Skip to content
DraconiticResearch
Search Sources API
201,683 studies31 sourcesUpdated 3 min ago
PreprintCC BY 4.0arXiv

Block-Wise Variational Quantum Algorithms for PDEs with Interface Penalty Constraints

Abstract

Global variational quantum algorithm (VQA) frameworks for solving partial differential equations (PDEs) often rely on a single expressive ansatz over a uniform grid, which becomes structurally inefficient when solutions exhibit spatially heterogeneous complexity such as localized singularities or thin boundary layers. A localized nonsmooth feature can degrade the convergence of the entire global quantum representation, imposing unnecessary circuit depth and amplifying the risk of vanishing gradients in barren plateaus. To overcome this limitation, we propose a block-wise VQA framework for PDEs characterized by spatially heterogeneous solution complexity. The computational domain is decomposed into locally represented quantum subproblems, where grid budgets and ansatz families are dynamically assigned based on a computable difficulty indicator. Artificial block interfaces are coupled through unified jump penalties for state values, derivatives, and physical fluxes, while adaptive reblocking tracks moving rough regions to maintain local accuracy without excessive global qubit overhead. The error analysis rigorously separates spatial discretization, ansatz expressivity, optimization convergence, finite-shot sampling noise, transfer errors from reblocking, and interface-coupling contributions. Reproducible residual-based simulations on representative elliptic, advection--diffusion, and Burgers problems demonstrate lower approximation errors and reduced peak local circuit width compared to global VQA approaches. These results substantiate block-wise quantum resource localization, confirming that high-fidelity PDE solutions can be achieved on near-term quantum devices without requiring a single globally expressive ansatz.

Related

  1. Accelerating noisy variational quantum algorithms with physics-informed denoising networks
    Jie Liu, Xin Wang
    Physical Review ResearchPeer-reviewedPublisher termsAtomic, molecular & optical physicsCrossref
  2. Scaling law of variational quantum algorithms
    Ningfeng Wang, Zhengwei Liu
    arXivPreprintarXiv licenceFull textQuantum physics & quantum informationarXiv
  3. A Case Study on Noise Resilient Operator Selection in Adaptive Variational Quantum Algorithms
    Soorya Haravu, Mafalda Ramôa, Bharath Sambasivam
    arXivPreprintCC BY 4.0Full textQuantum physics & quantum informationarXiv
  4. Aspects of Symmetry in Nuclear Quantum Computing Variational Algorithms
    Paul Stevenson, Chandan Sarma, Lloyd La Ronde +3
    SymmetryPeer-reviewedCC BYNuclear physicsQuantum physics & quantum informationHAL
  5. Training Variational Quantum Algorithms Is NP-Hard, Even Locally
    Dax Enshan Koh, Triscia Mundo, Iosif Sakos +1
    arXivPreprintarXiv licenceFull textOptimisation & controlQuantum physics & quantum informationarXiv
BibTeX
@misc{jie2026blockwise,
  title = {{Block-Wise Variational Quantum Algorithms for PDEs with Interface Penalty Constraints}},
  author = {Hangran Jie and Yuntao Cui and Sunho Kim},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.36710},
  eprint = {2609.36710},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.36710},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Block-Wise Variational Quantum Algorithms for PDEs with Interface Penalty Constraints
AU  - Hangran Jie
AU  - Yuntao Cui
AU  - Sunho Kim
PY  - 2026
DA  - 2026/09/29
PB  - arXiv
DO  - 10.48550/arxiv.2609.36710
UR  - https://arxiv.org/abs/2609.36710
AB  - Global variational quantum algorithm (VQA) frameworks for solving partial differential equations (PDEs) often rely on a single expressive ansatz over a uniform grid, which becomes structurally inefficient when solutions exhibit spatially heterogeneous complexity such as localized singularities or thin boundary layers. A localized nonsmooth feature can degrade the convergence of the entire global quantum representation, imposing unnecessary circuit depth and amplifying the risk of vanishing gradients in barren plateaus. To overcome this limitation, we propose a block-wise VQA framework for PDEs characterized by spatially heterogeneous solution complexity. The computational domain is decomposed into locally represented quantum subproblems, where grid budgets and ansatz families are dynamically assigned based on a computable difficulty indicator. Artificial block interfaces are coupled through unified jump penalties for state values, derivatives, and physical fluxes, while adaptive reblocking tracks moving rough regions to maintain local accuracy without excessive global qubit overhead. The error analysis rigorously separates spatial discretization, ansatz expressivity, optimization convergence, finite-shot sampling noise, transfer errors from reblocking, and interface-coupling contributions. Reproducible residual-based simulations on representative elliptic, advection--diffusion, and Burgers problems demonstrate lower approximation errors and reduced peak local circuit width compared to global VQA approaches. These results substantiate block-wise quantum resource localization, confirming that high-fidelity PDE solutions can be achieved on near-term quantum devices without requiring a single globally expressive ansatz.
ER  -