The Computational Lens: from Quantum Physics to Neuroscience
- URL: http://arxiv.org/abs/2310.20539v1
- Date: Tue, 31 Oct 2023 15:21:22 GMT
- Title: The Computational Lens: from Quantum Physics to Neuroscience
- Authors: Chi-Ning Chou
- Abstract summary: Two transformative waves of computing have redefined the way we approach science.
I will present the computational lens in science, aiming at the conceptual level.
Specifically, the central thesis posits that computation serves as a convenient and mechanistic language for understanding and analyzing information processing systems.
- Score: 0.92463347238923
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Two transformative waves of computing have redefined the way we approach
science. The first wave came with the birth of the digital computer, which
enabled scientists to numerically simulate their models and analyze massive
datasets. This technological breakthrough led to the emergence of many
sub-disciplines bearing the prefix "computational" in their names. Currently,
we are in the midst of the second wave, marked by the remarkable advancements
in artificial intelligence. From predicting protein structures to classifying
galaxies, the scope of its applications is vast, and there can only be more
awaiting us on the horizon.
While these two waves influence scientific methodology at the instrumental
level, in this dissertation, I will present the computational lens in science,
aiming at the conceptual level. Specifically, the central thesis posits that
computation serves as a convenient and mechanistic language for understanding
and analyzing information processing systems, offering the advantages of
composability and modularity.
This dissertation begins with an illustration of the blueprint of the
computational lens, supported by a review of relevant previous work.
Subsequently, I will present my own works in quantum physics and neuroscience
as concrete examples. In the concluding chapter, I will contemplate the
potential of applying the computational lens across various scientific fields,
in a way that can provide significant domain insights, and discuss potential
future directions.
Related papers
- Atomic Quantum Technologies for Quantum Matter and Fundamental Physics Applications [0.0]
Physics is living an era of unprecedented cross-fertilization among the different areas of science.
We discuss the manifold impact that ultracold-atom quantum technologies can have in fundamental and applied science.
We illustrate how the engineering of table-top experiments with atom technologies is engendering applications.
arXiv Detail & Related papers (2024-05-10T16:52:20Z) - Large Language Models for Scientific Synthesis, Inference and
Explanation [56.41963802804953]
We show how large language models can perform scientific synthesis, inference, and explanation.
We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature.
This approach has the further advantage that the large language model can explain the machine learning system's predictions.
arXiv Detail & Related papers (2023-10-12T02:17:59Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - A Computational Inflection for Scientific Discovery [48.176406062568674]
We stand at the foot of a significant inflection in the trajectory of scientific discovery.
As society continues on its fast-paced digital transformation, so does humankind's collective scientific knowledge.
Computer science is poised to ignite a revolution in the scientific process itself.
arXiv Detail & Related papers (2022-05-04T11:36:54Z) - Quantum computing at the quantum advantage threshold: a down-to-business
review [1.0323063834827415]
We review the state of the art in quantum computing, promising computational models and the most developed physical platforms.
We also discuss potential applications, the requirements posed by these applications and technological pathways towards addressing these requirements.
The review is written in a simple language without equations, and should be accessible to readers with no advanced background in mathematics and physics.
arXiv Detail & Related papers (2022-03-31T16:55:39Z) - Neural Fields in Visual Computing and Beyond [54.950885364735804]
Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks.
neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation.
This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
arXiv Detail & Related papers (2021-11-22T18:57:51Z) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - Simulating Quantum Materials with Digital Quantum Computers [55.41644538483948]
Digital quantum computers (DQCs) can efficiently perform quantum simulations that are otherwise intractable on classical computers.
The aim of this review is to provide a summary of progress made towards achieving physical quantum advantage.
arXiv Detail & Related papers (2021-01-21T20:10:38Z) - Quantum Computing Methods for Supervised Learning [0.08594140167290096]
Small-scale quantum computers and quantum annealers have been built and are already being sold commercially.
We provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems.
arXiv Detail & Related papers (2020-06-22T06:34:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.