Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function evaluations on quantum computers. We propose a new approach to VQEs using the principles of measurement-based quantum computation. This strategy uses entangled resource states and local measurements. We present two measurement-based VQE schemes. The first introduces a new approach for constructing variational families. The second provides a translation of circuit- to measurement-based schemes. Both schemes offer problem-specific advantages in terms of the required resources and coherence times
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function ...
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al. (2014), has received...
Library to implement the Variational Quantum Eigensolver (VQE) using hardware-efficient entangled me...
Applications such as simulating complicated quantum systems or solving large-scale linear algebra pr...
Variational algorithms for strongly correlated chemical and materials systems are one of the most pr...
Variational algorithms have received significant attention in recent years due to their potential to...
The variational quantum eigensolver (VQE) is a hybrid quantum classical algorithm designed for curre...
Variational algorithms have received significant attention in recent years due to their potential to...
Despite the raw computational power of classical computers, some problems require an exponential amo...
As quantum computers are developing, they are beginning to become useful for practical applications,...
12 pags., 10 figs., 1 tab.Quantum variational optimization has been posed as an alternative to solve...
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the g...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function ...
The variational quantum eigensolver (or VQE), first developed by Peruzzo et al. (2014), has received...
Library to implement the Variational Quantum Eigensolver (VQE) using hardware-efficient entangled me...
Applications such as simulating complicated quantum systems or solving large-scale linear algebra pr...
Variational algorithms for strongly correlated chemical and materials systems are one of the most pr...
Variational algorithms have received significant attention in recent years due to their potential to...
The variational quantum eigensolver (VQE) is a hybrid quantum classical algorithm designed for curre...
Variational algorithms have received significant attention in recent years due to their potential to...
Despite the raw computational power of classical computers, some problems require an exponential amo...
As quantum computers are developing, they are beginning to become useful for practical applications,...
12 pags., 10 figs., 1 tab.Quantum variational optimization has been posed as an alternative to solve...
The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm used to find the g...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...
The primary subject of this dissertation is the analysis and improvement of variational methods that...