Hybrid Quantum-Classical Exact Solver for Combinatorial Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computational methods struggle to efficiently find optimal solutions for complex problems, as quantum processors can generate probabilistic solutions that lack certification of optimality, while classical exact algorithms face computational bottlenecks due to high time requirements for complete enumeration.
Innovation Solution
A hybrid quantum-classical exact solver combines quantum annealing to generate samples with a Branch and Bound algorithm, embedding problems into a regular structure to improve bounding functions and accelerate computation by statistically analyzing samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum annealing is used to generate solutions, then computation speed is improved, but solution optimality cannot be certified
Solution Approach 1:
The patent merges quantum annealing (for fast solution generation) with classical Branch and Bound algorithms (for optimality certification) into a hybrid solver. The quantum processor generates candidate solutions that are then fed into the classical exact solver, which uses Branch and Bound with statistical analysis of quantum samples to efficiently certify optimality without exhaustive enumeration.
2Reliability
If classical exact algorithms perform complete enumeration to certify optimality, then solution optimality is certified, but computation time increases significantly
Solution Approach 1:
The quantum annealing component performs preliminary action by generating high-quality candidate solutions and statistical samples before the classical Branch and Bound algorithm begins. These pre-generated samples provide information about the solution space that allows the classical solver to establish tighter bounds and prune the search tree more effectively, avoiding complete enumeration.
Solution Approach 2:
The patent changes the parameters of the Branch and Bound algorithm by incorporating statistical information from quantum samples. Instead of using traditional conservative bounds, the system uses quantum-generated samples to inform bound calculations and search strategies, significantly reducing the effective search space while maintaining optimality certification.
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 enhances the speed and accuracy of finding optimal solutions by leveraging quantum samples to refine bounds and guide the search tree, providing a computational speedup and ensuring certified optimality.
Implementation Method 1
Quantum annealing may use quantum effects, such as quantum tunneling, to reach a low-energy state
Data Source
AI summary
Hybrid quantum-classical approaches for solving computational problems in which results from a quantum processor are combined with an exact method executed on a classical processor are described. Quantum processors can generate candidate solutions to a combinatorial optimization problem, but since quantum processors can be probabilistic, they are unable to certify that a solution is an optimal solution. A hybrid quantum-classical exact solver addresses this problem by combining outputs from a quantum annealing processor with a classical exact algorithm that is modified to exploit properties of the quantum computation. The exact method executed on a classical processor can be a Branch and Bound algorithm. A Branch and Bound algorithm can be modified to exploit properties of quantum computation including a) the sampling of multiple low-energy solutions by a quantum processor, and b) the embedding of solutions in a regular structure such as a native hardware graph of a quantum processor.