PIRSA:26090042 Scientific Series Other

DOI10.48660/26090042

Series: Colloquium

Zhou, S. (2026). Reshaping Metrology with Quantum Information Processing. Perimeter Institute. https://pirsa.org/26090042

Zhou, Sisi. Reshaping Metrology with Quantum Information Processing. Perimeter Institute, Sep. 30, 2026, https://pirsa.org/26090042 

@misc{ pirsa_PIRSA:26090042,
  doi = {10.48660/26090042},
  url = {https://pirsa.org/26090042},
  author = {Zhou, Sisi},
  keywords = {Other},
  language = {en},
  title = {Reshaping Metrology with Quantum Information Processing},
  publisher = {Perimeter Institute},
  year = {2026},
  month = {sep},
  note = {PIRSA:26090042 see, \url{https://pirsa.org}}
}

Abstract

Quantum metrology studies how precisely physical parameters can be estimated using quantum systems. In this talk, I will illustrate how modern quantum technologies, including quantum error correction and quantum learning, can help approach the ultimate limits of quantum sensing and reshape the design of quantum sensors. I will first discuss the role of quantum error correction in metrology, beginning with simple qubit examples that show how certain types of noise can be suppressed, while others are fundamentally indistinguishable from the signal and cannot be removed. I will then extend these ideas to multi-qubit settings, with applications to phase estimation, many-body sensing, and distributed quantum sensing. In the second part, I will explain how techniques from quantum learning, including tomography, certification, and purification, can be applied to multi-parameter quantum metrology. Multi-parameter estimation is ubiquitous but fundamentally challenging because different parameters generally cannot all be estimated simultaneously, as reflected by the uncertainty principle. This makes optimal measurement design both conceptually and experimentally difficult. Inspired by recent advances in quantum learning, I will present measurement protocols for several multi-parameter sensing problems that are efficiently implementable while remaining near-optimal in sensitivity. 

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