Sirui Lu he/him

@article{Lu2021Algorithms,
  title = {Algorithms for Quantum Simulation at Finite Energies},
  author = {Lu, Sirui and Ba{\~n}uls, Mari Carmen and Cirac, J. Ignacio},
  year = 2021,
  month = may,
  journal = {PRX Quantum},
  volume = {2},
  number = {2},
  pages = {020321},
  doi = {10.1103/prxquantum.2.020321}
}

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We introduce two kinds of quantum algorithms to explore microcanonical and canonical properties of many-body systems. The first is a hybrid quantum algorithm that, given an efficiently preparable state, computes expectation values in a finite energy interval around its mean energy, using a filtering operator similar to quantum phase estimation and recovering physical values through interferometric measurements. Its computational time scales polynomially with the number of qubits, the inverse variance, and the inverse error, and it does not require long-time evolution. The second is a quantum-assisted Monte Carlo sampling method for microcanonical and canonical ensemble quantities that circumvents the sign problem of classical quantum Monte Carlo. Both can run on small quantum computers and analog quantum simulators.

@misc{Lu2026Multi,
  title = {Multi-agent Autoformalization of Tensor Network Theory},
  author = {Lu, Sirui and Tjoa, Erickson and Cirac, J. Ignacio},
  year = 2026,
  month = jul,
  eprint = {2607.07857},
  archiveprefix = {arXiv},
  primaryclass = {quant-ph}
}

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We build a team of specialized large language-model agents and present an agent-driven workflow for research-level formalization in theoretical physics, with the autoformalization of the fundamental theorem of matrix-product states as a demonstration. The agents, coordinated through a structured mathematical blueprint and periodic human review, orchestrated and executed the full formalization autonomously. For some statements, the agents were able to explore new proof routes that are not part of the standard literature. Along the way the agents produced extensive tensor-network and quantum-information libraries not previously available in Mathlib, Lean's mathematical library. As a physical application, the formalization also extends towards symmetry-protected topological phases in one dimension. We find that the main bottleneck in large-scale autoformalization is enforcing mathematical intent and we provide a detailed study of the full process and various subtleties involved. We release the codebase as the library TNLean, together with a chapter-by-chapter blueprint of the formalization effort.

@misc{Lu2025Can,
  title = {Can Theoretical Physics Research Benefit from Language Agents?},
  author = {Lu, Sirui and Jin, Zhijing and Zhang, Terry Jingchen and Kos, Pavel and Cirac, J. Ignacio and Sch{\"o}lkopf, Bernhard},
  year = 2026,
  month = jun,
  eprint = {2506.06214},
  archiveprefix = {arXiv},
  primaryclass = {cs.CL}
}

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Large Language Models (LLMs) are rapidly advancing across diverse domains, yet their application in theoretical physics remains inadequate. While current models show competence in mathematical reasoning and code generation, we identify critical gaps in physical intuition, constraint satisfaction, and reliable reasoning that cannot be addressed through prompting alone. Physics demands approximation judgment, symmetry exploitation, and physical grounding that require AI agents specifically trained on physics reasoning patterns and equipped with physics-aware verification tools. We envision physics-specialized AI agents that handle multimodal data, propose physically consistent hypotheses, and autonomously verify theoretical results, and we call for collaborative efforts between physics and AI communities to build the specialized infrastructure necessary for AI-driven scientific discovery.

@article{Lu2025Tensor,
  title = {Tensor Networks and Efficient Descriptions of Classical Data},
  author = {Lu, Sirui and {Kan{\'a}sz-Nagy}, M{\'a}rton and Kukuljan, Ivan and Cirac, J. Ignacio},
  year = 2025,
  month = mar,
  journal = {Phys. Rev. A},
  volume = {111},
  number = {3},
  pages = {032409},
  doi = {10.1103/PhysRevA.111.032409}
}

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We investigate the potential of tensor network based machine learning methods to scale to large image and text data sets. For that, we study how the mutual information between a subregion and its complement scales with the subsystem size L, similarly to how it is done in quantum many-body physics. We find that for text, the mutual information scales as a power law with a close to volume law exponent, indicating that text cannot be efficiently described by 1D tensor networks. For images, the scaling is close to an area law, hinting that 2D tensor networks such as PEPS could have adequate expressibility. For the numerical analysis, we introduce a mutual information estimator based on autoregressive networks, and we also use convolutional neural networks in a neural estimator method.