Complementary Component Matching Using Quantum Production Control
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Solution Overview
Problem
Distributed production processes face challenges in achieving precise component combinations with high computational effort and resource requirements, especially when combining multiple components, which is unsolvable for classical digital computing units.
Innovation Solution
Utilizing a quantum-based computing unit, such as a quantum annealer or digital annealer, to efficiently determine optimal combinations of complementary components by exploiting quantum mechanical effects, reducing computational time and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If classical digital computing units are used to determine optimal combinations of complementary components, then the combination problem can be solved with conventional computing resources, but the computational effort and time required increase significantly, making it unsolvable for large numbers of components
Solution Approach 1:
The patent replaces classical digital computing systems with quantum-based computing units to solve the combination optimization problem. Quantum mechanical effects (superposition, entanglement, tunneling) enable parallel evaluation of multiple component combinations simultaneously, achieving exponential speedup for combinatorial optimization problems that are intractable for classical computers.
Solution Approach 2:
The patent changes the fundamental computational parameters by transitioning from classical bits to quantum bits (qubits), enabling the system to represent and process multiple possible component combinations in superposition states. This parameter change allows the quantum computing unit to evaluate factorial(N) combinations in polynomial time rather than requiring exponential computational resources.
2Manufacturing precision
If all possible combinations of complementary components are evaluated to find optimal combinations, then the best product quality can be achieved, but the computational resources and time required increase factorially with the number of components
Solution Approach 1:
The patent uses quantum annealing to perform preliminary optimization by preparing the quantum system in a superposition of all possible component combinations and then gradually evolving the system toward the optimal configuration. This preliminary quantum optimization identifies near-optimal combinations much faster than exhaustive classical evaluation, achieving high product quality without factorial time consumption.
Solution Approach 2:
The patent employs dynamic quantum annealing processes where the Hamiltonian of the quantum system is continuously adjusted during computation. The system dynamically transitions from an initial easy-to-prepare state to a final state representing the optimal component combination, allowing real-time adaptation and efficient convergence to high-quality solutions.
3Productivity
If quantum-based computing units are used to determine optimal combinations, then computational time and energy consumption are reduced, but quantum computing infrastructure requires specialized cooling and maintenance
Solution Approach 1:
The patent introduces hybrid quantum-classical computing architectures where quantum computing units serve as specialized co-processors for optimization tasks. Classical control systems interface with quantum hardware through standardized protocols, allowing quantum capabilities to be integrated into existing manufacturing control infrastructure without requiring complete system replacement. This intermediary approach manages the complexity of quantum infrastructure while delivering quantum speedup for combination optimization.
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 the efficient, cost-effective manufacture of products with complementary components by determining optimal combinations in a shorter time and with lower energy consumption, suitable for large numbers of components.
Implementation Method 1
by exploiting quantum mechanical effects
Data Source
Figure 1~2
Figure 3
AI summary
Method for controlling the production of a product comprising at least two mutually complementary components (A_i, B_j), of which a quantity (A, B) has been produced in independent production processes (1a, 1b) and which each exhibit at least one characteristic (a_i, b_j) with a production-related variation, wherein the characteristics (a_i, b_j) of the components (A_i, B_j) have been determined by measurement processes (2a, 2b) and have been stored in a storage medium (16) in assignment to an identifier of the respective component (A_i, B_j), and wherein the complementary components (A_i, B_j) are combined from the quantities (A, B) in a combination step (3) based on the respective characteristics (a_i, b_j) such that a selected combination of the components (A_i, B_j) forms a product, wherein the combination step (3) is at least partially performed by means of a quantum-based computing unit (18) is carried out,where the selected combinations of components (A_i, B_j) as a whole correspond as best as possible to a target value.