Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters

Gao, Qi; Jones, Gavin O.; Motta, Mário; Sugawara, M.; Watanabe, Hiroshi; Kobayashi, Takao; Watanabe, Eriko; Ohnishi, Yutaka; Nakamura, Hajime; Yamamoto, Naoki

doi:10.1038/s41524-021-00540-6

Cited by 52 publications

(51 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other important and recent works can be seen in Singh et al ( 2021a ), Wang et al ( 2021 ), Yunakovsky et al ( 2021 ), Gao et al ( 2021 ), Huber et al ( 2021 ), Kulkarni et al ( 2021 ), Ajagekar and You ( 2021 ), Alberts et al ( 2021 ), Medvidović and Carleo ( 2021 ), Singh et al ( 2021b ), Im et al ( 2021 ) Liu et al ( 2021 ).…”

Section: Literature Reviewmentioning

confidence: 94%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

View full text Add to dashboard Cite

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world.

show abstract

Section: Literature Reviewmentioning

confidence: 94%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

View full text Add to dashboard Cite

show abstract

“…One route to compute excitation energies directly is the quantum equation of motion (q-EOM) method, well established in classical simulations and recently extended to quantum computation. 310,[317][318][319][320] In this framework, excitation energies are computed as…”

Section: Variational Quantum Simulationmentioning

confidence: 99%

“…Quantum subspace expansion (QSE) computes total energies of the ground and excited states, which can be subtracted to yield excitation energies. One route to compute excitation energies directly is the quantum equation of motion (q‐EOM) method, well established in classical simulations and recently extended to quantum computation 310,317–320 . In this framework, excitation energies are computed as

normalΔ E_{μ} goodbreak= \frac{〈Ψ_{0}| ([],,, {\overset{O}{true}}_{μ}, \hat{H}, {\overset{O}{true}}_{μ}^{†})| Ψ_{0}〉}{〈Ψ_{0}| ([],, {\overset{O}{true}}_{μ}, {\overset{O}{true}}_{μ}^{†})| Ψ_{0}〉},

where

2 [{\hat{O}}_{μ}, \overset{H}{true}, {\hat{O}}_{μ}^{†}] = [[{\hat{O}}_{μ}, \overset{H}{true}], {\hat{O}}_{μ}^{†}] + [{\hat{O}}_{μ}, [\overset{H}{true}, {\hat{O}}_{μ}^{†}]]

and

{\hat{O}}_{μ}

is an excitation operator expanded on a suitable basis.…”

Section: Quantum Algorithms For Chemistrymentioning

confidence: 99%

Emerging quantum computing algorithms for quantum chemistry

Motta

Rice

2021

WIREs Comput Mol Sci

Self Cite

View full text Add to dashboard Cite

Digital quantum computers provide a computational framework for solving the Schrödinger equation for a variety of many‐particle systems. Quantum computing algorithms for the quantum simulation of these systems have recently witnessed remarkable growth, notwithstanding the limitations of existing quantum hardware, especially as a tool for electronic structure computations in molecules. In this review, we provide a self‐contained introduction to emerging algorithms for the simulation of Hamiltonian dynamics and eigenstates, with emphasis on their applications to the electronic structure in molecular systems. Theoretical foundations and implementation details of the method are discussed, and their strengths, limitations, and recent advances are presented. This article is categorized under: Quantum Computing > Algorithms Electronic Structure Theory > Ab Initio Electronic Structure Methods Quantum Computing > Theory Development

show abstract

“…[278] used the quantum equation of motion for computing molecular excitation energies. The method has been experimentally demonstrated by computing excited states of phenylsulfonyl-carbazole compounds [279].…”

Section: Excited Statesmentioning

confidence: 99%

Simulating Quantum Materials with Digital Quantum Computers

Bassman,

Urbanek,

Metcalf

et al. 2021

Preprint

View full text Add to dashboard Cite

Quantum materials exhibit a wide array of exotic phenomena and practically useful properties. A better understanding of these materials can provide deeper insights into fundamental physics in the quantum realm as well as advance technology for entertainment, healthcare, and sustainability. The emergence of digital quantum computers (DQCs), which can efficiently perform quantum simulations that are otherwise intractable on classical computers, provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. Equipped with these new tools, scientists from diverse domains are racing towards achieving physical quantum advantage (i.e., using a quantum computer to learn new physics with a computation that cannot feasibly be run on any classical computer). The aim of this review, therefore, is to provide a summary of progress made towards this goal that is accessible to scientists across the physical sciences. We will first review the available technology and algorithms, and detail the myriad ways to represent materials on quantum computers. Next, we will showcase the simulations that have been successfully performed on currently available DQCs, emphasizing the variety of properties, both static and dynamic, that can be studied with this nascent technology. Finally, we work through two examples of how to map a materials problem onto a DQC, with full code included in the Supplementary Material. It is our hope that this review can serve as an organized overview of progress in the field for domain experts and an accessible introduction to scientists in related fields interested in beginning to perform their own simulations of quantum materials on DQCs.

show abstract

Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters

Cited by 52 publications

References 39 publications

A review on quantum computing and deep learning algorithms and their applications

A review on quantum computing and deep learning algorithms and their applications

Emerging quantum computing algorithms for quantum chemistry

Simulating Quantum Materials with Digital Quantum Computers

Contact Info

Product

Resources

About