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