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

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 27, 2022
Source ID
10.1038/s41467-022-31639-z

Entities

People

  • Andrew M. Weiner
  • Daniel E Leaird
  • Hsuan-Hao Lu
  • Joseph M Lukens
  • Junqiu Liu
  • Karthik V. Myilswamy
  • Mohammed S Alshaykh
  • Ryan S. Bennink
  • Suparna Seshadri
  • Tobias Kippenberg

Organizations

  • Air Force Office of Scientific Research
  • National Science Foundation
  • Swiss National Science Foundation
  • United States Department of Energy

Tags

Fields of Study

  • Physics

Readers

  • Neural Network Machine Learning.
  • Optical Physics and Photonics.
  • Phased Array Antenna Design.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Quantum Computing
  • Space