Quantum vertex model for reversible classical computing

C. Chamon; E. R. Mucciolo; A. E. Ruckenstein; Z.-C. Yang

doi:10.1038/ncomms15303

Quantum vertex model for reversible classical computing

Nature Communications ◽

10.1038/ncomms15303 ◽

2017 ◽

Vol 8 (1) ◽

Cited By ~ 6

Author(s):

C. Chamon ◽

E. R. Mucciolo ◽

A. E. Ruckenstein ◽

Z.-C. Yang

Keyword(s):

Vertex Model ◽

Quantum Vertex ◽

Classical Computing

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Quantum Vertex Algebras

Taniguchi Conference on Mathematics Nara '98 ◽

10.2969/aspm/03110051 ◽

2018 ◽

Cited By ~ 8

Author(s):

Richard E. Borcherds

Keyword(s):

Vertex Algebras ◽

Quantum Vertex

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Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements

Energies ◽

10.3390/en14040830 ◽

2021 ◽

Vol 14 (4) ◽

pp. 830

Author(s):

Filipe F. C. Silva ◽

Pedro M. S. Carvalho ◽

Luís A. F. M. Ferreira

Keyword(s):

Distributed Generation ◽

Optimal Solution ◽

Dynamic Reconfiguration ◽

Scheduling Problem ◽

Low Carbon ◽

Distribution Grid ◽

Medium Voltage ◽

Classical Computing ◽

The Impact ◽

Distribution Grids

The dissemination of low-carbon technologies, such as urban photovoltaic distributed generation, imposes new challenges to the operation of distribution grids. Distributed generation may introduce significant net-load asymmetries between feeders in the course of the day, resulting in higher losses. The dynamic reconfiguration of the grid could mitigate daily losses and be used to minimize or defer the need for network reinforcement. Yet, dynamic reconfiguration has to be carried out in near real-time in order to make use of the most updated load and generation forecast, this way maximizing operational benefits. Given the need to quickly find and update reconfiguration decisions, the computational complexity of the underlying optimal scheduling problem is studied in this paper. The problem is formulated and the impact of sub-optimal solutions is illustrated using a real medium-voltage distribution grid operated under a heavy generation scenario. The complexity of the scheduling problem is discussed to conclude that its optimal solution is infeasible in practical terms if relying upon classical computing. Quantum computing is finally proposed as a way to handle this kind of problem in the future.

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On Delocalization in the Six-Vertex Model

Communications in Mathematical Physics ◽

10.1007/s00220-021-03949-8 ◽

2021 ◽

Author(s):

Marcin Lis

Keyword(s):

Vertex Model

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Scaling limit of the Z2 invariant inhomogeneous six-vertex model

Nuclear Physics B ◽

10.1016/j.nuclphysb.2021.115337 ◽

2021 ◽

Vol 965 ◽

pp. 115337 ◽

Cited By ~ 2

Author(s):

Vladimir V. Bazhanov ◽

Gleb A. Kotousov ◽

Sergii M. Koval ◽

Sergei L. Lukyanov

Keyword(s):

Scaling Limit ◽

Vertex Model

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Coreset Clustering on Small Quantum Computers

Electronics ◽

10.3390/electronics10141690 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1690

Author(s):

Teague Tomesh ◽

Pranav Gokhale ◽

Eric R. Anschuetz ◽

Frederic T. Chong

Keyword(s):

Quantum Algorithms ◽

Quantum Computers ◽

Data Sets ◽

New Paradigm ◽

Data Set ◽

Small Quantum ◽

Natural Data ◽

Near Term ◽

Classical Computing ◽

Quantum Speedup

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

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