John Realpe-Gómez, Ph.D.

Associate Director

Senior Scientist

Laboratory for Research in Complex Systems


Polytechnic University of Turin, Italy 

(Ph.D., Physics with Riccardo Zecchina)

NASA, Quantum Artificial Intelligence Lab, USA: Research Scientist
University of Manchester, Complex Systems and Statistical Physics Group, UK: Research Associate
University of Cartagena, Physics Department, Colombia: Associate Professor
Polytechnic University of Turin, Microsoft Research Theory, Italy: PhD Student
Abdus Salam ICTP, Condensed Matter Physics: Pre-PhD Diploma Student
University of Valle, Physics Department, Colombia: MSc & BSc Student

What's your background?

I am an interdisciplinary physicist with experience in complex systems, quantum machine learning, and cognitive science.

What's your role at LRC?

I am using tools from complex systems and modern cognitive science to investigate biological complexity, life as information and how scientists can discover regularities we call "laws of nature"---a kind of reverse-engineering of science. I hope this can help us develop a more scientific and powerful approach to artificial intelligence and to tackle some big questions in science and society.

What trend, breakthrough or discovery are you most excited about?

I am delighted to see the growing interest in the combination of first- and third-person methods for the investigation of the mind and its physical correlate. I am convinced this can provide unprecedented insights to help us tackle some big questions in science, build superior technologies, and develop a less fearful and biased, more socially and environmentally conscious society.

Research Interests

I am currently focused on understanding the origin of biological complexity, life as information, and the potential synergies between biology, AI, and physics. I am using analytical and computational tools to investigate the origin of biological complexity in protein interaction networks, which essentially underpin nearly all biophysical and biochemical aspects of known life. I am also interested in using the tools of modern cognitive science to understand life as information and investigate "scientific intelligence" by modeling scientists as biological/cognitive systems learning about the environment they inhabit. 

Key Publications

Realpe-Gomez, J. (2020). Embodied observations from an intrinsic perspective can entail quantum dynamics. ArXiv preprint:

Realpe-Gómez J, et al. Balancing selfishness and norm conformity can explain human behavior in large-scale prisoner's dilemma games and can poise human groups near criticality. Phys. Rev. E. 2018 Apr;97(4-1):042321. 


M Benedetti, J Realpe-Gómez, et al. Quantum-assisted learning of hardware-embedded probabilistic graphical models. Phys. Rev. X 7 (4), 041052


Realpe-Gómez J, et al. Demographic noise and piecewise deterministic Markov processes.

Galla T, McKane AJ. Phys. Rev. E. 2012 Jul;86(1 Pt 1):011137. 


J. Realpe-Gómez, et al. Demographic noise and resilience in semi-arid ecosystems; Ecol. Complex. 15 97-108 (2013).


M. Benedetti, J. Realpe-Gómez, et al. Quantum-assisted Helmholtz machines: A hybrid quantum-classical deep learning framework for commercial datasets; Quantum Science and Technology 3, 034007 (2018).


As a Research Scientist at the NASA Quantum Artificial Intelligence Lab, John helped advance the field of quantum machine learning by contributing to successfully train, for the first time, a quantum annealing computer to generate, reconstruct, and classify images of hand-written digits. John earned a PhD in ICT-Physics at the Polytechnique University of Turin, Italy. His thesis work in Ricardo Zecchina's Microsoft Research theory group focused on the development of message-passing algorithms inspired on the physics of disordered systems for solving combinatorial socio-economic problems. Afterwards he worked as a posdoc with Alan McKane and Tobias Galla at the University of Manchester, combining tools of non-equilibrium statistical physics and agent based models to investigate socio-economic and ecological systems.