The QUATRO Application Suite: Quantum Computing for Models of Human
Cognition
- URL: http://arxiv.org/abs/2309.00597v2
- Date: Fri, 8 Dec 2023 12:21:50 GMT
- Title: The QUATRO Application Suite: Quantum Computing for Models of Human
Cognition
- Authors: Raghavendra Pradyumna Pothukuchi, Leon Lufkin, Yu Jun Shen, Alejandro
Simon, Rome Thorstenson, Bernardo Eilert Trevisan, Michael Tu, Mudi Yang, Ben
Foxman, Viswanatha Srinivas Pothukuchi, Gunnar Epping, Thi Ha Kyaw, Bryant J
Jongkees, Yongshan Ding, Jerome R Busemeyer, Jonathan D Cohen, Abhishek
Bhattacharjee
- Abstract summary: We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling.
We release QUATRO, a collection of quantum computing applications from cognitive models.
- Score: 49.038807589598285
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Research progress in quantum computing has, thus far, focused on a narrow set
of application domains. Expanding the suite of quantum application domains is
vital for the discovery of new software toolchains and architectural
abstractions. In this work, we unlock a new class of applications ripe for
quantum computing research -- computational cognitive modeling. Cognitive
models are critical to understanding and replicating human intelligence. Our
work connects computational cognitive models to quantum computer architectures
for the first time. We release QUATRO, a collection of quantum computing
applications from cognitive models. The development and execution of QUATRO
shed light on gaps in the quantum computing stack that need to be closed to
ease programming and drive performance. Among several contributions, we propose
and study ideas pertaining to quantum cloud scheduling (using data from gate-
and annealing-based quantum computers), parallelization, and more. In the long
run, we expect our research to lay the groundwork for more versatile quantum
computer systems in the future.
Related papers
- Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study [11.29246196323319]
We describe NASA's work in assessing and advancing the potential of quantum computing.
We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware and with simulations.
This work also includes physics-inspired classical algorithms that can be used at application scale today.
arXiv Detail & Related papers (2024-06-21T19:05:42Z) - Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms [0.6465466167591405]
QInterpreter is a tool embedded in the Quantum Science Gateway QubitHub.
It translates seamlessly programs from one library to the other and visualizes the results.
arXiv Detail & Related papers (2023-10-11T03:45:11Z) - 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) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Evolution of Quantum Computing: A Systematic Survey on the Use of
Quantum Computing Tools [5.557009030881896]
We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing.
We discuss the current essence, identify open challenges and provide future research direction.
We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
arXiv Detail & Related papers (2022-04-04T21:21:12Z) - From Quantum Graph Computing to Quantum Graph Learning: A Survey [86.8206129053725]
We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions.
For its practicability and wide-applicability, we give a brief review of typical graph learning techniques.
We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research.
arXiv Detail & Related papers (2022-02-19T02:56:47Z) - A comparative study of universal quantum computing models: towards a
physical unification [0.0]
Recent progresses motivate us to study in depth the universal quantum computing models (UQCM)
Although being developed decades ago, a physically concise principle or picture to formalize and understand UQCM is still lacking.
This is challenging given the diversity of still-emerging models, but important to understand the difference between classical and quantum computing.
arXiv Detail & Related papers (2021-08-17T23:56:04Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum Computing - A new scientific revolution in the making [2.240702708599667]
We advocate the PISQ approach: Perfect Intermediate-Scale Quantum computing based on a well-established concept of perfect qubits.
We expand the quantum road map with (N)FTQC, which stands for (Non) Fault-Tolerant Quantum Computing.
This will allow researchers to focus exclusively on developing new applications by defining the algorithms in terms of perfect qubits.
arXiv Detail & Related papers (2021-06-22T14:56:55Z)
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.