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#import "@preview/touying:0.6.1": *
#import "@preview/physica:0.9.5": *
#import "@preview/cetz:0.3.4"
#import "@preview/typsium:0.2.0": ce
#import "@preview/numbly:0.1.0": numbly
#import "./theme.typ": *
#set heading(numbering: numbly("{1}.", default: "1.1"))
#show ref: set text(size:0.5em, baseline: -0.75em)
#let cetz-canvas = touying-reducer.with(reduce: cetz.canvas, cover: cetz.draw.hide.with(bounds: true))
#show: university-theme.with(
config-info(
title: "Implementation Specific QAS", // Required
date: datetime.today().display(),
authors: ("Noa Aarts"),
// Optional Styling (for more / explanation see in the typst universe)
// ignore how bad the images look i'll adjust it until Monday
title-color: blue.darken(10%),
),
config-common(
// handout: true, // enable this for a version without animations
),
aspect-ratio: "16-9",
config-colors(
primary: rgb("#00a6d6"),
secondary: rgb("#00b3dc"),
tertiary: rgb("#b8cbde"),
neutral-lightest: rgb("#ffffff"),
neutral-darkest: rgb("#000000"),
),
)
#show outline.entry: it => link(
it.element.location(),
text(fill: rgb("#00b3dc"), size: 1.3em)[#it.indented(it.prefix(), it.body())],
)
#slide[
- #text(fill: purple)[Purple text is a question I have]
- #text(fill: red)[Red text is something I think they did not do well]
- #text(fill: orange)[Orange text is something I would have preferred a reference for]
]
#title-slide()
#outline(depth: 1, title: text(fill: rgb("#00a6d6"))[Content])
= Introduction
== Variational Quantum Algorithms
- Classical optimisation
- Parametrized Quantum Circuit
- Very structure dependent
== Quantum Architecture Search
- Automated Design
- New Problems
- Exponential search space
- Ranking circuits during search
- Parallels with Neural Architecture Search
- Differentiable QAS
- Reinforcement-learning QAS
- Predictor-based QAS
- Weight-sharing QAS
== Training Free Proxies
- No need to train parametrized quantum circuit
\ $->$ Faster searching
- No objective functions
- Possibility for easier transfer
- Need to prove correlation with ground-truth
#text(fill: red)[- not done in paper]
= Method
== Overview
#align(horizon)[
1. Sample circuits from search space
2. Filter using Path proxy
3. Rank on Expressibility
]
== Search Space
Following Neural Predictor based QAS@npqas
- Native gate set ($cal(A) = {R_x, R_y, R_z, X X, Y Y, Z Z}$)
- Layer based sampling
- Layers of $n/2$ gates
- Gate based sampling
- placing 1 gate at a time
- Why not fully random circuits?
- Mitigating barren plateaus
- Mitigating high circuit depth
#text(fill:purple)[- What is the difference with gate-based?]
== Path Proxy
#slide(composer: (auto, auto))[
- 'zero-cost'
#text(fill:orange)[- best case: $O("Operations" times "Qubits"^2)$]
// I think it'd scale like this, but am uncertain since they didn't explain it anywhere
- below $7.8 times 10^(-4)$s
1. Represent as Directed acyclic graph
2. Count distinct paths from input-to-output
3. Top-R highest path count circuits
][
#image("tf-qas/circuit.png", height: 40%)
#image("tf-qas/dag.png")
#text(size: 0.6em)[#align(right)[from Training-Free QAS@training-free]]
]
== Expressibility Proxy
#text(fill: red)[- Performance hinges on Expressibility]
- Particularly valueable without prior knowledge
#block(fill: blue.lighten(85%), inset: 12pt, radius: 6pt, stroke: 2pt + blue)[
*Expressibility:* \
The capability to uniformly reach the entire Hilbert space.
]
1. Calculate expressibility:
#align(center)[$cal(E)(cal(C)) = -D_"KL" (P(cal(C),F) || P_"Haar" (F)$]
2. Top expressibility circuits
= Results
== Evaluation
- Three variational quantum eigensolver tasks
- Transverse field Ising model
- Heisenberg model
- $"Be"space.hair"H"_2$ molecule
- Compared to
- Network-Predictive QAS@npqas
#text(fill: red)[- Hardware-efficient ansatz@hea-kandala] // but like, which one
- Random sampling
- Implementation details
- TensorCircuit python package@tensorcircuit
#text(fill: red)[- No code included anywhere]
= Conclusion
==
- Combining proxies can improve on either
#slide[
== References <touying:unoutlined>
#bibliography("references.bib", title: [])
]

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report/references.bib
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@article{quantum-advantage-bounds,
title = {Information-Theoretic Bounds on Quantum Advantage in Machine Learning
},
author = {Huang, Hsin-Yuan and Kueng, Richard and Preskill, John},
journal = {Phys. Rev. Lett.},
volume = {126},
issue = {19},
pages = {190505},
numpages = {7},
year = {2021},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.126.190505},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.126.190505},
title = {Information-Theoretic Bounds on Quantum Advantage in Machine
Learning },
author = {Huang, Hsin-Yuan and Kueng, Richard and Preskill, John},
journal = {Phys. Rev. Lett.},
volume = {126},
issue = {19},
pages = {190505},
numpages = {7},
year = {2021},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.126.190505},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.126.190505},
}
@article{quantum-advantage-learning,
author = {Hsin-Yuan Huang and Michael Broughton and Jordan Cotler and Sitan
Chen and Jerry Li and Masoud Mohseni and Hartmut Neven and Ryan
Babbush and Richard Kueng and John Preskill and Jarrod R. McClean },
title = {Quantum advantage in learning from experiments},
journal = {Science},
volume = {376},
number = {6598},
pages = {1182-1186},
year = {2022},
doi = {10.1126/science.abn7293},
URL = {https://www.science.org/doi/abs/10.1126/science.abn7293},
eprint = {https://www.science.org/doi/pdf/10.1126/science.abn7293},
abstract = {Quantum technology promises to revolutionize how we learn about
the physical world. An experiment that processes quantum data with
a quantum computer could have substantial advantages over
conventional experiments in which quantum states are measured and
outcomes are processed with a classical computer. We proved that
quantum machines could learn from exponentially fewer experiments
than the number required by conventional experiments. This
exponential advantage is shown for predicting properties of
physical systems, performing quantum principal component analysis,
and learning about physical dynamics. Furthermore, the quantum
resources needed for achieving an exponential advantage are quite
modest in some cases. Conducting experiments with 40
superconducting qubits and 1300 quantum gates, we demonstrated that
a substantial quantum advantage is possible with today’s quantum
processors. There is considerable interest in extending the recent
success of quantum computers in outperforming their conventional
classical counterparts (quantum advantage) from some model
mathematical problems to more meaningful tasks. Huang et al. show
how manipulating multiple quantum states can provide an exponential
advantage over classical processing of measurements of
single-quantum states for certain learning tasks. These include
predicting properties of physical systems, performing quantum
principal component analysis on noisy states, and learning
approximate models of physical dynamics (see the Perspective by
Dunjko). In their proof-of-principle experiments using up to 40
qubits on a Google Sycamore quantum processor, the authors achieved
almost four orders of magnitude of reduction in the required number
of experiments over the best-known classical lower bounds. —YS
Quantum-enhanced strategies can provide a dramatic performance
boost in learning useful information from quantum experiments.},
author = {Hsin-Yuan Huang and Michael Broughton and Jordan Cotler and Sitan
Chen and Jerry Li and Masoud Mohseni and Hartmut Neven and Ryan
Babbush and Richard Kueng and John Preskill and Jarrod R. McClean },
title = {Quantum advantage in learning from experiments},
journal = {Science},
volume = {376},
number = {6598},
pages = {1182-1186},
year = {2022},
doi = {10.1126/science.abn7293},
URL = {https://www.science.org/doi/abs/10.1126/science.abn7293},
eprint = {https://www.science.org/doi/pdf/10.1126/science.abn7293},
abstract = {Quantum technology promises to revolutionize how we learn about
the physical world. An experiment that processes quantum data
with a quantum computer could have substantial advantages over
conventional experiments in which quantum states are measured and
outcomes are processed with a classical computer. We proved that
quantum machines could learn from exponentially fewer experiments
than the number required by conventional experiments. This
exponential advantage is shown for predicting properties of
physical systems, performing quantum principal component analysis
, and learning about physical dynamics. Furthermore, the quantum
resources needed for achieving an exponential advantage are quite
modest in some cases. Conducting experiments with 40
superconducting qubits and 1300 quantum gates, we demonstrated
that a substantial quantum advantage is possible with today’s
quantum processors. There is considerable interest in extending
the recent success of quantum computers in outperforming their
conventional classical counterparts (quantum advantage) from some
model mathematical problems to more meaningful tasks. Huang et
al. show how manipulating multiple quantum states can provide an
exponential advantage over classical processing of measurements
of single-quantum states for certain learning tasks. These
include predicting properties of physical systems, performing
quantum principal component analysis on noisy states, and
learning approximate models of physical dynamics (see the
Perspective by Dunjko). In their proof-of-principle experiments
using up to 40 qubits on a Google Sycamore quantum processor, the
authors achieved almost four orders of magnitude of reduction in
the required number of experiments over the best-known classical
lower bounds. —YS Quantum-enhanced strategies can provide a
dramatic performance boost in learning useful information from
quantum experiments.},
}
@article{expressibility-and-entanglement,
author = {Sim, Sukin and Johnson, Peter D. and Aspuru-Guzik, Alán},
title = {Expressibility and Entangling Capability of Parameterized Quantum
Circuits for Hybrid Quantum-Classical Algorithms},
journal = {Advanced Quantum Technologies},
volume = {2},
number = {12},
pages = {1900070},
keywords = {quantum algorithms, quantum circuits, quantum computation},
doi = {https://doi.org/10.1002/qute.201900070},
url = {https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900070
},
eprint = {
https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.201900070
},
abstract = {Abstract Parameterized quantum circuits (PQCs) play an essential
role in the performance of many variational quantum algorithms. One
challenge in implementing such algorithms is choosing an effective
circuit that well represents the solution space while maintaining a
low circuit depth and parameter count. To characterize and identify
expressible, yet compact, circuits, several descriptors are
proposed, including expressibility and entangling capability, that
are statistically estimated from classical simulations. These
descriptors are computed for different circuit structures, varying
the qubit connectivity and selection of gates. From these
simulations, circuit fragments that perform well with respect to
the descriptors are identified. In particular, a substantial
improvement in performance of two-qubit gates in a ring or
all-to-all connected arrangement, compared to that of those on a
line, is observed. Furthermore, improvement in both descriptors is
achieved by sequences of controlled X-rotation gates compared to
sequences of controlled Z-rotation gates. In addition, it is
investigated how expressibility “saturates” with increased circuit
depth, finding that the rate and saturated value appear to be
distinguishing features of a PQC. While the correlation between
each descriptor and algorithm performance remains to be
investigated, methods and results from this study can be useful for
algorithm development and design of experiments.},
year = {2019},
author = {Sim, Sukin and Johnson, Peter D. and Aspuru-Guzik, Alán},
title = {Expressibility and Entangling Capability of Parameterized Quantum
Circuits for Hybrid Quantum-Classical Algorithms},
journal = {Advanced Quantum Technologies},
volume = {2},
number = {12},
pages = {1900070},
keywords = {quantum algorithms, quantum circuits, quantum computation},
doi = {https://doi.org/10.1002/qute.201900070},
url = {
https://advanced.onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900070
},
eprint = {
https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.201900070
},
abstract = {Abstract Parameterized quantum circuits (PQCs) play an essential
role in the performance of many variational quantum algorithms.
One challenge in implementing such algorithms is choosing an
effective circuit that well represents the solution space while
maintaining a low circuit depth and parameter count. To
characterize and identify expressible, yet compact, circuits,
several descriptors are proposed, including expressibility and
entangling capability, that are statistically estimated from
classical simulations. These descriptors are computed for
different circuit structures, varying the qubit connectivity and
selection of gates. From these simulations, circuit fragments
that perform well with respect to the descriptors are identified.
In particular, a substantial improvement in performance of
two-qubit gates in a ring or all-to-all connected arrangement,
compared to that of those on a line, is observed. Furthermore,
improvement in both descriptors is achieved by sequences of
controlled X-rotation gates compared to sequences of controlled
Z-rotation gates. In addition, it is investigated how
expressibility “saturates” with increased circuit depth, finding
that the rate and saturated value appear to be distinguishing
features of a PQC. While the correlation between each descriptor
and algorithm performance remains to be investigated, methods and
results from this study can be useful for algorithm development
and design of experiments.},
year = {2019},
}
@article{quantum-dynamics-physical-resource,
title = {Quantum dynamics as a physical resource},
author = {Nielsen, Michael A. and Dawson, Christopher M. and Dodd, Jennifer L.
and Gilchrist, Alexei and Mortimer, Duncan and Osborne, Tobias J. and
Bremner, Michael J. and Harrow, Aram W. and Hines, Andrew},
journal = {Phys. Rev. A},
volume = {67},
issue = {5},
pages = {052301},
numpages = {19},
year = {2003},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevA.67.052301},
url = {https://link.aps.org/doi/10.1103/PhysRevA.67.052301},
title = {Quantum dynamics as a physical resource},
author = {Nielsen, Michael A. and Dawson, Christopher M. and Dodd, Jennifer
L. and Gilchrist, Alexei and Mortimer, Duncan and Osborne, Tobias
J. and Bremner, Michael J. and Harrow, Aram W. and Hines, Andrew},
journal = {Phys. Rev. A},
volume = {67},
issue = {5},
pages = {052301},
numpages = {19},
year = {2003},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevA.67.052301},
url = {https://link.aps.org/doi/10.1103/PhysRevA.67.052301},
}
@article{scaling-variational-circuit-depth,
doi = {10.22331/q-2020-05-28-272},
url = {https://doi.org/10.22331/q-2020-05-28-272},
title = {Scaling of variational quantum circuit depth for condensed matter
systems},
author = {Bravo-Prieto, Carlos and Lumbreras-Zarapico, Josep and Tagliacozzo,
Luca and Latorre, Jos{\'{e}} I.},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
Quantenwissenschaften}},
volume = {4},
pages = {272},
month = may,
year = {2020},
doi = {10.22331/q-2020-05-28-272},
url = {https://doi.org/10.22331/q-2020-05-28-272},
title = {Scaling of variational quantum circuit depth for condensed matter
systems},
author = {Bravo-Prieto, Carlos and Lumbreras-Zarapico, Josep and Tagliacozzo
, Luca and Latorre, Jos{\'{e}} I.},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
Quantenwissenschaften}},
volume = {4},
pages = {272},
month = may,
year = {2020},
}
@misc{akash,
title={Reinforcement learning-assisted quantum architecture search for variational quantum algorithms},
author={Akash Kundu},
year={2024},
eprint={2402.13754},
archivePrefix={arXiv},
primaryClass={quant-ph},
url={https://arxiv.org/abs/2402.13754},
title = {Reinforcement learning-assisted quantum architecture search for
variational quantum algorithms},
author = {Akash Kundu},
year = {2024},
eprint = {2402.13754},
archivePrefix = {arXiv},
primaryClass = {quant-ph},
url = {https://arxiv.org/abs/2402.13754},
}
@article{architecture-search,
author = {Du, Yuxuan and Huang, Tao and You, Shan and Hsieh, Min-Hsiu and Tao,
Dacheng},
title = {Quantum circuit architecture search for variational quantum
algorithms},
journal = {npj Quantum Information},
year = {2022},
month = {May},
day = {23},
volume = {8},
number = {1},
pages = {62},
abstract = {Variational quantum algorithms (VQAs) are expected to be a path to
quantum advantages on noisy intermediate-scale quantum devices.
However, both empirical and theoretical results exhibit that the
deployed ansatz heavily affects the performance of VQAs such that
an ansatz with a larger number of quantum gates enables a stronger
expressivity, while the accumulated noise may render a poor
trainability. To maximally improve the robustness and trainability
of VQAs, here we devise a resource and runtime efficient scheme
termed quantum architecture search (QAS). In particular, given a
learning task, QAS automatically seeks a near-optimal ansatz (i.e.,
circuit architecture) to balance benefits and side-effects brought
by adding more noisy quantum gates to achieve a good performance.
We implement QAS on both the numerical simulator and real quantum
hardware, via the IBM cloud, to accomplish data classification and
quantum chemistry tasks. In the problems studied, numerical and
experimental results show that QAS cannot only alleviate the
influence of quantum noise and barren plateaus but also outperforms
VQAs with pre-selected ansatze.},
issn = {2056-6387},
doi = {10.1038/s41534-022-00570-y},
url = {https://doi.org/10.1038/s41534-022-00570-y},
author = {Du, Yuxuan and Huang, Tao and You, Shan and Hsieh, Min-Hsiu and
Tao, Dacheng},
title = {Quantum circuit architecture search for variational quantum
algorithms},
journal = {npj Quantum Information},
year = {2022},
month = {May},
day = {23},
volume = {8},
number = {1},
pages = {62},
abstract = {Variational quantum algorithms (VQAs) are expected to be a path
to quantum advantages on noisy intermediate-scale quantum
devices. However, both empirical and theoretical results exhibit
that the deployed ansatz heavily affects the performance of VQAs
such that an ansatz with a larger number of quantum gates enables
a stronger expressivity, while the accumulated noise may render a
poor trainability. To maximally improve the robustness and
trainability of VQAs, here we devise a resource and runtime
efficient scheme termed quantum architecture search (QAS). In
particular, given a learning task, QAS automatically seeks a
near-optimal ansatz (i.e., circuit architecture) to balance
benefits and side-effects brought by adding more noisy quantum
gates to achieve a good performance. We implement QAS on both the
numerical simulator and real quantum hardware, via the IBM cloud,
to accomplish data classification and quantum chemistry tasks. In
the problems studied, numerical and experimental results show
that QAS cannot only alleviate the influence of quantum noise and
barren plateaus but also outperforms VQAs with pre-selected
ansatze.},
issn = {2056-6387},
doi = {10.1038/s41534-022-00570-y},
url = {https://doi.org/10.1038/s41534-022-00570-y},
}
@article{evolutionary-architecture-search,
AUTHOR = {Ding, Li and Spector, Lee},
TITLE = {Multi-Objective Evolutionary Architecture Search for Parameterized
Quantum Circuits},
JOURNAL = {Entropy},
VOLUME = {25},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {93},
URL = {https://www.mdpi.com/1099-4300/25/1/93},
PubMedID = {36673234},
ISSN = {1099-4300},
ABSTRACT = {Recent work on hybrid quantum-classical machine learning systems
has demonstrated success in utilizing parameterized quantum
circuits (PQCs) to solve the challenging reinforcement learning
(RL) tasks, with provable learning advantages over classical
systems, e.g., deep neural networks. While existing work
demonstrates and exploits the strength of PQC-based models, the
design choices of PQC architectures and the interactions between
different quantum circuits on learning tasks are generally
underexplored. In this work, we introduce a Multi-objective
Evolutionary Architecture Search framework for parameterized
quantum circuits (MEAS-PQC), which uses a multi-objective genetic
algorithm with quantum-specific configurations to perform efficient
searching of optimal PQC architectures. Experimental results show
that our method can find architectures that have superior learning
performance on three benchmark RL tasks, and are also optimized for
additional objectives including reductions in quantum noise and
model size. Further analysis of patterns and probability
distributions of quantum operations helps identify
performance-critical design choices of hybrid quantum-classical
learning systems.},
DOI = {10.3390/e25010093},
AUTHOR = {Ding, Li and Spector, Lee},
TITLE = {Multi-Objective Evolutionary Architecture Search for Parameterized
Quantum Circuits},
JOURNAL = {Entropy},
VOLUME = {25},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {93},
URL = {https://www.mdpi.com/1099-4300/25/1/93},
PubMedID = {36673234},
ISSN = {1099-4300},
ABSTRACT = {Recent work on hybrid quantum-classical machine learning systems
has demonstrated success in utilizing parameterized quantum
circuits (PQCs) to solve the challenging reinforcement learning
(RL) tasks, with provable learning advantages over classical
systems, e.g., deep neural networks. While existing work
demonstrates and exploits the strength of PQC-based models, the
design choices of PQC architectures and the interactions between
different quantum circuits on learning tasks are generally
underexplored. In this work, we introduce a Multi-objective
Evolutionary Architecture Search framework for parameterized
quantum circuits (MEAS-PQC), which uses a multi-objective genetic
algorithm with quantum-specific configurations to perform
efficient searching of optimal PQC architectures. Experimental
results show that our method can find architectures that have
superior learning performance on three benchmark RL tasks, and
are also optimized for additional objectives including reductions
in quantum noise and model size. Further analysis of patterns and
probability distributions of quantum operations helps identify
performance-critical design choices of hybrid quantum-classical
learning systems.},
DOI = {10.3390/e25010093},
}
@misc{generative-quantum-eigensolver,
title = {The generative quantum eigensolver (GQE) and its application for
ground state search},
author = {Kouhei Nakaji and Lasse Bjørn Kristensen and Ryota Kemmoku and Jorge
A. Campos-Gonzalez-Angulo and Mohammad Ghazi Vakili and Haozhe Huang
and Mohsen Bagherimehrab and Christoph Gorgulla and FuTe Wong and
Alex McCaskey and Jin-Sung Kim and Thien Nguyen and Pooja Rao and Qi
Gao and Michihiko Sugawara and Naoki Yamamoto and Alán Aspuru-Guzik},
year = {2025},
eprint = {2401.09253},
archivePrefix = {arXiv},
primaryClass = {quant-ph},
url = {https://arxiv.org/abs/2401.09253},
title = {The generative quantum eigensolver (GQE) and its application for
ground state search},
author = {Kouhei Nakaji and Lasse Bjørn Kristensen and Ryota Kemmoku and
Jorge A. Campos-Gonzalez-Angulo and Mohammad Ghazi Vakili and
Haozhe Huang and Mohsen Bagherimehrab and Christoph Gorgulla and
FuTe Wong and Alex McCaskey and Jin-Sung Kim and Thien Nguyen and
Pooja Rao and Qi Gao and Michihiko Sugawara and Naoki Yamamoto and
Alán Aspuru-Guzik},
year = {2025},
eprint = {2401.09253},
archivePrefix = {arXiv},
primaryClass = {quant-ph},
url = {https://arxiv.org/abs/2401.09253},
}
@inproceedings{calibration-aware-transpilation,
author = {Ji, Yanjun and Brandhofer, Sebastian and Polian, Ilia},
booktitle = {2022 IEEE International Conference on Quantum Computing and
Engineering (QCE)},
title = {Calibration-Aware Transpilation for Variational Quantum Optimization},
year = {2022},
volume = {},
number = {},
pages = {204-214},
keywords = {Computers;Quantum computing;Quantum algorithm;Program
processors;Error analysis;Logic
gates;Calibration;Calibration-Aware;Transpilation;NISQ;QAOA;Benchmarking;Quantum
Computing},
doi = {10.1109/QCE53715.2022.00040},
author = {Ji, Yanjun and Brandhofer, Sebastian and Polian, Ilia},
booktitle = {2022 IEEE International Conference on Quantum Computing and
Engineering (QCE)},
title = {Calibration-Aware Transpilation for Variational Quantum
Optimization},
year = {2022},
volume = {},
number = {},
pages = {204-214},
keywords = {Computers;Quantum computing;Quantum algorithm;Program
processors;Error analysis;Logic
gates;Calibration;Calibration-Aware;Transpilation;NISQ;QAOA;Benchmarking;Quantum
Computing},
doi = {10.1109/QCE53715.2022.00040},
}
@article{topology-driven-search,
author = {Su, Junjian and Fan, Jiacheng and Wu, Shengyao and Li, Guanghui and
Qin, Sujuan and Gao, Fei},
title = {Topology-driven quantum architecture search framework},
journal = {Science China Information Sciences},
year = {2025},
month = {Jul},
day = {03},
volume = {68},
number = {8},
pages = {180507},
abstract = {The limitations of noisy intermediate-scale quantum (NISQ) devices
have motivated the development of variational quantum algorithms
(VQAs), which are designed to potentially achieve quantum advantage
for specific tasks. Quantum architecture search (QAS) algorithms
play a critical role in automating the design of high-performance
parameterized quantum circuits (PQCs) for VQAs. However, existing
QAS approaches struggle with large search spaces, leading to
substantial computational overhead when optimizing large-scale
quantum circuits. Extensive empirical analysis reveals that circuit
topology has a greater impact on quantum circuit performance than
gate types. Based on this insight, we propose the topology-driven
quantum architecture search (TD-QAS) framework, which first
identifies optimal circuit topologies and then fine-tunes the gate
types. In the fine-tuning phase, the QAS inherits parameters from
the topology search phase, eliminating the need for training from
scratch. By decoupling the large search space into separate
topology and gate-type components, TD-QAS avoids exploring gate
configurations within low-performance topologies, thereby
significantly reducing computational complexity. Numerical
simulations across various tasks, under both noiseless and noisy
conditions, validate the effectiveness of the TD-QAS framework.
This framework advances standard QAS algorithms by enabling the
identification of high-performance quantum circuits while
minimizing computational demands. These findings indicate that
TD-QAS deepens our understanding of VQAs and offers broad potential
for the development of future QAS algorithms.},
issn = {1869-1919},
doi = {10.1007/s11432-024-4486-x},
url = {https://doi.org/10.1007/s11432-024-4486-x},
author = {Su, Junjian and Fan, Jiacheng and Wu, Shengyao and Li, Guanghui
and Qin, Sujuan and Gao, Fei},
title = {Topology-driven quantum architecture search framework},
journal = {Science China Information Sciences},
year = {2025},
month = {Jul},
day = {03},
volume = {68},
number = {8},
pages = {180507},
abstract = {The limitations of noisy intermediate-scale quantum (NISQ)
devices have motivated the development of variational quantum
algorithms (VQAs), which are designed to potentially achieve
quantum advantage for specific tasks. Quantum architecture search
(QAS) algorithms play a critical role in automating the design of
high-performance parameterized quantum circuits (PQCs) for VQAs.
However, existing QAS approaches struggle with large search
spaces, leading to substantial computational overhead when
optimizing large-scale quantum circuits. Extensive empirical
analysis reveals that circuit topology has a greater impact on
quantum circuit performance than gate types. Based on this
insight, we propose the topology-driven quantum architecture
search (TD-QAS) framework, which first identifies optimal circuit
topologies and then fine-tunes the gate types. In the fine-tuning
phase, the QAS inherits parameters from the topology search phase
, eliminating the need for training from scratch. By decoupling
the large search space into separate topology and gate-type
components, TD-QAS avoids exploring gate configurations within
low-performance topologies, thereby significantly reducing
computational complexity. Numerical simulations across various
tasks, under both noiseless and noisy conditions, validate the
effectiveness of the TD-QAS framework. This framework advances
standard QAS algorithms by enabling the identification of
high-performance quantum circuits while minimizing computational
demands. These findings indicate that TD-QAS deepens our
understanding of VQAs and offers broad potential for the
development of future QAS algorithms.},
issn = {1869-1919},
doi = {10.1007/s11432-024-4486-x},
url = {https://doi.org/10.1007/s11432-024-4486-x},
}
@article{Hirsbrunner2024beyondmp,
doi = {10.22331/q-2024-11-26-1538},
url = {https://doi.org/10.22331/q-2024-11-26-1538},
title = {Beyond {MP}2 initialization for unitary coupled cluster quantum circuits},
author = {Hirsbrunner, Mark R. and Chamaki, Diana and Mullinax, J. Wayne and Tubman, Norm M.},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
volume = {8},
pages = {1538},
month = nov,
year = {2024}
doi = {10.22331/q-2024-11-26-1538},
url = {https://doi.org/10.22331/q-2024-11-26-1538},
title = {Beyond {MP}2 initialization for unitary coupled cluster quantum
circuits},
author = {Hirsbrunner, Mark R. and Chamaki, Diana and Mullinax, J. Wayne and
Tubman, Norm M.},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
Quantenwissenschaften}},
volume = {8},
pages = {1538},
month = nov,
year = {2024},
}
@misc{liu2025haqgnnhardwareawarequantumkernel,
title={HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural Networks},
author={Yuxiang Liu and Fanxu Meng and Lu Wang and Yi Hu and Sixuan Li and Xutao Yu and Zaichen Zhang},
year={2025},
eprint={2506.21161},
archivePrefix={arXiv},
primaryClass={quant-ph},
url={https://arxiv.org/abs/2506.21161},
title = {HaQGNN: Hardware-Aware Quantum Kernel Design Based on Graph Neural
Networks},
author = {Yuxiang Liu and Fanxu Meng and Lu Wang and Yi Hu and Sixuan Li and
Xutao Yu and Zaichen Zhang},
year = {2025},
eprint = {2506.21161},
archivePrefix = {arXiv},
primaryClass = {quant-ph},
url = {https://arxiv.org/abs/2506.21161},
}
@article{training-free,
title={Training-Free Quantum Architecture Search},
volume={38},
url={https://ojs.aaai.org/index.php/AAAI/article/view/29135},
DOI={10.1609/aaai.v38i11.29135},
abstractNote={Variational quantum algorithm (VQA) derives advantages from its error resilience and high flexibility in quantum resource requirements, rendering it broadly applicable in the noisy intermediate-scale quantum era. As the performance of VQA highly relies on the structure of the parameterized quantum circuit, it is worthwhile to propose quantum architecture search (QAS) algorithms to automatically search for high-performance circuits. Nevertheless, existing QAS methods are time-consuming, requiring circuit training to assess circuit performance. This study pioneers training-free QAS by utilizing two training-free proxies to rank quantum circuits, in place of the expensive circuit training employed in conventional QAS. Taking into account the precision and computational overhead of the path-based and expressibility-based proxies, we devise a two-stage progressive training-free QAS (TF-QAS). Initially, directed acyclic graphs (DAGs) are employed for circuit representation, and a zero-cost proxy based on the number of paths in the DAG is designed to filter out a substantial portion of unpromising circuits. Subsequently, an expressibility-based proxy, finely reflecting circuit performance, is employed to identify high-performance circuits from the remaining candidates. These proxies evaluate circuit performance without circuit training, resulting in a remarkable reduction in computational cost compared to current training-based QAS methods. Simulations on three VQE tasks demonstrate that TF-QAS achieves a substantial enhancement of sampling efficiency ranging from 5 to 57 times compared to state-of-the-art QAS, while also being 6 to 17 times faster.},
number={11},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={He, Zhimin and Deng, Maijie and Zheng, Shenggen and Li, Lvzhou and Situ, Haozhen},
year={2024},
month={Mar.},
pages={12430-12438}
title = {Training-Free Quantum Architecture Search},
volume = {38},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/29135},
DOI = {10.1609/aaai.v38i11.29135},
abstractNote = {Variational quantum algorithm (VQA) derives advantages from
its error resilience and high flexibility in quantum resource
requirements, rendering it broadly applicable in the noisy
intermediate-scale quantum era. As the performance of VQA
highly relies on the structure of the parameterized quantum
circuit, it is worthwhile to propose quantum architecture
search (QAS) algorithms to automatically search for
high-performance circuits. Nevertheless, existing QAS methods
are time-consuming, requiring circuit training to assess
circuit performance. This study pioneers training-free QAS by
utilizing two training-free proxies to rank quantum circuits,
in place of the expensive circuit training employed in
conventional QAS. Taking into account the precision and
computational overhead of the path-based and
expressibility-based proxies, we devise a two-stage
progressive training-free QAS (TF-QAS). Initially, directed
acyclic graphs (DAGs) are employed for circuit representation
, and a zero-cost proxy based on the number of paths in the
DAG is designed to filter out a substantial portion of
unpromising circuits. Subsequently, an expressibility-based
proxy, finely reflecting circuit performance, is employed to
identify high-performance circuits from the remaining
candidates. These proxies evaluate circuit performance
without circuit training, resulting in a remarkable reduction
in computational cost compared to current training-based QAS
methods. Simulations on three VQE tasks demonstrate that
TF-QAS achieves a substantial enhancement of sampling
efficiency ranging from 5 to 57 times compared to
state-of-the-art QAS, while also being 6 to 17 times faster.},
number = {11},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
author = {He, Zhimin and Deng, Maijie and Zheng, Shenggen and Li, Lvzhou and
Situ, Haozhen},
year = {2024},
month = {Mar.},
pages = {12430-12438},
}
@article{npqas,
doi = {10.1088/2632-2153/ac28dd},
url = {https://doi.org/10.1088/2632-2153/ac28dd},
year = {2021},
month = {oct},
publisher = {IOP Publishing},
volume = {2},
number = {4},
pages = {045027},
author = {Zhang, Shi-Xin and Hsieh, Chang-Yu and Zhang, Shengyu and Yao,
Hong},
title = {Neural predictor based quantum architecture search},
journal = {Machine Learning: Science and Technology},
abstract = {Variational quantum algorithms (VQAs) are widely speculated to
deliver quantum advantages for practical problems under the
quantum–classical hybrid computational paradigm in the near term.
Both theoretical and practical developments of VQAs share many
similarities with those of deep learning. For instance, a key
component of VQAs is the design of task-dependent parameterized
quantum circuits (PQCs) as in the case of designing a good neural
architecture in deep learning. Partly inspired by the recent
success of AutoML and neural architecture search (NAS), quantum
architecture search (QAS) is a collection of methods devised to
engineer an optimal task-specific PQC. It has been proven that
QAS-designed VQAs can outperform expert-crafted VQAs in various
scenarios. In this work, we propose to use a neural network based
predictor as the evaluation policy for QAS. We demonstrate a
neural predictor guided QAS can discover powerful quantum circuit
ansatz, yielding state-of-the-art results for various examples
from quantum simulation and quantum machine learning. Notably,
neural predictor guided QAS provides a better solution than that
by the random-search baseline while using an order of magnitude
less of circuit evaluations. Moreover, the predictor for QAS as
well as the optimal ansatz found by QAS can both be transferred
and generalized to address similar problems.},
}
@article{hea-kandala,
author = {Kandala, Abhinav and Mezzacapo, Antonio and Temme, Kristan and
Takita, Maika and Brink, Markus and Chow, Jerry M. and Gambetta,
Jay M.},
title = {Hardware-efficient variational quantum eigensolver for small
molecules and quantum magnets},
journal = {Nature},
year = {2017},
month = {Sep},
day = {01},
volume = {549},
number = {7671},
pages = {242-246},
abstract = {The ground-state energy of small molecules is determined
efficiently using six qubits of a superconducting quantum
processor.},
issn = {1476-4687},
doi = {10.1038/nature23879},
url = {https://doi.org/10.1038/nature23879},
}
@article{tensorcircuit,
doi = {10.22331/q-2023-02-02-912},
url = {https://doi.org/10.22331/q-2023-02-02-912},
title = {Tensor{C}ircuit: a {Q}uantum {S}oftware {F}ramework for the {NISQ}
{E}ra},
author = {Zhang, Shi-Xin and Allcock, Jonathan and Wan, Zhou-Quan and Liu,
Shuo and Sun, Jiace and Yu, Hao and Yang, Xing-Han and Qiu,
Jiezhong and Ye, Zhaofeng and Chen, Yu-Qin and Lee, Chee-Kong and
Zheng, Yi-Cong and Jian, Shao-Kai and Yao, Hong and Hsieh, Chang-Yu
and Zhang, Shengyu},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den
Quantenwissenschaften}},
volume = {7},
pages = {912},
month = feb,
year = {2023},
}
@misc{genetic-expressibility,
title = {Genetic optimization of ansatz expressibility for enhanced
variational quantum algorithm performance},
author = {Manish Mallapur and Ronit Raj and Ankur Raina},
year = {2025},
eprint = {2509.05804},
archivePrefix = {arXiv},
primaryClass = {quant-ph},
url = {https://arxiv.org/abs/2509.05804},
}