Quantum Job Scheduling With Pattern-Based Qubit Reallocation
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Solution Overview
Problem
Conventional scheduling techniques for quantum jobs are inefficient in systems that continuously receive new jobs, leading to suboptimal parallel execution and utilization of qubits in quantum computers.
Innovation Solution
A method for quantum job scheduling that involves compiling quantum jobs to fit specific patterns based on the number of qubits required, moving jobs to maintain pattern alignment, and reallocating areas to maximize qubit use efficiency, thereby optimizing the qubit area utilization and reducing fragmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional scheduling techniques are used for quantum jobs, then the scheduling process is simple, but the throughput and qubit utilization are inefficient
Solution Approach 1:
The patent segments quantum jobs into different patterns based on the number of qubits they require. Each pattern represents a specific configuration of qubit allocation areas, allowing the scheduling system to categorize and manage jobs more efficiently. This segmentation enables the system to handle multiple jobs with different resource requirements by assigning them to appropriate pre-defined patterns, thereby improving throughput without requiring overly complex scheduling logic.
Solution Approach 2:
The patent performs preliminary compilation of quantum jobs into standardized patterns before scheduling. By pre-defining allocation area patterns for different qubit counts and pre-compiling jobs to match these patterns, the system prepares jobs in advance for efficient scheduling. This preliminary action reduces the complexity of real-time scheduling decisions while maintaining high throughput, as the scheduler only needs to match pre-processed jobs with pre-defined patterns.
2Productivity
If quantum jobs are allocated without pattern constraints, then scheduling flexibility is high, but qubit area fragmentation increases
Solution Approach 1:
The patent applies local quality by defining specific patterns for different regions of the qubit area based on the number of qubits required. Each pattern represents a localized configuration that optimizes qubit utilization for jobs of a specific size. By matching jobs to appropriate local patterns rather than allowing arbitrary allocation, the system improves qubit utilization efficiency while maintaining continuity within each allocated region, thus reducing fragmentation.
Solution Approach 2:
The patent implements dynamic pattern matching and movement capabilities. When a quantum job completes execution, the system dynamically moves other jobs to fill the newly available qubit area while maintaining pattern integrity. This dynamic adjustment allows the system to respond to changing resource availability while preserving the structural benefits of patterns, thereby maintaining both high utilization efficiency and area continuity.
3Productivity
If quantum jobs are moved frequently to optimize allocation, then qubit utilization improves, but the scheduling overhead increases
Solution Approach 1:
The patent performs preliminary compilation of quantum jobs into standardized patterns before the scheduling and execution phases. By pre-organizing jobs according to their qubit requirements and matching them with predefined allocation patterns, the system reduces the need for frequent movements during execution. This preliminary organization minimizes scheduling overhead and time loss, as the majority of allocation decisions are made in advance rather than requiring dynamic adjustments.
Solution Approach 2:
The patent changes the parameter of job representation by compiling jobs into standardized patterns with specific qubit count classifications. This parameter transformation allows the scheduling system to make efficient allocation decisions based on discrete pattern categories rather than continuous optimization, reducing computational complexity and scheduling time while maintaining effective qubit utilization through pattern-based matching.
Data Source
AI summary
A computer acquires a first quantum job using a first number of qubits, the first quantum job having been compiled to be executable in a first pattern corresponding to the first number of qubits among a plurality of patterns corresponding to the numbers of qubits, each of the plurality of patterns defining the shape of an allocation area in a qubit area. The computer moves, upon completion of a third quantum job in the qubit area, a second quantum job to which an allocation area matching a second pattern corresponding to a second number of qubits is allocated and which uses the second number of qubits in a certain direction on the qubit area while maintaining the second pattern. The computer allocates an allocation area matching the first pattern to the first quantum job in an available area included in the qubit area after the second quantum job is moved.