Programmable Optical Coupler for Sparse Coding Optimization
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Solution Overview
Problem
Current technologies face challenges in rapidly and efficiently solving sparse coding optimization problems, which are crucial in fields like machine learning and signal processing, as they often require complex calculations and are not well-suited for programmable and configurable optical couplers.
Innovation Solution
A programmable optical coupler system is developed that converts sparse coding optimization problems into quadratic unconstrained binary optimization (QUBO) problems, enabling efficient minimization using an annealer or quantum computer, thereby solving sparse coding optimization problems quickly and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional computational methods are used to solve sparse coding optimization problems, then calculation accuracy can be maintained, but the solving speed and efficiency are insufficient
Solution Approach 1:
The patent replaces traditional electronic computational systems with an optical computational system. The optical coupler uses light propagation, interference, and diffraction to perform sparse coding optimization calculations, substituting electronic mechanical operations with optical field interactions. This enables parallel processing of multiple calculations simultaneously, dramatically improving solving speed while maintaining accuracy.
Solution Approach 2:
The patent introduces a new dimension of computation by using optical field space. Instead of sequential electronic bit operations, the system utilizes spatial distribution of light beams, phase modulation, and optical path differences to encode and process information. This dimensional transition from electronic to optical domain enables massive parallelism in solving optimization problems.
2Measurement precision
If complex calculations are performed to solve sparse coding problems accurately, then solution precision is improved, but the computational time increases
Solution Approach 1:
The patent performs preliminary configuration of the optical system by setting up the coupler topology, phase modulators, and beam paths in advance according to the specific sparse coding problem. The system pre-establishes the computational framework using optical components, so that when the actual calculation is needed, the system can immediately execute the optimization algorithm at high speed without setup delays.
Solution Approach 2:
The optical computational system maintains continuous operation with light beams constantly propagating through the coupler. Unlike electronic systems that may require resetting or sequential processing steps, the optical system sustains continuous calculation through uninterrupted light propagation and real-time phase modulation, eliminating idle time and maintaining persistent computational action.
3Adaptability or versatility
If a programmable and configurable optical coupler is designed, then versatility for various operations is improved, but device complexity increases
Solution Approach 1:
The patent designs a universal optical coupler architecture that can perform multiple sparse coding operations and potentially other optimization tasks. The coupler incorporates configurable phase modulators, adjustable beam splitters, and reconfigurable coupling ratios, allowing a single device to handle various sparse coding problems, different dictionary matrices, and multiple measurement vectors without requiring separate dedicated systems for each operation.
Solution Approach 2:
The optical coupler employs dynamic and reconfigurable components including phase modulators that can change coupling ratios in real-time, adjustable beam splitters that can modify light distribution dynamically, and programmable control systems that can reconfigure the computational topology on-the-fly. This dynamic capability allows the system to adapt to different problem specifications while maintaining a relatively compact physical structure.
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
The system effectively converts sparse coding problems into QUBO form, allowing for rapid and accurate solutions, enhancing the efficiency of sparse coding optimization tasks in various fields such as machine learning and signal processing.
Implementation Method 1
a beam splitter, to split each laser beam of the input array of laser beams, to produce split laser beams
Implementation Method 2
a phase alteration unit to receive values indicative of the QUBO matrix from the computing device, and to alter a phase of each laser beam of the split laser beams based on the QUBO matrix
Implementation Method 3
a beam combiner to combine the phase-altered split laser beams into a combined output array of laser beams
Implementation Method 4
a light detector to detect the combined output array of laser beams
Data Source
AI summary
The systems and methods disclosed may improve existing optical coupler and sparse coding problem solving technology. Optical coupler technology may be improved by the provision of a versatile, efficient, and rapid optical coupler that may be programmed. Sparse coding optimization technology may be improved by the provision of methods for converting a sparse coding optimization problem into a quadratic unconstrained binary optimization for minimization by an annealer (such as a programmable optical coupler), a quantum computer, or similar apparatus. When combined, the programmable optical coupler may solve sparse coding optimization problems particularly quickly and efficiently.