scholarly journals Full quantum state tomography of high-dimensional on-chip biphoton frequency combs with randomized measurements

Author(s):  
Hsuan-Hao Lu ◽  
Karthik Myilswamy ◽  
Ryan Bennink ◽  
Suparna Seshadri ◽  
Mohammed Alshaykh ◽  
...  

Abstract Owing in large part to the advent of integrated biphoton frequency combs (BFCs), 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 (QST) 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 novel solution that employs a pulse shaper and electro-optic phase modulator (EOM) to perform random operations instead of mixing in a prescribed manner. Incorporating state-of-the-art Bayesian statistical method, we successfully verify the entanglement and reconstruct the full density matrix of BFCs generated from an on-chip Si3N4 microring resonator (MRR) in up to an 8×8-dimensional two-qudit Hilbert space, the highest dimension to date for frequency bins. Overall, our method furnishes an experimentally powerful approach for frequency-bin tomography with readily implementable operations.

2021 ◽  
Author(s):  
Xiang Cheng ◽  
Zhenda Xie ◽  
Kai-Chi Chang ◽  
Murat Can Sarihan ◽  
Yoo Seung Lee ◽  
...  

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
James G. Titchener ◽  
Markus Gräfe ◽  
René Heilmann ◽  
Alexander S. Solntsev ◽  
Alexander Szameit ◽  
...  

Author(s):  
James Titchener ◽  
Markus Grafe ◽  
Rene Heilmann ◽  
Alexander S. Solntsev ◽  
Alexander Szameit ◽  
...  

Author(s):  
James Titchener ◽  
Markus Gräfe ◽  
René Heilmann ◽  
Alexander S. Solntsev ◽  
Alexander Szameit ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yihui Quek ◽  
Stanislav Fort ◽  
Hui Khoon Ng

AbstractCurrent algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.


Heliyon ◽  
2021 ◽  
pp. e07384
Author(s):  
Ali Motazedifard ◽  
S.A. Madani ◽  
J.J. Dashkasan ◽  
N.S. Vayaghan

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