scholarly journals Optimization schemes for unitary tensor-network circuit

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Reza Haghshenas
Keyword(s):  
Tensor Network ◽  
Unitary Tensor ◽  
Optimization Schemes

scholarly journals Machine learning by unitary tensor network of hierarchical tree structure

2019 ◽  
Vol 21 (7) ◽  
pp. 073059 ◽  
Author(s):  
Ding Liu ◽  
Shi-Ju Ran ◽  
Peter Wittek ◽  
Cheng Peng ◽  
Raul Blázquez García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tree Structure ◽  
Hierarchical Tree ◽  
Tensor Network ◽  
Unitary Tensor ◽  
Hierarchical Tree Structure

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.

Investigation and Optimization of the Effects of Geologic Parameters on the Performance of Gravity-Stable Surfactant Floods

SPE Journal ◽  
2016 ◽  
Vol 21 (03) ◽  
pp. 761-775 ◽  
Author(s):  
Shayan Tavassoli ◽  
Gary A. Pope ◽  
Kamy Sepehrnoori
Keyword(s):  
Salinity Gradient ◽  
Horizontal Wells ◽  
Sensitivity Study ◽  
Third Order ◽  
Fine Grid ◽  
Heterogeneous Reservoirs ◽  
Vertical Permeability ◽  
Well Spacing ◽  
Optimization Schemes ◽  
Very High

Summary A systematic simulation study of gravity-stable surfactant flooding was performed to understand the conditions under which it is practical and to optimize its performance. Different optimization schemes were introduced to minimize the effects of geologic parameters and to improve the performance and the economics of surfactant floods. The simulations were carried out by use of horizontal wells in heterogeneous reservoirs. The results show that one can perform gravity-stable surfactant floods at a reasonable velocity and with very-high sweep efficiencies for reservoirs with high vertical permeability. These simulations were carried out with a 3D fine grid and a third-order finite-difference method to accurately model fingering. A sensitivity study was conducted to investigate the effects of heterogeneity and well spacing. The simulations were performed with realistic surfactant properties on the basis of laboratory experiments. The critical velocity for a stable surfactant flood is a function of the microemulsion (ME) viscosity, and it turns out there is an optimum value that one can use to significantly increase the velocity and still be stable. One can optimize the salinity gradient to gradually change the ME viscosity. Another alternative is to inject a low-concentration polymer drive following the surfactant slug (without polymer). Polymer complicates the process and adds to its cost without a significant benefit in most gravity-stable surfactant floods, but an exception is when the reservoir is highly layered. The effect of an aquifer on gravity-stable surfactant floods was also investigated, and strategies were developed for minimizing its effect on the process.

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.

Research on the Transmission Optimization Schemes of User Side Power Consumption Information

2021 ◽  
Author(s):  
Wu Zhongxing ◽  
Wang Chaoliang ◽  
Li Dan ◽  
Zhu Enguo ◽  
Zhu Zixu
Keyword(s):  
Power Consumption ◽  
Optimization Schemes
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