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Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements

Author

Listed:
  • Hsuan-Hao Lu

    (Quantum Information Science Section, Oak Ridge National Laboratory
    Purdue University)

  • Karthik V. Myilswamy

    (Purdue University)

  • Ryan S. Bennink

    (Quantum Information Science Section, Oak Ridge National Laboratory)

  • Suparna Seshadri

    (Purdue University)

  • Mohammed S. Alshaykh

    (Purdue University
    King Saud University)

  • Junqiu Liu

    (Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL))

  • Tobias J. Kippenberg

    (Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL))

  • Daniel E. Leaird

    (Purdue University
    Torch Technologies, supporting AFRL/RW)

  • Andrew M. Weiner

    (Purdue University)

  • Joseph M. Lukens

    (Quantum Information Science Section, Oak Ridge National Laboratory)

Abstract

Owing in large part to the advent of integrated biphoton frequency combs, recent years have witnessed increased attention to quantum information processing in the frequency domain for its inherent high dimensionality and entanglement compatible with fiber-optic networks. Quantum state tomography of such states, however, has required complex and precise engineering of active frequency mixing operations, which are difficult to scale. To address these limitations, we propose a solution that employs a pulse shaper and electro-optic phase modulator to perform random operations instead of mixing in a prescribed manner. We successfully verify the entanglement and reconstruct the full density matrix of biphoton frequency combs generated from an on-chip Si3N4 microring resonator in up to an 8 × 8-dimensional two-qudit Hilbert space, the highest dimension to date for frequency bins. More generally, our employed Bayesian statistical model can be tailored to a variety of quantum systems with restricted measurement capabilities, forming an opportunistic tomographic framework that utilizes all available data in an optimal way.

Suggested Citation

  • Hsuan-Hao Lu & Karthik V. Myilswamy & Ryan S. Bennink & Suparna Seshadri & Mohammed S. Alshaykh & Junqiu Liu & Tobias J. Kippenberg & Daniel E. Leaird & Andrew M. Weiner & Joseph M. Lukens, 2022. "Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31639-z
    DOI: 10.1038/s41467-022-31639-z
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    References listed on IDEAS

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    1. Michael Kues & Christian Reimer & Piotr Roztocki & Luis Romero Cortés & Stefania Sciara & Benjamin Wetzel & Yanbing Zhang & Alfonso Cino & Sai T. Chu & Brent E. Little & David J. Moss & Lucia Caspani , 2017. "On-chip generation of high-dimensional entangled quantum states and their coherent control," Nature, Nature, vol. 546(7660), pages 622-626, June.
    2. Junqiu Liu & Guanhao Huang & Rui Ning Wang & Jijun He & Arslan S. Raja & Tianyi Liu & Nils J. Engelsen & Tobias J. Kippenberg, 2021. "High-yield, wafer-scale fabrication of ultralow-loss, dispersion-engineered silicon nitride photonic circuits," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    3. Zijiao Yang & Mandana Jahanbozorgi & Dongin Jeong & Shuman Sun & Olivier Pfister & Hansuek Lee & Xu Yi, 2021. "A squeezed quantum microcomb on a chip," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
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