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Bayesian machine learning of frequency-bin CNOT

Conference ·
 [1];  [2];  [2];  [1];  [2];  [1];  [2]
  1. Purdue University
  2. ORNL
+ Show Author Affiliations

We analyze the first experimental two-photon frequency-bin gate: a coincidence-basis CNOT. A novel characterization approach based on Bayesian machine learning is developed to estimate the gate performance with measurements in the logical basis alone.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-00OR22725
OSTI ID:
1513376
Country of Publication:
United States
Language:
English

References (8)

Ramsey Interference with Single Photons journal November 2016
Electro-Optic Frequency Beam Splitters and Tritters for High-Fidelity Photonic Quantum Information Processing journal January 2018
Quantum phase gate for photonic qubits using only beam splitters and postselection journal August 2002
Direct characterization of linear-optical networks journal January 2013
Quantum interference and correlation control of frequency-bin qubits journal January 2018
Linear optical controlled-NOT gate in the coincidence basis journal June 2002
Frequency-encoded photonic qubits for scalable quantum information processing journal December 2016
Frequency-domain Hong–Ou–Mandel interference journal April 2016

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