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

• URL: http://arxiv.org/abs/2603.26904v1
• Date: Fri, 27 Mar 2026 18:23:09 GMT
• Title: Are LLMs Good For Quantum Software, Architecture, and System Design?
• Abstract summary: Quantum computers promise massive computational speedup for problems in many critical domains, such as physics, chemistry, cryptanalysis, healthcare, etc.<n>Despite decades of research, they remain far from entering an era of utility.<n>The lack of mature software, architecture, and systems solutions capable of translating quantum-mechanical properties of algorithms into physical state transformations remains a key factor underlying the slow pace of technological progress.
• Score: 1.1428160198646473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
  This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
  arXiv  Detail & Related papers  (2025-08-29T10:37:19Z)
• Programming Quantum Computers with Large Language Models [0.29998889086656577]
  Large language models (LLMs) promise transformative change to fields as diverse as medical diagnosis, legal services, and software development.<n>This work is a first look at how well an uncustomized, publicly available LLM can write straightforward quantum circuits.
  arXiv  Detail & Related papers  (2025-06-22T18:18:35Z)
• Towards reliable quantum software, algorithm and use-case development: Multidisciplinary analysis from the perspective of Finnish industries [1.7529550873546489]
  TORQS has studied the dilemma of reliable software development and potential for quantum computing for Finnish industries from multidisciplinary points of views.<n>Here we condense the main observations and results of the project into an essay roadmap and timeline for investing in quantum software, algorithms, hardware, and business.
  arXiv  Detail & Related papers  (2025-06-19T12:05:47Z)
• Quantum Software Engineering and Potential of Quantum Computing in Software Engineering Research: A Review [8.626933144631955]
  This paper aims to review the role of quantum computing in software engineering research and the latest developments in quantum software engineering.<n>We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering.
  arXiv  Detail & Related papers  (2025-02-13T03:22:36Z)
• QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
  Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• The QUATRO Application Suite: Quantum Computing for Models of Human Cognition [49.038807589598285]
  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.
  arXiv  Detail & Related papers  (2023-09-01T17:34:53Z)
• Reliable AI: Does the Next Generation Require Quantum Computing? [71.84486326350338]
  We show that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations. In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.
  arXiv  Detail & Related papers  (2023-07-03T19:10:45Z)
• Quantum Computing Toolkit From Nuts and Bolts to Sack of Tools [0.0]
  Quantum computing has the potential to provide exponential performance benefits in processing over classical computing. It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem. Quantum computers are in the nascent stage of development and are noisy due to decoherence, i.e., quantum bits deteriorate with environmental interactions.
  arXiv  Detail & Related papers  (2023-02-17T14:08:44Z)
• 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)
• Architectures for Quantum Information Processing [5.190207094732672]
  Quantum computing is changing the way we think about computing. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments.
  arXiv  Detail & Related papers  (2022-11-11T19:18:44Z)
• 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)
• Long-Time Error-Mitigating Simulation of Open Quantum Systems on Near Term Quantum Computers [38.860468003121404]
  We study an open quantum system simulation on quantum hardware, which demonstrates robustness to hardware errors even with deep circuits containing up to two thousand entangling gates. We simulate two systems of electrons coupled to an infinite thermal bath: 1) a system of dissipative free electrons in a driving electric field; and 2) the thermalization of two interacting electrons in a single orbital in a magnetic field -- the Hubbard atom. Our results demonstrate that algorithms for simulating open quantum systems are able to far outperform similarly complex non-dissipative algorithms on noisy hardware.
  arXiv  Detail & Related papers  (2021-08-02T21:36:37Z)

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.