Alejandro Perdomo-Ortiz joined NASA Ames Research Center in 2012 as a research scientist, where his latest research involves the design of quantum algorithms to solve hard optimization problems and machine learning applications. In addition to his main focus on using quantum technologies to assist in solving computationally intractable problems, he has also worked on the quantum engineering of molecular electronics devices, biomolecular sensors, and the design of nanostructures capable of directing excitonic energy transfer in light-harvesting antenna arrays. He has also worked in the theoretical description of the emerging field of nonlinear ultrafast fluorescence spectroscopy.

Perdomo-Ortiz received a master's degree in chemistry and a Ph.D. in chemical physics from Harvard University. Before joining Harvard, he held research assistant positions at Clemson University, the University of Florida, and the Georgia Institute of Technology. He is a three-time winner of Harvard's Certificate of Excellence in Teaching and the Dudley R. Herschbach Teaching Award for teaching quantum mechanics to undergraduate students. While at NASA, he was the recipient of the 2014 Staff Appreciation and Recognition (STAR) Award from the University of California, and the recipient of the 2016 Ames Honor Award for the category of Contractor Employee. He is originally from Cali, Colombia where he completed undergraduate studies in chemistry at Universidad del Valle.

Since October 2012 and building on his five years of prior experience programming the D-Wave quantum computer (2007-2012), Alejandro has worked in the implementation of several applications of interest to NASA's Quantum Artificial Intelligence Laboratory (QuAIL). He also developed a tuning strategy for quantum computing devices that achieved up to two orders of magnitude enhancement in the performance of the quantum algorithms for certain problem instances. Over the past nine years, he has worked on several foundational methods to implement robust quantum annealing algorithms; from physics-based approaches towards a better fundamental understanding of the device, to the validation and verification of these quantum technologies, and in the code development and implementation of several applications across a broad spectrum of application domains, with quantum machine learning and fault diagnosis being his current foci.

Perdomo-Ortiz is fascinated with the unique opportunities in the nanoscale world and their quantum mechanical properties. Within the NASA team, he is interested in understanding the scalability and performance of quantum computers and their realistic experimental implementations for broad applications in space exploration research.

- A. Perdomo-Ortiz, M. Benedetti, J. Realpe-Gomez, and R. Biswas, Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers. arXiv:1708.09757 (2017)
- A. Perdomo-Ortiz, A. Feldman, A. Ozaeta, S. V. Isakov, Z. Zhu, B. O'Gorman, H. G. Katzgraber, A. Diedrich, H. Neven, J. de Kleer, B. Lackey, and R. Biswas, On the readiness of quantum optimization machines for industrial applications. arXiv:1708.09780 (2017)
- M. Benedetti, J. Realpe-Gomez and A. Perdomo-Ortiz, Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices. arXiv:1708.09784 (2017)
- M. Benedetti, J. Realpe-Gomez, R. Biswas, and A. Perdomo-Ortiz, Quantum-assisted learning of graphical models with arbitrary pairwise connectivity. arXiv:1609.02542 (2017)
- V. N. Smelyanskiy, D. Venturelli, A. Perdomo-Ortiz, S. Knysh, M. I.Dykman, Quantum annealing via environment-mediated quantum diffusion, Phys. Rev. Lett. 118, 066802 (2017)
- S. MandrĂ , Z. Zhu, W. Wang, A. Perdomo-Ortiz, and H. Katzgraber, Strengths and Weaknesses of Weak-Strong Cluster Problems: A Detailed Overview of State-of-the-art Classical Heuristics vs Quantum Approaches, Phys. Rev. A 94, 022337 (2016)
- M. Benedetti, J. Realpe-Gomez, R. Biswas, and A. Perdomo-Ortiz, Estimation of effective temperatures in quantum annealers for sampling applications: A case study towards deep learning, Phys. Rev. A 94, 022308 (2016)
- A. Perdomo-Ortiz, J. Fluegemann, R. Biswas, V. N. Smelyanskiy, A Performance Estimator for Quantum Annealers: Gauge selection and Parameter Setting. arXiv:1503.01083
- A. Perdomo-Ortiz, B. O'Gorman, J. Fluegemann, R. Biswas, V. N. Smelyanskiy, Determination and correction of persistent biases in quantum annealers, Sci. Rep. 6, 18628 (2016).
- A. Perdomo-Ortiz, J. Fluegemann, S. Narasimhan, R. Biswas, V. N.Smelyanskiy, A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems, Eur.Phys. J. Spec. Topics. 224, 131-148 (2015)
- B. O'Gorman, A. Perdomo-Ortiz, R. Babbush, A. Aspuru-Guzik, V. N. Smelyanskiy, Bayesian Network Structure Learning Using Quantum Annealing, Eur. Phys. J. Spec. Topics. 224, 163-188 (2015).
- A. Perdomo-Ortiz, N. Dickson, M. Drew-Brook, G. Rose, and A. Aspuru-Guzik, Finding low-energy conformations of lattice protein models by quantum annealing.
- G. A. Lott, A. Perdomo-Ortiz, J. K. Utterback, J. R. Widom, A. Aspuru-Guzik, and A. H. Marcus, Conformation of Self-Assembled Porphyrin Dimers in Liposome Vesicles by Phase-Modulation 2D Fluorescence Spectroscopy, Proc. Natl. Acad. Sci., 108, 16521-16526 (2011).
- A. Perdomo-Ortiz, S. Venegas-Andraca, and A. Aspuru-Guzik, A Study of Heuristic Guesses for Adiabatic Quantum Computation, Quantum. Inf. Process., 10, 33 (2011).
- I. Kassal, J. D. Whitfield, A. Perdomo-Ortiz, M.-H. Yung, A. Aspuru-Guzik, Simulating chemistry using quantum computers, Annu. Rev. Phys. Chem. 2011. 62:185-207 (2011).
- J. C. Arce, A. Perdomo-Ortiz, M. L. Zambrano, and C. A. Mujica-Martinez, Envelope Molecular-Orbital Theory of Extended Systems I. Electronic States of Organic Quasi-Linear Nano-Heterostructures, J. Chem. Phys., 134, 104103 (2011).
- A. Perdomo, L.Vogt, A. Najmaie, and A. Aspuru-Guzik, Engineering Directed excitonic Energy Transfer, Appl. Phys. Lett., 96, 093114 (2010).

Alejandro Perdomo-Ortiz

Research Scientist, NASA Ames Research Center

alejandro.perdomoortiz

@nasa.gov