Quantum Computing SDK Layered Architecture for PUBO Optimization
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Solution Overview
Problem
Current quantum computing technologies face challenges such as embedding issues, manual errors, and optimization complexities, particularly in implementing polynomial unconstrained binary optimization (PUBO) on quantum computers, which limits user access and efficiency in solving computationally expensive problems across fields like finance and drug discovery.
Innovation Solution
A quantum-ready or quantum-enabled software development kit (SDK) with a multi-layer architecture, comprising an algorithms layer, binary polynomial layer, and common solver interface layer, that transforms user inputs into polynomial unconstrained binary optimization (PUBO) form, enabling execution on both quantum and classical computers, thereby abstracting away complexity and providing a platform-agnostic solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a quantum computer is used to solve NP-complete and NP-hard problems, then computational power and problem-solving capability are improved, but device complexity and programming difficulty increase
Solution Approach 1:
The patent introduces a software development kit (SDK) as an intermediary layer between the user and the quantum computer. This SDK includes high-level algorithms and a common interface that automatically handles the complex tasks of problem transformation, embedding, and hardware interaction, thereby reducing the complexity burden on the user while preserving quantum computational power.
Solution Approach 2:
The patent divides the quantum computing system into distinct layers: an algorithms layer for high-level problem solving, a binary polynomial layer for transformation, and a solver layer for execution. This segmentation allows each layer to be optimized independently and simplifies the interface for users, who only need to interact with the high-level algorithms rather than the underlying quantum hardware complexity.
2Ease of operation
If quantum computing is made accessible to users, then ease of operation is improved, but loss of information about quantum mechanics complexity occurs
Solution Approach 1:
The SDK acts as an information-filtering intermediary that translates complex quantum mechanical concepts and hardware requirements into simple, user-friendly algorithmic interfaces. Users interact with abstracted problem representations rather than quantum states, losing none of the underlying complexity but gaining operational simplicity.
Solution Approach 2:
The patent creates a simplified copy or abstraction of the quantum computing process through the SDK interface. This abstraction layer preserves the essential problem-solving capabilities while presenting a simplified, more manageable interface that doesn't require users to understand the full quantum mechanical complexity.
3Adaptability or versatility
If manual programming of quantum algorithms is performed, then customization capability is improved, but implementation effort and error rate increase
Solution Approach 1:
The SDK pre-implements common algorithms and optimization routines in the algorithms layer, so that users can leverage these pre-built solutions without performing manual programming. This preliminary action eliminates the time-consuming tasks of algorithm design and hardware optimization while preserving the ability to customize problems through the high-level interface.
Solution Approach 2:
The SDK enables self-service by automatically handling the transformation of user problems into the required quantum formats and by self-optimizing the embedding of problems onto the quantum hardware. This eliminates the need for manual programming expertise while maintaining full customization capability through the high-level algorithmic interface.
4Productivity
If a platform-specific quantum software is used, then performance optimization is improved, but adaptability across different quantum hardware decreases
Solution Approach 1:
The patent designs the SDK with a universal common interface that works across multiple quantum hardware platforms and architectures. The algorithms layer is written in a platform-agnostic manner, allowing the same code to be executed on different quantum computers without modification. This universality maintains performance optimization through platform-specific solvers while ensuring adaptability across the quantum computing ecosystem.
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AI summary
Described herein are methods, systems, and media for generating a quantum-ready or quantum-enabled software development kit (SDK) for a quantum computing system. Such methods may comprise accepting user input from an application at an application interface, which application is executed on a digital computer, and implementing one or more algorithms, at an algorithms layer, that may be solved heuristically or exactly depending on the requirements of the user input. The one or more algorithms may abstract away a complexity of the application; transforming the one or more algorithms from the application space into the one or more instructions in polynomial unconstrained binary optimization (PUBO) form. The one or more instructions may be executed in PUBO form at the common interface of the solver layer.