Low-Code Quantum Workflows for Computing Device Selection
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Solution Overview
Problem
Quantum computing resources are underutilized due to the complexity of quantum algorithms and the lack of expertise among users, making it difficult for new users to optimize their use and determine the appropriate computing device for specific tasks.
Innovation Solution
A low-code system that uses a machine learning model to determine the type of computing device (quantum, classical, or hybrid) needed for a task based on user input, generating corresponding algorithms and executing them on the appropriate device, thereby simplifying the process and reducing the need for extensive coding knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If quantum computing devices are used to perform computations more quickly, then processing speed is improved, but the complexity of coding techniques and algorithms increases
Solution Approach 1:
The patent introduces an automated algorithm generation system that acts as an intermediary between the user and quantum computing devices. The system includes a problem specification module that receives user inputs, an algorithm generation module that automatically creates quantum algorithms, and a device selection module that determines the appropriate computing device. This intermediary system eliminates the need for users to directly write complex quantum code while still enabling utilization of quantum computing resources.
Solution Approach 2:
The system enables self-service by allowing users to specify computational problems in natural language or high-level descriptions without requiring expertise in quantum programming. The automated algorithm generation module then self-generates the appropriate quantum algorithms, and the system self-selects the optimal computing device, making quantum computing accessible to users who would otherwise be unable to utilize it.
2Power
If quantum algorithms are created to harness quantum computer capabilities, then computational power is improved, but the time and expertise required to create algorithms increases
Solution Approach 1:
The system performs preliminary actions by pre-generating quantum algorithms based on problem specifications before actual computation begins. The automated algorithm generation module creates ready-to-execute quantum algorithms in advance, eliminating the time-consuming process of manual algorithm creation. The system also pre-determines the appropriate computing device configuration before the computational task is executed.
Solution Approach 2:
The patent replaces the manual mechanical process of writing and debugging quantum algorithms with an automated computational system. The algorithm generation module uses classical computing to automatically generate quantum algorithms, substituting the manual expertise-based process with an automated system that can rapidly produce algorithms without requiring deep quantum computing knowledge.
3Ease of operation
If quantum computing resources are made accessible to new users, then ease of operation is improved, but the need for expertise in quantum algorithms decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the automated algorithm generation module receives input from problem specifications and adjusts algorithm generation accordingly. The device selection module uses feedback from problem characteristics to determine the optimal computing device. This feedback loop ensures that even though users lack quantum expertise, the system can still generate optimized algorithms and select appropriate devices based on the computational requirements.
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for low-code utilization of a computing system involving a classical computing device and a quantum computing device. An example method includes receiving, by formulation circuitry, an input from an interaction modality. The example method includes transmitting, by the formulation circuitry, the input to a machine learning model to produce an intermediate output. The example method includes determining, by the formulation circuitry, a type of computing device needed for based on the intermediate output. The example method includes generating, by a first runtime circuitry, one or more algorithms based on the determined type of computing device. The example method includes executing, by a second runtime circuitry, the one or more algorithms on one or more corresponding computing devices to produce an output. The example method further includes packaging, by the formulation circuitry, the output for transmission via the interaction modality.