activity
20152021
most citedEstimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

217 citations · 333 across the 6 of their papers we have counts for

collaborators

6 papers

physics.hist-ph2021

How can scientists establish an observer-independent science? Embodied cognition, consciousness and quantum mechanics

John Realpe-Gómez

Evidence is growing for the theory of embodied cognition, which posits that action and perception co-determine each other, forming an action-perception loop. This suggests that we…

cond-mat.stat-mech2019★ 1 cited

"Negative probabilities" as relative probabilistic expressions

John Realpe-Gómez

Here we briefly discuss how negative numbers, or "negative probabilities", can naturally arise in probabilistic expressions and be given an operational interpretation. Like the use…

quant-ph2019

Quantum-inspired memory-enhanced stochastic algorithms

John Realpe-Gómez, Nathan Killoran

Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory…

quant-ph2017★ 4 cited

Modeling observers as physical systems representing the world from within: Quantum theory as a physical and self-referential theory of inference

John Realpe-Gómez

In 1929 Szilard pointed out that the physics of the observer may play a role in the analysis of experiments. The same year, Bohr pointed out that complementarity appears to arise n…

quant-ph2016★ 111 cited

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Marcello Benedetti, John Realpe-Gómez, Rupak Biswas +1

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is i…

quant-ph2015★ 217 cited

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Marcello Benedetti, John Realpe-Gómez, Rupak Biswas +1

An increase in the efficiency of sampling from Boltzmann distributions would have a significant impact on deep learning and other machine-learning applications. Recently, quantum a…