Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Physics Today

A systems perspective of quantum computing

A. Y. Matsuura, Sonika Johri, Justin Hogaboam

Published Mar 1, 2019• 13 citations
Publisher

Abstract

Quantum architects, with knowledge of both physics and computer engineering, are key to developing scalable quantum computers.
DOI
10.1063/pt.3.4163

Related papers

A blueprint for building a quantum computer

Rodney Van Meter, Dominic C. Horsman

Quantum computer architecture holds the key to building commercially viable systems.

Communications of the ACM•Sep 26, 2013•115 citations

Architecting full-stack quantum computing systems in the NISQ era and beyond

Carmen G. Almudéver

The advances in quantum hardware with functional quantum processors integrating tens of noisy qubits, together with the availability of near-term quantum algorithms have allowed the development of the so-called full-stacks that bridge quantum applications with quantum devices. In this talk, we will provide an overview of the different layers of the quantum computing full-stack, with emphasis on the software ones that include the compilation of quantum algorithms. We will then discuss the need for optimal physical-aware and algorithm-driven compilation techniques in the midst of the NISQ (Noisy Intermediate-scale quantum) era. We will also focus on key principles for architecting quantum computers such as codesign, optimization and benchmarking and illustrate how the use of structured design space exploration methodologies could help towards the development of a cross-layer codesign framework for full-stack quantum computing systems. Finally, we will talk about the scalability of quantum computing systems, which is one of the main challenges the quantum computing community is currently facing.

•Nov 18, 2022•0 citations

Towards Process Centered Architecting for Quantum Software Systems

Aakash Ahmad, Arif Ali Khan, Muhammad Waseem +2 more

Quantum Software Engineering (QSE) is a recent trend - focused on unifying the principles of quantum mechanics and practices of software engineering - to design, develop, validate, and evolve quantum age software systems and applications. Software architecture for quantum computing (a.k. a. quantum software architectures (QSA)) supports the design, development, and maintenance etc. phases of quantum software systems using architectural components and connectors. QSA can enable quantum software designers and developers to map the operations of Qubits to architectural components and connectors for implementing quantum software. This research aims to explore the role of QSAs by investigating (i) architectural process having architecting activities, and (ii) human roles that can exploit available tools to automate and customise architecture-centric implementation of quantum software. Results of this research can facilitate knowledge transfer, enabling researchers and practitioners, to address challenges of architecture-centric implementation of quantum software systems.

IEEE International Conference on Quantum Software (QSW)•Jul 1, 2022•25 citations

Quantum Computing Architecture and Hardware for Engineers

Hiu Yung Wong

Springer eBooks•Jan 1, 2025•6 citations

Architectural Paradigms Shaping the Future of Quantum Computing

Jyoti Gavhane, Gayatri Shirke, Ayush Nakod

International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT)•Apr 1, 2026•0 citations

Are LLMs Good For Quantum Software, Architecture, and System Design?

Sourish Wawdhane, Poulami Das

Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they remain far from entering an era of utility. The lack of mature software, architecture, and systems solutions capable of translating quantum-mechanical properties of algorithms into physical state transformations on qubit devices remains a key factor underlying the slow pace of technological progress. The problem worsens due to significant reliance on domain-specific expertise, especially for software developers, computer architects, and systems engineers. To address these limitations and accelerate large-scale high-performance quantum system design, we ask: Can large language models (LLMs) help with solving quantum software, architecture, and systems problems? In this work, we present a case study assessing the performance of LLMs on quantum system reasoning tasks. We evaluate nine frontier LLMs and compare their performance to graduate UT Austin students on a set of quantum computing problems. Finally, we recommend several directions along which research and engineering development efforts must be pursued.

quant-pharXiv:2603.26904•Mar 31, 2026•0 citations
The advances in quantum hardware with functional quantum processors integrating tens of noisy qubits, together with the availability of near-term quantum algorithms have allowed the development of the so-called full-stacks that bridge quantum applications with quantum devices. In this talk, we will provide an overview of the different layers of the quantum computing full-stack, with emphasis on the software ones that include the compilation of quantum algorithms. We will then discuss the need for optimal physical-aware and algorithm-driven compilation techniques in the midst of the NISQ (Noisy Intermediate-scale quantum) era. We will also focus on key principles for architecting quantum computers such as codesign, optimization and benchmarking and illustrate how the use of structured design space exploration methodologies could help towards the development of a cross-layer codesign framework for full-stack quantum computing systems. Finally, we will talk about the scalability of quantum computing systems, which is one of the main challenges the quantum computing community is currently facing.
Quantum Software Engineering (QSE) is a recent trend - focused on unifying the principles of quantum mechanics and practices of software engineering - to design, develop, validate, and evolve quantum age software systems and applications. Software architecture for quantum computing (a.k. a. quantum software architectures (QSA)) supports the design, development, and maintenance etc. phases of quantum software systems using architectural components and connectors. QSA can enable quantum software designers and developers to map the operations of Qubits to architectural components and connectors for implementing quantum software. This research aims to explore the role of QSAs by investigating (i) architectural process having architecting activities, and (ii) human roles that can exploit available tools to automate and customise architecture-centric implementation of quantum software. Results of this research can facilitate knowledge transfer, enabling researchers and practitioners, to address challenges of architecture-centric implementation of quantum software systems.
Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc. However, despite decades of research, they remain far from entering an era of utility. The lack of mature software, architecture, and systems solutions capable of translating quantum-mechanical properties of algorithms into physical state transformations on qubit devices remains a key factor underlying the slow pace of technological progress. The problem worsens due to significant reliance on domain-specific expertise, especially for software developers, computer architects, and systems engineers. To address these limitations and accelerate large-scale high-performance quantum system design, we ask: Can large language models (LLMs) help with solving quantum software, architecture, and systems problems? In this work, we present a case study assessing the performance of LLMs on quantum system reasoning tasks. We evaluate nine frontier LLMs and compare their performance to graduate UT Austin students on a set of quantum computing problems. Finally, we recommend several directions along which research and engineering development efforts must be pursued.