Applications of photon-number-resolving measurements for quantum interferometry and quantum state tomography (Conference Presentation)

2017 ◽  
Author(s):  
Olivier Pfister ◽  
Aye Win ◽  
Rajveer Nehra ◽  
Sae-Woo Nam ◽  
Thomas Gerrits
Keyword(s):  
Quantum State ◽  
Photon Number ◽  
Quantum State Tomography ◽  
Quantum Interferometry ◽  
State Tomography

scholarly journals State-independent quantum state tomography by photon-number-resolving measurements

Optica ◽  
2019 ◽  
Vol 6 (10) ◽  
pp. 1356 ◽  
Author(s):  
Rajveer Nehra ◽  
Aye Win ◽  
Miller Eaton ◽  
Reihaneh Shahrokhshahi ◽  
Niranjan Sridhar ◽  
...  
Keyword(s):  
Quantum State ◽  
Photon Number ◽  
Quantum State Tomography ◽  
State Tomography

scholarly journals Adaptive quantum state tomography with neural networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yihui Quek ◽  
Stanislav Fort ◽  
Hui Khoon Ng
Keyword(s):  
Machine Learning ◽  
Quantum State ◽  
The State ◽  
Learning To Learn ◽  
Seamless Integration ◽  
Quantum State Tomography ◽  
Flexible Machine ◽  
Meta Learning ◽  
Long Time ◽  
State Tomography

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.

scholarly journals Nonlocal realism tests and quantum state tomography in Sagnac-based type-II polarization-entanglement SPDC-source

Heliyon ◽  
2021 ◽  
pp. e07384
Author(s):  
Ali Motazedifard ◽  
S.A. Madani ◽  
J.J. Dashkasan ◽  
N.S. Vayaghan
Keyword(s):  
Quantum State ◽  
Type Ii ◽  
Quantum State Tomography ◽  
State Tomography
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