Quantum Annealing Computation Method for Discrete Optimization
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Solution Overview
Problem
Conventional quantum annealing computation methods, relying on the adiabatic theorem, face a bottleneck where computation time increases exponentially with problem size, limiting the ability to accelerate processing, especially for large problems.
Innovation Solution
A method that defines an energy function to guide physical systems to a lowest energy state by repeatedly transferring one system to a low energy state and another to a high energy state, constructing a network structure to apply these operations, and determining the minimum energy state among multiple systems, reducing computation steps and required quantum bits to a highly polynomial size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional quantum annealing computation using adiabatic theorem is used, then the physical system can be transferred to the lowest energy state, but the computation time increases exponentially with problem size
Solution Approach 1:
The patent divides the quantum system into multiple groups (first group and second group) and applies different operations to each group. By segmenting the system and applying targeted operations to specific groups based on their energy states, the computation can progress more efficiently toward the lowest energy state without requiring uniformly slow evolution across the entire system, thus reducing computation time while maintaining accuracy.
2Reliability
If the time change in potential is slow to satisfy adiabatic theorem, then the state traces the lowest energy state, but the computation cannot be accelerated beyond the limit
Solution Approach 1:
The patent dynamically adjusts the operations applied to different groups of quantum bits based on their current energy states. By making the computation process adaptive and dynamic rather than uniformly slow, the system can maintain reliability in tracing the lowest energy state while significantly improving computation speed through targeted interventions on specific groups.
Solution Approach 2:
The patent employs periodic measurement and operation cycles where the system state is measured, groups are identified based on energy states, and specific operations are applied. This periodic action allows the system to maintain accuracy while progressing through computation steps more rapidly than continuous slow evolution would permit.
3Loss of time
If multiple physical systems are used to reduce computation steps, then the number of quantum bits increases, but the computation time increase is suppressed
Solution Approach 1:
The patent segments the quantum system into multiple groups that can be processed in parallel or sequence. By organizing quantum bits into structured groups and applying operations to specific groups based on their energy states, the system efficiently utilizes the available quantum bits to reduce computation time without requiring a proportional increase in the total number of quantum bits.
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
This approach significantly reduces the number of computation steps and quantum bits needed, suppressing the increase in computation time with problem size, allowing for more efficient solving of discrete optimization problems compared to conventional methods.
Implementation Method 1
repeatedly applying a method for transferring an energy state of one of two physical systems having the same quantum state to a low energy state and transferring an energy state of the other of the two physical systems to a high energy state
Data Source
AI summary
A method for performing quantum annealing computation for solving a discrete optimization problem includes the steps of: (a) identifying an operation for transferring an energy state of one of two physical systems (u, d) having the same quantum state to a low energy state and transferring an energy state of the other of the two physical systems to a high energy state; (b) constructing a network structure among a plurality of physical systems that indicates an order of application of the operation of the step (a) on two physical systems among the plurality of physical systems; and (c) obtaining a physical system having a minimum energy state in the plurality of physical systems by applying the operation of the step (a) to the plurality of physical systems according to the order indicated in the network structure of step (b).