Hybrid QPU Execution for Variational Quantum Algorithms
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Solution Overview
Problem
Conventional variational quantum algorithms run slowly due to the limitations of current quantum computers in terms of fidelity and qubit size, making it time-consuming to obtain solutions.
Innovation Solution
A hybrid approach using two different types of quantum processing units (QPUs) is employed, where one QPU optimizes circuit parameters for a precursory problem and then these parameters are used on another QPU to solve the target problem, leveraging the relative strengths of each QPU in speed, qubit capacity, and fidelity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single QPU with high fidelity and sufficient qubits is used to solve the target problem, then solution accuracy is improved, but the time to obtain a solution increases significantly
Solution Approach 1:
The patent divides the problem-solving process into two distinct stages: a first stage using a faster QPU with fewer qubits to optimize circuit parameters for a precursory problem, and a second stage using a slower QPU with more qubits and higher fidelity to solve the actual target problem. This segmentation allows each QPU to operate in its optimal performance regime, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The patent performs preliminary optimization of circuit parameters using the faster first QPU before transferring these optimized parameters to the second QPU for solving the target problem. This preliminary action reduces the computational burden on the second QPU, enabling it to achieve high-accuracy solutions more quickly than if it had to perform the entire optimization process alone.
2Reliability
If a QPU with more qubits and higher fidelity is used, then solution quality is improved, but the operation speed decreases
Solution Approach 1:
The patent segments the computational workload between two QPUs with different performance characteristics. The first QPU is optimized for speed and used for parameter optimization, while the second QPU is optimized for fidelity and used for the final problem solution. This segmentation allows the system to leverage the speed advantage of the first QPU without sacrificing the fidelity advantage of the second QPU.
Solution Approach 2:
The patent applies the concept of local quality by matching each QPU's strengths to the specific requirements of different computational stages. The faster first QPU is used where speed is critical (parameter optimization), while the higher-fidelity second QPU is used where accuracy is critical (target problem solution). Each QPU operates in the regime where its local quality advantage is most valuable.
3Productivity
If circuit parameters are optimized using a faster QPU with fewer qubits, then optimization time is reduced, but the precision of parameter optimization may be limited
Solution Approach 1:
The patent uses the faster first QPU to perform preliminary optimization of circuit parameters, obtaining a good initial solution quickly. These optimized parameters are then transferred to the second QPU, which uses them as a starting point for further refinement. This preliminary action approach allows the system to benefit from the speed of the first QPU while ultimately achieving high-precision parameters through the second QPU's higher fidelity.
Solution Approach 2:
The first QPU acts as an intermediary that prepares optimized circuit parameters which are then used by the second QPU. This intermediary role allows the faster first QPU to perform the computationally intensive parameter optimization, while the higher-fidelity second QPU performs the final precise calculations, combining the advantages of both systems.
Data Source
AI summary
A variational quantum algorithm is solved using two types of quantum processing units (QPU) with different performance metrics (e.g., speed, size and fidelity). One type of quantum processing unit (QPU) is used to optimize some or all of the circuit parameters in a first stage, and these are then used with a different type QPU in a second stage to solve the target problem. The different performance metrics permit tradeoffs between the two stages.