Hybrid Quantum-Classical Solver for QUBO Optimization
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Solution Overview
Problem
Current quantum computing solutions for quadratic unconstrained binary optimization (QUBO) problems are inefficient due to the volatility of quantum states and limited access to quantum hardware, leading to variable solution quality and calculation speed issues.
Innovation Solution
A method involving a trained graph neural network to predict performance metrics for QUBO problems, determining whether a variational quantum solver or a classical solver should be used based on the predicted quality of solutions, thereby optimizing the selection of processing hardware and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If quantum hardware is used to solve QUBO problems, then solution quality may be improved, but calculation speed and reliability deteriorate due to state volatility and decoherence
Solution Approach 1:
The system performs preliminary evaluation of the QUBO problem characteristics before committing to quantum computation. By assessing problem properties in advance, the system determines whether quantum hardware is likely to provide adequate solution quality, thereby preventing unreliable quantum computations from being executed on unsuitable problems.
Solution Approach 2:
A classical evaluation system acts as an intermediary between the QUBO problem and the quantum solver. This intermediary assesses problem characteristics and predicts quantum solver performance, serving as a gatekeeper that filters which problems should be submitted to quantum hardware, thus improving overall system reliability.
2Manufacturing precision
If quantum hardware is used to solve QUBO problems, then solution quality may be improved, but calculation time increases due to limited access and hardware constraints
Solution Approach 1:
The system performs preliminary assessment of problem suitability for quantum solving before execution. This advance evaluation prevents time-consuming quantum computations from being launched on problems that would not benefit from quantum processing, thereby reducing overall calculation time while preserving solution quality for appropriate problems.
Solution Approach 2:
Instead of universally applying quantum solving to all QUBO problems, the system applies quantum computation selectively only to problems where it is predicted to provide meaningful advantages. This partial application approach optimizes resource utilization and reduces unnecessary calculation time.
3Productivity
If variational quantum algorithms are used on NISQ devices, then quantum advantage may be achieved, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs preliminary characterization of NISQ device capabilities and problem requirements before attempting quantum advantage. By evaluating device metrics and problem properties in advance, the system identifies suitable problem-device matches that can achieve quantum advantage without excessive complexity.
Solution Approach 2:
The system adjusts operational parameters based on preliminary assessments, optimizing the configuration of variational quantum algorithms for specific NISQ devices. By tuning parameters such as circuit depth, number of measurements, and algorithm iterations based on device characteristics, the system achieves quantum advantage while managing device complexity.
4Manufacturing precision
If more quantum gates are applied to improve solution accuracy, then manufacturing precision improves, but stability deteriorates due to decoherence and fidelity margins
Solution Approach 1:
The system performs preliminary assessment of problem requirements and device stability characteristics before determining the appropriate number of quantum gates. By evaluating the minimum gates needed for acceptable accuracy against device stability limits, the system optimizes the gate count to achieve solution accuracy without exceeding stability thresholds.
Solution Approach 2:
Instead of applying maximum numbers of quantum gates to ensure accuracy, the system applies only the necessary number of gates required to achieve acceptable solution quality. This partial action approach prevents excessive gate sequences that would compromise stability due to decoherence and fidelity margins.
Data Source
AI summary
A computer-implemented method includes encoding a QUBO problem in a corresponding QUBO graph problem, wherein each binary variable of the QUBO problem corresponds to a node of the QUBO graph problem and edges between two nodes encode coefficients of terms containing both binary variables corresponding to the two nodes, or receiving a QUBO graph problem in a QUBO graph representation; providing the QUBO graph problem as an input to a trained graph neural network on a processing system; retrieving a predicted performance metric for solving the QUBO problem with a variational quantum solver from an output of the trained graph neural network to the QUBO graph problem provided at the input; and, based on the predicted performance metric, providing the QUBO problem to the variational quantum solver implemented on quantum hardware.