arXiv subjects

John Realpe-Gómez

Publications and source records attributed to John Realpe-Gómez.

10 recordsLinked to original sources

arXiv2024

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

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 humans somehow participate in what we perceive. So, how can scientists escape the action-perception loop to obtain an observer-independent description of the world? Here we present a set of conjectures informed by the philosophy of mind and a reverse-engineering of science and quantum physics to explore this question. We argue that embodiment, as traditionally understood, can manifest aspects of imaginary-time quantum dynamics. We then explore what additional constraints are required to obtain aspects of genuine, real-time quantum dynamics. In particular, we conjecture that an embodied scientist doing experiments must be described from the perspective of another scientist, which is ignored in traditional approaches to embodied cognition, and that observers play complementary roles as both objects experienced by other observers and ``subjects'' that experience other objects.

physics.hist-ph↗

arXiv2019

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

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 naturally in psychology where both the objects of perception and the perceiving subject belong to 'our mental content'. Here we argue that the formalism of quantum theory can be derived from two related intuitive principles: (i) inference is a physical process performed by physical systems, observers, which are part of the experimental setup---this implies non-commutativity and imaginary-time quantum mechanics; (ii) experiments must be described from a first-person perspective---this leads to self-reference, complementarity, and real-time quantum dynamics. This approach sheds new light on the foundations of quantum theory and suggests fundamental equations in physics are typically of second order due to the physical nature of the observer. It also suggests some experimental conjectures: (i) the quantum of action could be understood as the result of the additional energy required to transition from unconscious to conscious perception; (ii) humans can observe a single photon of visible light; (iii) self-aware systems and the neural correlates of the self should be composed of two complementary sub-systems, much like the DNA molecule is composed of two strands---this may help explain the double-hemisphere architecture of the brain. Moreover, this approach may help bridge the gap between science and human experience. We discuss the potential implications of these ideas for the modern research programs on consciousness and contemplative science. As side results: (i) we show that message-passing algorithms and stochastic processes can be written in a quantum-like manner; (ii) we provide evidence that non-stoquasticity, a quantum computational resource, may be related to non-equilibrium phenomena.

quant-ph↗

arXiv2019

"Negative probabilities" as relative probabilistic expressions

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 of negative numbers in arithmetical expressions, the use of negative probabilities can have substantial practical value. Indeed, some of the ideas discussed here have led to stochastic simulation algorithms that require much less memory than the best classical algorithms known to date [arXiv:1906.00263].

cond-mat.stat-mech↗

arXiv2019

Quantum-inspired memory-enhanced stochastic algorithms

Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory requirements for simulating stochastic models. Here we show that some of these recent quantum memory-enhanced algorithms can be either implemented or approximated classically. In other words, we show that it is possible to develop quantum-inspired classical algorithms that require much less memory than the best classical algorithms known to date. Being classical, such algorithms could be implemented in state-of-the-art high-performance computers, which could potentially enhance the study of large-scale complex systems. Furthermore, since memory is the main bottleneck limiting the performance of classical supercomputers in one of the most promising avenues to demonstrate quantum 'supremacy', we expect adaptations of these ideas may potentially further raise the bar for near-term quantum computers to reach such a milestone.

quant-ph↗

arXiv2018

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising "killer" applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices to demonstrate quantum enhancement in the near future. In this contribution to the focus collection on "What would you do with 1000 qubits?", we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques. We also highlight the case of classical datasets with potential quantum-like statistical correlations where quantum models could be more suitable. We focus on hybrid quantum-classical approaches and illustrate some of the key challenges we foresee for near-term implementations. Finally, we introduce the quantum-assisted Helmholtz machine (QAHM), an attempt to use near-term quantum devices to tackle high-dimensional datasets of continuous variables. Instead of using quantum computers to assist deep learning, as previous approaches do, the QAHM uses deep learning to extract a low-dimensional binary representation of data, suitable for relatively small quantum processors which can assist the training of an unsupervised generative model. Although we illustrate this concept on a quantum annealer, other quantum platforms could benefit as well from this hybrid quantum-classical framework.

quant-ph↗

arXiv2018

Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices

Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the \textit{quantum-assisted Helmholtz machine:} a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16x16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.

quant-ph↗

arXiv2018

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

quant-ph↗

arXiv2018

Balancing selfishness and norm conformity can explain human behavior in large-scale Prisoner's Dilemma games and can poise human groups near criticality

Cooperation is central to the success of human societies as it is crucial for overcoming some of the most pressing social challenges of our time. Yet how human cooperation is achieved and may persist is still a main puzzle in the social and biological sciences. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking. Using statistical physics techniques and integrating findings from cognitive and behavioral sciences, we present an analytically-tractable model in which individuals base their decisions to cooperate both on the economic rewards they obtain and on the degree to which their action comply with social norms. Results from this parsimonious model are in agreement with what has been observed in recent large-scale experiments with humans. We also find the phase diagram of the model and show that the experimental human group is poised near a critical point, a regime where recent work suggests living systems respond to changing external conditions in an efficient and coordinated manner.

physics.soc-ph↗

arXiv2017

A NASA Perspective on Quantum Computing: Opportunities and Challenges

In the last couple of decades, the world has seen several stunning instances of quantum algorithms that provably outperform the best classical algorithms. For most problems, however, it is currently unknown whether quantum algorithms can provide an advantage, and if so by how much, or how to design quantum algorithms that realize such advantages. Many of the most challenging computational problems arising in the practical world are tackled today by heuristic algorithms that have not been mathematically proven to outperform other approaches but have been shown to be effective empirically. While quantum heuristic algorithms have been proposed, empirical testing becomes possible only as quantum computation hardware is built. The next few years will be exciting as empirical testing of quantum heuristic algorithms becomes more and more feasible. While large-scale universal quantum computers are likely decades away, special-purpose quantum computational hardware has begun to emerge that will become more powerful over time, as well as some small-scale universal quantum computers.

quant-ph↗

arXiv2016

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

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 annealers have been proposed as a potential candidate to speed up this task, but several limitations still bar these state-of-the-art technologies from being used effectively. One of the main limitations is that, while the device may indeed sample from a Boltzmann-like distribution, quantum dynamical arguments suggest it will do so with an {\it instance-dependent} effective temperature, different from its physical temperature. Unless this unknown temperature can be unveiled, it might not be possible to effectively use a quantum annealer for Boltzmann sampling. In this work, we propose a strategy to overcome this challenge with a simple effective-temperature estimation algorithm. We provide a systematic study assessing the impact of the effective temperatures in the learning of a special class of a restricted Boltzmann machine embedded on quantum hardware, which can serve as a building block for deep-learning architectures. We also provide a comparison to $k$-step contrastive divergence (CD-$k$) with $k$ up to 100. Although assuming a suitable fixed effective temperature also allows us to outperform one step contrastive divergence (CD-1), only when using an instance-dependent effective temperature do we find a performance close to that of CD-100 for the case studied here.

quant-ph↗