David Bernal Neira

About

David Bernal Neira is an Assistant Professor of Chemical Engineering at Purdue University, having joined the institution in August 2023. His research interests encompass optimization software and theory, quantum computing as solution methods to problems in combinatorial optimization and chemistry, and chemical and process systems engineering. His work focuses on the mathematical modeling and optimization of discrete nonlinear systems through novel algorithms, theory, and computational methods, with applications in process and energy systems engineering. Bernal Neira's academic background includes a PhD in Chemical Engineering from Carnegie Mellon University, a B.A.Sc. in Physics, a M.Sc., and a B.A.Sc. in Chemical Engineering from Universidad de Los Andes. His research group explores the intersection of optimization, quantum computing, and chemical engineering, contributing to advancements in computational methods for complex systems.

Research topics

• Computer Science
• Mathematical optimization
• Mathematics
• Computer engineering
• Algorithm
• Physics
• Distributed computing
• Statistical physics
• Business
• Quantum mechanics

Selected publications

• Optimization Applications as Quantum Performance Benchmarks

  arXiv (Cornell University) · 2023 · 7 citations

  Senior authorCorresponding

  Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA) can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems using gate-model quantum devices and…

  DOI

• Utilizing modern computer architectures to solve mathematical optimization problems: A survey

  Computers & Chemical Engineering · 2024 · 5 citations

  1st authorCorresponding
  PublisherDOI

• A Multilevel Approach for Solving Large-Scale QUBO Problems with Noisy Hybrid Quantum Approximate Optimization

  2024-09-23 · 5 citations

  article

  Quantum approximate optimization is one of the promising candidates for useful quantum computation, particularly in the context of finding approximate solutions to Quadratic Unconstrained Binary Optimization (QUBO) problems. However, the existing quantum processing units (QPUs) are of relatively small size, and canonical mappings of QUBO via the Ising model require one qubit per variable, rendering direct large-scale optimization infeasible. In classical optimization, a general strategy for addr…

  PublisherDOI

• Benchmarking the operation of quantum heuristics and Ising machines: scoring parameter setting strategies on optimization applications

  Quantum Machine Intelligence · 2025-09-05 · 3 citations

  articleOpen access1st authorCorresponding

  We discuss guidelines for evaluating the performance of parameterized stochastic solvers for optimization problems, with particular attention to systems that employ novel hardware, such as digital quantum processors running variational algorithms, analog processors performing quantum annealing, or coherent Ising machines. We illustrate through an example a benchmarking procedure grounded in the statistical analysis of the expectation of a given performance metric measured in a test environment.…

  PublisherOA PDFDOI

• Quantum Optimization Benchmarking Library - The Intractable Decathlon

  ArXiv.org · 2025-04-04 · 2 citations

  preprintOpen access

  Through recent progress in hardware development, quantum computers have advanced to the point where benchmarking of (heuristic) quantum algorithms at scale is within reach. Particularly in combinatorial optimization - where most algorithms are heuristics - it is key to empirically analyze their performance on hardware and track progress towards quantum advantage. To this extent, we present ten optimization problem classes that are difficult for existing classical algorithms and can (mostly) be l…

  PublisherOA PDFDOI

Frequent coauthors

• Davide Venturelli

  NASA Research Park

  20 shared

• Farshud Sorourifar

  The Ohio State University

  11 shared

• Ignacio E. Grossmann

  Carnegie Mellon University

  10 shared

• Zoe Gonzalez Izquierdo

  Research Institute for Advanced Computer Science

  10 shared

• Andres F. Cabeza

  Universidad Nacional de Colombia

  9 shared

• Diana Chamaki

  8 shared

• Phillip A. Kerger

  Johns Hopkins University

  7 shared

• Eleanor Rieffel

  Quantum Group (United States)

  7 shared

Labs

• David Bernal Neira - Davidson School of Chemical EngineeringPI

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