Optical Ising Model Processor for Combinatorial Optimization
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Solution Overview
Problem
Conventional methods for solving combinatorial optimization problems are time-consuming due to the exponential increase in combinations as the number of elements increases, making it impractical to find optimal solutions in a reasonable time.
Innovation Solution
A combinatorial optimization problem processing device and method that associates the problem with an Ising model, utilizing a 1×2 Mach-Zehnder optical modulator, optical interference circuit, optical coupler, and modulation signal generator to repeatedly allow interactions in the Ising model from a neutral state, represented by polarized clock pulse trains, to efficiently find optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a brute-force method is used to solve NP-hard combinatorial optimization problems, then the solution is guaranteed to be optimal, but the computation time increases exponentially with the number of elements
Solution Approach 1:
The patent replaces conventional electronic computing systems with an optical computing system based on the Ising model. The system uses optical interference in a Mach-Zehnder modulator to physically simulate the energy minimization process of the Ising model, allowing the system to naturally converge to the optimal solution through physical laws rather than sequential computational steps. This substitution of optical physics for electronic computation fundamentally changes the problem-solving approach from exponential-time algorithms to polynomial-time physical simulation.
Solution Approach 2:
The patent transforms the combinatorial optimization problem parameters into physical parameters of the Ising model (spin states, coupling coefficients, and external fields). By mapping problem elements to spins and problem constraints to interaction energies, the system changes the parameter space from discrete combinatorial values to continuous physical states that can be manipulated through optical control, enabling faster convergence to optimal solutions.
2Adaptability or versatility
If the number of elements in the combinatorial optimization problem increases, then the problem becomes more comprehensive and applicable to real-world scenarios, but the number of combinations increases exponentially making the problem harder to solve
Solution Approach 1:
The patent replaces the mechanical/computational process of evaluating exponential combinations with a physical system that naturally explores the solution space through optical interference and energy minimization. The Ising model-based optical system processes all combinations simultaneously through parallel optical paths, transforming the complexity from exponential computational evaluation to polynomial-time physical simulation that scales gracefully with problem size.
3Ease of manufacture
If conventional CMOS semiconductor chip implementation is used to solve the Ising model, then the system can be practically implemented, but finding the optimal solution remains time-consuming
Solution Approach 1:
The patent replaces conventional CMOS electronic implementation with an optical implementation using a Mach-Zehnder modulator. This substitution leverages the parallel processing capability of optical systems and the natural energy minimization of the Ising model to achieve faster convergence. The optical system processes information through light interference patterns rather than sequential electronic logic operations, fundamentally reducing the time required to reach optimal solutions while maintaining implementation feasibility through integrated photonic circuits.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables finding optimal solutions to combinatorial optimization problems in a significantly shorter time compared to conventional methods, as demonstrated by solving a Max-Cut-3 problem with 16 elements in approximately 500 ns, whereas previous methods required over 672 μs.
Implementation Method 1
a 1×2 Mach-Zehnder optical modulator configured to receive a polarized clock pulse train
Implementation Method 2
an optical interference circuit configured to receive polarized clock pulse trains that were modulated by the Mach-Zehnder optical modulator
Implementation Method 3
an optical coupler configured to couple output of the optical interference circuit with an initialization optical pulse train that creates a neutral state
Implementation Method 4
a modulation signal generator configured to perform waveform shaping on an electrical signal obtained by photoelectrically converting an output signal of the optical coupler
Data Source
AI summary
A combinatorial optimization problem processing device is for associating a combinatorial optimization problem having N elements with an Ising model to process the combinatorial optimization problem. The combinatorial optimization problem processing device includes: a 1×2 Mach-Zehnder optical modulator that receives a polarized clock pulse train; an optical interference circuit that receives polarized clock pulse trains that were modulated by the Mach-Zehnder optical modulator; an optical coupler that couples output of the optical interference circuit with an initialization optical pulse train that creates a neutral state with respect to interactions between the elements; and a modulation signal generator that performs waveform shaping on an electrical signal obtained by photoelectrically converting an output signal of the optical coupler, generates a modulation signal for the Mach-Zehnder optical modulator, and externally outputs a monitor signal that represents a solution to the optimization problem. The optical interference circuit repeatedly allows a predetermined interaction in the Ising model to occur from the neutral state at a period corresponding to the N pulses of the polarized clock pulse train.