2 citations · 4 across the 5 of their papers we have counts for
5 papers
Towards Efficient Quantum Computation of Molecular Ground State Energies using Bayesian Optimization with Priors over Surface Topology
Farshud Sorourifar, Mohamed Taha Rouabah, Nacer Eddine Belaloui +6
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classic…
Bayesian Optimization Priors for Efficient Variational Quantum Algorithms
Farshud Sorourifar, Diana Chamaki, Norm M. Tubman +2
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with V…
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Joel A. Paulson, Calvin Tsay
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engine…
Accelerating Black-Box Molecular Property Optimization by Adaptively Learning Sparse Subspaces
Farshud Sorourifar, Thomas Banker, Joel A. Paulson
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive s…
Multi-agent Black-box Optimization using a Bayesian Approach to Alternating Direction Method of Multipliers
Dinesh Krishnamoorthy, Joel A. Paulson
Bayesian optimization (BO) is a powerful black-box optimization framework that looks to efficiently learn the global optimum of an unknown system by systematically trading-off betw…