wave Matrix Lindbladization (WML) algorithm. For a jump operator with dimens…
Dual-VQE: A quantum algorithm to lower bound the ground-state energy
Hanna Westerheim, Jingxuan Chen, Zoë Holmes +6
The variational quantum eigensolver (VQE) is a hybrid quantum-classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As…
Evolved Quantum Boltzmann Machines
Michele Minervini, Dhrumil Patel, Mark M. Wilde
We introduce evolved quantum Boltzmann machines as a variational ansatz for quantum optimization and learning tasks. Given two parameterized Hamiltonians and , an ev…
Digital Quantum Simulations of the Non-Resonant Open Tavis-Cummings Model
Aidan N. Sims, Dhrumil Patel, Aby Philip +4
The open Tavis--Cummings model consists of quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum i…
Quantum Boltzmann machine learning of ground-state energies
Dhrumil Patel, Daniel Koch, Saahil Patel +1
Estimating the ground-state energy of Hamiltonians is a fundamental task for which it is believed that quantum computers can be helpful. Several approaches have been proposed towar…