scholarly journals Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Junyi Huang ◽  
Yisheng Fang ◽  
Zhichao Ruan
Keyword(s):  
Spatial Light Modulator ◽  
Large Scale ◽  
Single Phase ◽  
Optimization Problems ◽  
Light Modulator ◽  
Practical Applications ◽  
Combinatorial Optimization Problems ◽  
Partitioning Problem ◽  
Statistical Systems ◽  
Antiferromagnetic Model

AbstractRecently, spatial photonic Ising machines (SPIM) have been demonstrated to compute the minima of Hamiltonians for large-scale spin systems. Here we propose to implement an antiferromagnetic model through optoelectronic correlation computing with SPIM. Also we exploit the gauge transformation which enables encoding the spins and the interaction strengths in a single phase-only spatial light modulator. With a simple setup, we experimentally show the ground-state-search acceleration of an antiferromagnetic model with 40000 spins in number-partitioning problem. Thus such an optoelectronic computing exhibits great programmability and scalability for the practical applications of studying statistical systems and combinatorial optimization problems.

scholarly journals Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing

2021 ◽  
Author(s):  
Zhichao Ruan ◽  
Huang Junyi ◽  
Yisheng Fang
Keyword(s):  
Spatial Light Modulator ◽  
Large Scale ◽  
Single Phase ◽  
Optimization Problems ◽  
Light Modulator ◽  
Practical Applications ◽  
Combinatorial Optimization Problems ◽  
Partitioning Problem ◽  
Statistical Systems ◽  
Antiferromagnetic Model

Abstract Recently, spatial photonic Ising machines (SPIM) have been demonstrated to compute the minima of Hamiltonians for large-scale spin systems. Here we propose to implement an antiferromagnetic model through optoelectronic correlation computing with SPIM. Also we exploit the gauge transformation which enables encoding the spins and the interaction strengths in a single phase-only spatial light modulator. With a simple setup, we experimentally show the ground state search of an antiferromagnetic model with $40000$ spins in number-partitioning problem. Thus such an optoelectronic computing exhibits great programmability and scalability for the practical applications of studying statistical systems and combinatorial optimization problems.

scholarly journals Multilevel Combinatorial Optimization across Quantum Architectures

2021 ◽  
Vol 2 (1) ◽  
pp. 1-29
Author(s):  
Hayato Ushijima-Mwesigwa ◽  
Ruslan Shaydulin ◽  
Christian F. A. Negre ◽  
Susan M. Mniszewski ◽  
Yuri Alexeev ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Combinatorial Optimization Problems ◽  
Large Scale Problems ◽  
Quantum Processor ◽  
Partitioning Problem ◽  
Real World Datasets ◽  
Near Future

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this article, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. To demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave’s quantum annealer and IBM’s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitude larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution. Reproducibility : Our code and data are available at Reference [1].

A Hybrid Approach of Heuristic and Neural Network for Packing Problems

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Approach ◽  
Two Dimensions ◽  
Packing Problems ◽  
Combinatorial Optimization Problems ◽  
Complete Problems ◽  
Np Complete

Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this paper we propose a heuristic-based Hopfield neural network designed to solve the rectangular packing problems in two dimensions, which is still NP-complete [3]. By comparing the adequacy and efficiency of the results with that obtained by several other exact and heuristic approaches, it has been concluded that the proposed method has great potential in solving 2D packing problems.

scholarly journals Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems

2019 ◽  
Vol 5 (4) ◽  
pp. eaav2372 ◽  
Author(s):  
Hayato Goto ◽  
Kosuke Tatsumura ◽  
Alexander R. Dixon
Keyword(s):  
Combinatorial Optimization ◽  
Hamiltonian Systems ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximate Solutions ◽  
Combinatorial Optimization Problems ◽  
Bifurcation Phenomena ◽  
Field Programmable ◽  
Max Cut Problem ◽  
Adiabatic Optimization

Combinatorial optimization problems are ubiquitous but difficult to solve. Hardware devices for these problems have recently been developed by various approaches, including quantum computers. Inspired by recently proposed quantum adiabatic optimization using a nonlinear oscillator network, we propose a new optimization algorithm simulating adiabatic evolutions of classical nonlinear Hamiltonian systems exhibiting bifurcation phenomena, which we call simulated bifurcation (SB). SB is based on adiabatic and chaotic (ergodic) evolutions of nonlinear Hamiltonian systems. SB is also suitable for parallel computing because of its simultaneous updating. Implementing SB with a field-programmable gate array, we demonstrate that the SB machine can obtain good approximate solutions of an all-to-all connected 2000-node MAX-CUT problem in 0.5 ms, which is about 10 times faster than a state-of-the-art laser-based machine called a coherent Ising machine. SB will accelerate large-scale combinatorial optimization harnessing digital computer technologies and also offer a new application of computational and mathematical physics.

scholarly journals Preparing arbitrary pure states of spatial qudits with a single phase-only spatial light modulator

2015 ◽  
Vol 605 ◽  
pp. 012035 ◽  
Author(s):  
J J M Varga ◽  
A M A Solís-Prosser ◽  
L Rebón ◽  
A Arias ◽  
L Neves ◽  
...  
Keyword(s):  
Spatial Light Modulator ◽  
Single Phase ◽  
Light Modulator ◽  
Pure States

Complex encoding of rotation-invariant filters onto a single phase-only spatial light modulator

2003 ◽  
Vol 42 (11) ◽  
pp. 1973 ◽  
Author(s):  
Oscar Gualdrón ◽  
Jeffrey Davis ◽  
Josep Nicolás ◽  
Juan Campos ◽  
Maria J. Yzuel
Keyword(s):  
Spatial Light Modulator ◽  
Single Phase ◽  
Rotation Invariant ◽  
Light Modulator
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