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.

Full tomography of biphoton frequency comb states requires frequency mixing operations which are hard to scale. Here, the authors propose and demonstrate a protocol exploiting advanced Bayesian statistical methods and randomized measurements coming from complex mode mixing in electro-optic phase modulators.

Details

Title
Bayesian tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements
Author
Lu, Hsuan-Hao 1   VIAFID ORCID Logo  ; Myilswamy, Karthik V. 2   VIAFID ORCID Logo  ; Bennink, Ryan S. 3 ; Seshadri, Suparna 2   VIAFID ORCID Logo  ; Alshaykh, Mohammed S. 4   VIAFID ORCID Logo  ; Liu, Junqiu 5   VIAFID ORCID Logo  ; Kippenberg, Tobias J. 5   VIAFID ORCID Logo  ; Leaird, Daniel E. 6   VIAFID ORCID Logo  ; Weiner, Andrew M. 2   VIAFID ORCID Logo  ; Lukens, Joseph M. 3   VIAFID ORCID Logo 

 Quantum Information Science Section, Oak Ridge National Laboratory, Oak Ridge, USA (GRID:grid.135519.a) (ISNI:0000 0004 0446 2659); Purdue University, School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197) 
 Purdue University, School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197) 
 Quantum Information Science Section, Oak Ridge National Laboratory, Oak Ridge, USA (GRID:grid.135519.a) (ISNI:0000 0004 0446 2659) 
 Purdue University, School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197); King Saud University, Electrical Engineering Department, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396) 
 Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland (GRID:grid.5333.6) (ISNI:0000000121839049) 
 Purdue University, School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute, West Lafayette, USA (GRID:grid.169077.e) (ISNI:0000 0004 1937 2197); Torch Technologies, supporting AFRL/RW, Shalimar, USA (GRID:grid.456287.a) (ISNI:0000 0004 0507 0307) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2695309133
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.