Recent Advances in Quantum Architecture Search
- URL: http://arxiv.org/abs/2604.26289v1
- Date: Wed, 29 Apr 2026 04:34:22 GMT
- Title: Recent Advances in Quantum Architecture Search
- Abstract summary: Variational quantum algorithms (VQAs) constitute a prominent framework for exploring the capabilities of near-term quantum computers.<n>Quantum Architecture Search (QAS) has emerged as a critical research area to automate the discovery of high-performing circuit structures.
- Score: 7.473388435484934
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational quantum algorithms (VQAs) constitute a prominent framework for exploring the capabilities of near-term quantum computers. As the effectiveness of VQAs depends heavily on the design of variational quantum circuits, Quantum Architecture Search (QAS) has emerged as a critical research area to automate the discovery of high-performing circuit structures. This paper reviews key advancements in current research on QAS, including its core concepts, representative methodologies, and applications. The content is structured to ensure broad accessibility for a diverse audience of researchers while preserving the core principles of complex methodologies. In addition, we discuss remaining challenges and suggest potential research directions to offer perspectives on future exploration in this rapidly evolving field.
Related papers
- Quantum Reinforcement Learning: Recent Advances and Future Directions [50.89638884527093]
reinforcement learning stands out as a promising yet underexplored frontier.<n>We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK.<n>We discuss promising use cases that may drive innovation in quantum-inspired reinforcement learning.
arXiv Detail & Related papers (2025-10-16T11:59:08Z) - Artificial intelligence for representing and characterizing quantum systems [49.29080693498154]
Efficient characterization of large-scale quantum systems is a central challenge in quantum science.<n>Recent advances in artificial intelligence (AI) have emerged as a powerful tool to address this challenge.<n>This review discusses how each of these AI paradigms contributes to two core tasks in quantum systems characterization.
arXiv Detail & Related papers (2025-09-05T08:41:24Z) - RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
arXiv Detail & Related papers (2025-06-04T08:30:35Z) - A Survey of Quantum Transformers: Architectures, Challenges and Outlooks [82.4736481748099]
Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing.<n>Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications.<n>This paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models.
arXiv Detail & Related papers (2025-04-04T05:40:18Z) - Topology-Driven Quantum Architecture Search Framework [2.9862856321580895]
Topology-Driven Quantum Architecture Search (TD-QAS)<n>Topology-Driven Quantum Architecture Search (TD-QAS)<n>Topology-Driven Quantum Architecture Search (TD-QAS)
arXiv Detail & Related papers (2025-02-20T05:05:53Z) - Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey [4.829719151294255]
Quantum-inspired evolutionary algorithms (QIEAs) employ quantum bits to adopt a probabilistic representation of the state of a feature in a given solution.
We conducted a comprehensive survey across various publishers and gathered 56 papers.
arXiv Detail & Related papers (2024-07-25T11:07:19Z) - Quantum Architecture Search: A Survey [0.0]
The application of quantum computing to solve real-world problems is still hampered by hardware limitations and a relatively under-explored landscape of quantum algorithms.
Research on the automated generation of quantum circuits (PQCs) has gained considerable interest.
arXiv Detail & Related papers (2024-06-10T12:17:46Z) - Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - Towards Quantum Federated Learning [80.1976558772771]
Quantum Federated Learning aims to enhance privacy, security, and efficiency in the learning process.
We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL.
As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries.
arXiv Detail & Related papers (2023-06-16T15:40:21Z) - QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks.
Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture.
We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
arXiv Detail & Related papers (2023-04-14T08:03:20Z) - Evolutionary Quantum Architecture Search for Parametrized Quantum
Circuits [7.298440208725654]
We introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models.
We show that our method can significantly improve the performance of hybrid quantum-classical models.
arXiv Detail & Related papers (2022-08-23T19:47:37Z) - Differentiable Quantum Architecture Search [15.045985536395479]
We propose a general framework of differentiable quantum architecture search (DQAS)
DQAS enables automated designs of quantum circuits in an end-to-end differentiable fashion.
arXiv Detail & Related papers (2020-10-16T18:00:03Z)
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.