scholarly journals TNQMetro: Tensor-network based package for efficient quantum metrology computations

2022 ◽  
pp. 108282
Author(s):  
Krzysztof Chabuda ◽  
Rafał Demkowicz-Dobrzański
Keyword(s):  
Quantum Metrology ◽  
Tensor Network

scholarly journals Tensor-network approach for quantum metrology in many-body quantum systems

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Krzysztof Chabuda ◽  
Jacek Dziarmaga ◽  
Tobias J. Osborne ◽  
Rafał Demkowicz-Dobrzański
Keyword(s):  
Quantum Systems ◽  
Quantum Metrology ◽  
Network Approach ◽  
Many Body ◽  
Tensor Network

scholarly journals Distributed quantum metrology with a single squeezed-vacuum source

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
Dario Gatto ◽  
Paolo Facchi ◽  
Frank A. Narducci ◽  
Vincenzo Tamma
Keyword(s):  
Quantum Metrology ◽  
Squeezed Vacuum

scholarly journals Multiparameter quantum metrology with postselection measurements

2021 ◽  
Vol 62 (1) ◽  
pp. 012102
Author(s):  
Le Bin Ho ◽  
Yasushi Kondo
Keyword(s):  
Quantum Metrology

scholarly journals Dyonic objects and tensor network representation

2020 ◽  
pp. 2050336
Author(s):  
A. Belhaj ◽  
Y. El Maadi ◽  
S-E. Ennadifi ◽  
Y. Hassouni ◽  
M. B. Sedra
Keyword(s):  
Particle Physics ◽  
Network Representation ◽  
Tensor Network ◽  
Arbitrary Dimensions

Motivated by particle physics results, we investigate certain dyonic solutions in arbitrary dimensions. Concretely, we study the stringy constructions of such objects from concrete compactifications. Then, we elaborate their tensor network realizations using multistate particle formalism.

scholarly journals A variational toolbox for quantum multi-parameter estimation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Johannes Jakob Meyer ◽  
Johannes Borregaard ◽  
Jens Eisert
Keyword(s):  
Parameter Estimation ◽  
Information Science ◽  
Quantum Algorithms ◽  
Continuous Variable ◽  
General Interest ◽  
Quantum Metrology ◽  
Estimation Problems ◽  
General Parameter ◽  
Quantum Technology ◽  
Aided Design

AbstractWith an ever-expanding ecosystem of noisy and intermediate-scale quantum devices, exploring their possible applications is a rapidly growing field of quantum information science. In this work, we demonstrate that variational quantum algorithms feasible on such devices address a challenge central to the field of quantum metrology: The identification of near-optimal probes and measurement operators for noisy multi-parameter estimation problems. We first introduce a general framework that allows for sequential updates of variational parameters to improve probe states and measurements and is widely applicable to both discrete and continuous-variable settings. We then demonstrate the practical functioning of the approach through numerical simulations, showcasing how tailored probes and measurements improve over standard methods in the noisy regime. Along the way, we prove the validity of a general parameter-shift rule for noisy evolutions, expected to be of general interest in variational quantum algorithms. In our approach, we advocate the mindset of quantum-aided design, exploiting quantum technology to learn close to optimal, experimentally feasible quantum metrology protocols.

scholarly journals Constructing tensor network influence functionals for general quantum dynamics

2021 ◽  
Vol 155 (4) ◽  
pp. 044104
Author(s):  
Erika Ye ◽  
Garnet Kin-Lic Chan
Keyword(s):  
Quantum Dynamics ◽  
General Quantum ◽  
Tensor Network

scholarly journals Quantum Divide and Compute: Exploring the Effect of Different Noise Sources

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Thomas Ayral ◽  
François-Marie Le Régent ◽  
Zain Saleem ◽  
Yuri Alexeev ◽  
Martin Suchara
Keyword(s):  
Success Probability ◽  
Quantum Circuit ◽  
Noise Sources ◽  
Output Distribution ◽  
Tensor Network ◽  
Learning Machine ◽  
Society Annual ◽  
The Impact ◽  
Noise Models ◽  
Detailed Derivation

AbstractOur recent work (Ayral et al. in Proceedings of IEEE computer society annual symposium on VLSI, ISVLSI, pp 138–140, 2020. 10.1109/ISVLSI49217.2020.00034) showed the first implementation of the Quantum Divide and Compute (QDC) method, which allows to break quantum circuits into smaller fragments with fewer qubits and shallower depth. This accommodates the limited number of qubits and short coherence times of quantum processors. This article investigates the impact of different noise sources—readout error, gate error and decoherence—on the success probability of the QDC procedure. We perform detailed noise modeling on the Atos Quantum Learning Machine, allowing us to understand tradeoffs and formulate recommendations about which hardware noise sources should be preferentially optimized. We also describe in detail the noise models we used to reproduce experimental runs on IBM’s Johannesburg processor. This article also includes a detailed derivation of the equations used in the QDC procedure to compute the output distribution of the original quantum circuit from the output distribution of its fragments. Finally, we analyze the computational complexity of the QDC method for the circuit under study via tensor-network considerations, and elaborate on the relation the QDC method with tensor-network simulation methods.

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