Hybrid Quantum Control Apparatus for Optimization Problems
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Solution Overview
Problem
Current quantum computing paradigms, such as gate-based quantum computers and quantum annealers, are limited in their ability to efficiently solve real-world optimization problems, particularly in the development of new chemicals and materials, due to error-proneness and limited scalability of gate-based systems and limited applicability but easier scalability of quantum annealers.
Innovation Solution
An apparatus and method that utilize control signals to efficiently utilize quantum computers for solving optimization problems by translating optimization problems into interaction problems, with at least part of the interaction problem calculated on a quantum computing system and the optimization problem calculated on both quantum and classical systems based on determined interaction parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If gate-based quantum computing is used to calculate a broad variety of problems including electronic structure problems, then problem-solving versatility is improved, but error rate increases and scalability deteriorates
Solution Approach 1:
The patent divides the quantum computing system into two distinct segments: a gate-based quantum computer for solving electronic structure problems (interaction problem) and a quantum annealer for solving optimization problems. This segmentation allows each system to operate in its optimal regime, with the gate-based system handling calculations where it excels and the quantum annealer handling optimization where it is more reliable and scalable.
2Adaptability or versatility
If gate-based quantum computing is used to calculate a broad variety of problems, then problem-solving versatility is improved, but the number of utilizable quantum operations that can be applied consecutively is limited
Solution Approach 1:
The patent segments the computational workflow into two parts: the interaction problem (electronic structure calculations) solved by gate-based quantum computing, and the optimization problem (finding optimal configurations) solved by quantum annealing. This allows the system to bypass the limitation on consecutive operations in gate-based systems by offloading the optimization iterations to the quantum annealer, which can perform many more consecutive operations.
3Productivity
If quantum annealing is used for specific problems, then scalability is improved and a larger number of quantum elements are available, but applicability to diverse problems deteriorates
Solution Approach 1:
The patent creates a universal hybrid framework that combines the strengths of both quantum computing paradigms. The gate-based quantum computer provides versatility for electronic structure calculations, while the quantum annealer provides scalability for optimization. Together, they form a multi-functional system that can tackle both types of problems, with each component performing the tasks it is best suited for.
4Productivity
If a hybrid approach combining gate-based quantum computing and quantum annealing is used, then computational efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a classical computer as an intermediary that coordinates between the gate-based quantum computer and the quantum annealer. This classical mediator handles the workflow management, data transfer, and integration of results from both quantum systems, thereby managing the overall system complexity while enabling the computational efficiency gains of the hybrid approach.
Data Source
AI summary
The invention refers to an apparatus for providing control signals to one or more quantum computing systems and/or classical computing systems. A providing unit provides an optimization problem representation comprising an interaction part quantified by an interaction parameter. A problem providing unit provides an interaction problem indicative of an interaction parameter. A providing unit provides a workflow indicating for the optimization and the interaction problem which operations are to be performed on the quantum computing systems or the classical computing systems. The workflow causes that at least a part of the interaction problem is calculated on a quantum computing system and that the optimization problem is calculated on a quantum computing system and/or a classical computing system. A generation unit generates control signals for controlling the one or more quantum computing systems and/or the classical computing systems based on the workflow.