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}
@article{Zhang_2022,
doi = {10.1088/2058-9565/ac87cd},
url = {https://doi.org/10.1088/2058-9565/ac87cd},
year = {2022},
month = {aug},
publisher = {IOP Publishing},
volume = {7},
number = {4},
pages = {045023},
author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao, Hong},
title = {Differentiable quantum architecture search},
journal = {Quantum Science and Technology},
abstract = {Quantum architecture search (QAS) is the process of automating architecture engineering of quantum circuits. It has been desired to construct a powerful and general QAS platform which can significantly accelerate current efforts to identify quantum advantages of error-prone and depth-limited quantum circuits in the NISQ era. Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion. We present several examples of circuit design problems to demonstrate the power of DQAS. For instance, unitary operations are decomposed into quantum gates, noisy circuits are re-designed to improve accuracy, and circuit layouts for quantum approximation optimization algorithm are automatically discovered and upgraded for combinatorial optimization problems. These results not only manifest the vast potential of DQAS being an essential tool for the NISQ application developments, but also present an interesting research topic from the theoretical perspective as it draws inspirations from the newly emerging interdisciplinary paradigms of differentiable programming, probabilistic programming, and quantum programming.}
doi = {10.1088/2058-9565/ac87cd},
url = {https://doi.org/10.1088/2058-9565/ac87cd},
year = {2022},
month = {aug},
publisher = {IOP Publishing},
volume = {7},
number = {4},
pages = {045023},
author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao,
Hong},
title = {Differentiable quantum architecture search},
journal = {Quantum Science and Technology},
abstract = {Quantum architecture search (QAS) is the process of automating
architecture engineering of quantum circuits. It has been desired
to construct a powerful and general QAS platform which can
significantly accelerate current efforts to identify quantum
advantages of error-prone and depth-limited quantum circuits in
the NISQ era. Hereby, we propose a general framework of
differentiable quantum architecture search (DQAS), which enables
automated designs of quantum circuits in an end-to-end
differentiable fashion. We present several examples of circuit
design problems to demonstrate the power of DQAS. For instance,
unitary operations are decomposed into quantum gates, noisy
circuits are re-designed to improve accuracy, and circuit layouts
for quantum approximation optimization algorithm are
automatically discovered and upgraded for combinatorial
optimization problems. These results not only manifest the vast
potential of DQAS being an essential tool for the NISQ
application developments, but also present an interesting
research topic from the theoretical perspective as it draws
inspirations from the newly emerging interdisciplinary paradigms
of differentiable programming, probabilistic programming, and
quantum programming.},
}
@article{qdea,
title = {A survey on Quality-Diversity optimization: Approaches,
applications, and challenges},
journal = {Swarm and Evolutionary Computation},
volume = {100},
pages = {102240},
year = {2026},
issn = {2210-6502},
doi = {https://doi.org/10.1016/j.swevo.2025.102240},
url = {https://www.sciencedirect.com/science/article/pii/S2210650225003979},
author = {Haoxiang Qin and Yi Xiang and Hainan Zhang and Yuyan Han and
Yuting Wang and Xinrui Tao and Yiping Liu},
keywords = {Quality-Diversity optimization, Evolutionary computation,
Feature space, MAP-Elites},
abstract = {Quality-Diversity (QD) optimization is a paradigm of
evolutionary computation (EC) that extends the classic approaches
, aiming to generate a collection of solutions that are both
diverse and high-performing. Unlike traditional evolutionary
algorithms (EAs), QD methods emphasize the illumination (or
coverage) of a user-defined feature space, while simultaneously
aiming for local optimization within each discovered region of
the feature space. Over the past decade, QD has rapidly developed
and proven effective in areas such as evolutionary robotics and
video games. However, a systematic review of this growing field
remains lacking. To date, the most recent review article on QD
was published in 2021. Therefore, to offer a more comprehensive
overview of the latest QD research, this paper provides a
thorough survey of QD optimization, covering its foundational
principles and representative algorithmic frameworks such as
Novelty Search with Local Competition (NSLC), MAP-Elites, the
unified modular QD framework, and RIBS. In addition, we divide
the algorithm improvement part into three modules for discussion:
containers, selection, and mutation. Then, the evaluation metrics
widely used in QD optimization are listed for researchers. We
further explore its diverse applications across domains such as
evolutionary robotics, video games, scheduling, software testing,
and engineering design. Finally, we discuss the current
challenges in the field and outline promising directions for
future research.},
}