Hybrid Quantum Solver Interface for Automated Hardware Selection
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Solution Overview
Problem
There is a need for efficient and cost-effective computational systems that can solve a large variety of problems using a combination of classical and quantum computing, capable of selecting the appropriate solver and hardware automatically to provide high-quality solutions in a time-efficient manner.
Innovation Solution
A hybrid computational system that utilizes a combination of classical and quantum processors, employing algorithms like quantum annealing and adiabatic quantum computation, and includes a solver interface that can access various solvers and hardware via a simple API, allowing for pre-processing, post-processing, and autonomous selection of heuristic optimizers and hardware resources to generate solutions efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a hybrid computational system with multiple solvers and hardware is deployed, then problem-solving capability and solution quality are improved, but system complexity and difficulty of selecting appropriate solvers increase
Solution Approach 1:
The system provides a universal interface that can access multiple types of solvers (quantum annealing, simulated annealing, genetic algorithms, etc.) and hardware resources through a single standardized API. This allows the system to handle diverse problem types without requiring users to manually configure complex hardware-solver combinations, thereby improving adaptability while managing complexity through abstraction.
Solution Approach 2:
The system introduces an intermediary layer (the interface and controller components) that mediates between the user and the complex underlying hardware-solver infrastructure. This intermediary handles the complexity of solver selection, hardware allocation, and parameter configuration automatically, presenting a simplified interface to users while maintaining access to sophisticated computational resources.
2Ease of operation
If automated solver and hardware selection is implemented, then ease of operation is improved, but computational time for problem analysis increases
Solution Approach 1:
The system performs preliminary analysis of the input problem to automatically identify suitable solvers and hardware resources before the main computation begins. By pre-characterizing the problem properties and pre-selecting optimal solver combinations, the system reduces the need for manual configuration while minimizing the time required for setup, thereby improving ease of operation without significant time penalty.
3Manufacturing precision
If pre- and post-processing techniques are applied, then solution quality is improved, but computational overhead increases
Solution Approach 1:
The pre-processing and post-processing techniques are applied selectively and locally rather than uniformly to all problems. The system analyzes the specific characteristics of each problem instance to determine the appropriate level and type of processing needed, applying intensive processing only where beneficial and using lighter processing where overhead would outweigh benefits, thereby improving solution quality while managing computational overhead.
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 provides high-quality solutions to a wide range of problems efficiently by leveraging both classical and quantum computing, automating the selection of solvers and hardware, thus optimizing computation time and resource utilization.
Implementation Method 1
Adiabatic quantum computation typically involves evolving a system from a known initial Hamiltonian to a final Hamiltonian by gradually changing the Hamiltonian. The rate of change is slow enough that the system is always in the instantaneous ground state of the evolution Hamiltonian during the evolution
Implementation Method 2
quantum annealing may use quantum effects, such as quantum tunneling, to reach a global energy minimum more accurately and/or more quickly than classical annealing
Implementation Method 3
employing pre- and post-processing techniques to generate quality solutions efficiently, including the use of quantum processors and classical hardware like FPGAs and GPUs
Data Source
AI summary
Computational systems implement problem solving using heuristic solvers or optimizers. Such may iteratively evaluate a result of processing, and modify the problem or representation thereof before repeating processing on the modified problem, until a termination condition is reached. Heuristic solvers or optimizers may execute on one or more digital processors and/or one or more quantum processors. The system may autonomously select between types of hardware devices and/or types of heuristic optimization algorithms. Such may coordinate or at least partially overlap post-processing operations with processing operations, for instance performing post-processing on an ith batch of samples while generating an (i+1)th batch of samples, e.g., so post-processing operation on the ith batch of samples does not extend in time beyond the generation of the (i+1)th batch of samples. Heuristic optimizers selection is based on pre-processing assessment of the problem, e.g., based on features extracted from the problem and for instance, on predicted success.