19 citations · 64 across the 17 of their papers we have counts for
13 papers · 1 filter
Addressing Discrete Dynamic Optimization via a Logic-Based Discrete-Steepest Descent Algorithm
Zedong Peng, Albert Lee, David E. Bernal Neira
Dynamic optimization problems involving discrete decisions have several applications, yet lead to challenging optimization problems that must be addressed efficiently. Combining di…
A Convexification-based Outer-Approximation Method for Convex and Nonconvex MINLP
Zedong Peng, Kaiyu Cao, Kevin C. Furman +3
The advancement of domain reduction techniques has significantly enhanced the performance of solvers in mathematical programming. This paper delves into the impact of integrating c…
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…
Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study
Eleanor G. Rieffel, Ata Akbari Asanjan, M. Sohaib Alam +20
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum co…
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…
Measure This, Not That: Optimizing the Cost and Model-Based Information Content of Measurements
Jialu Wang, Zedong Peng, Ryan Hughes +3
Model-based design of experiments (MBDoE) is a powerful framework for selecting and calibrating science-based mathematical models from data. This work extends popular MBDoE workflo…
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Amandeep Singh Bhatia, David E. Bernal Neira
Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context,…
Logic-Based Discrete-Steepest Descent: A Solution Method for Process Synthesis Generalized Disjunctive Programs
Daniel Ovalle, David A. Liñán, Albert Lee +4
The optimization of chemical processes is challenging due to the nonlinearities arising from process physics and discrete design decisions. In particular, optimal synthesis and des…
A Greedy Quantum Route-Generation Algorithm
Jordan Makansi, David E. Bernal Neira
Routing and scheduling problems with time windows have long been important optimization problems for logistics and planning. Many classical heuristics and exact methods exist for s…
Classical and Quantum Distributed Algorithms for the Survivable Network Design Problem
Phillip Kerger, David E. Bernal Neira, Zoe Gonzalez Izquierdo +1
We investigate distributed classical and quantum approaches for the survivable network design problem (SNDP), sometimes called the generalized Steiner problem. These problems gener…
Surrogate optimization of variational quantum circuits
Erik J. Gustafson, Juha Tiihonen, Diana Chamaki +8
Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of qu…
Benchmarking Quantum Optimization for the Maximum-Cut Problem on a Superconducting Quantum Computer
Maxime Dupont, Bhuvanesh Sundar, Bram Evert +4
Achieving high-quality solutions faster than classical solvers on computationally hard problems is a challenge for quantum optimization to deliver utility. Using a superconducting…
Benchmarking the Operation of Quantum Heuristics and Ising Machines: Scoring Parameter Setting Strategies on Optimization Applications
David E. Bernal Neira, Robin Brown, Pratik Sathe +4
We discuss guidelines for evaluating the performance of parameterized stochastic solvers for optimization problems, with particular attention to systems that employ novel hardware,…