Qubit Sharing Across Simultaneous Quantum Job Execution
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Solution Overview
Problem
Current quantum computing systems face limitations in executing multiple quantum jobs and trained models simultaneously due to redundant calculations, leading to wasted qubits and reduced capacity, as they often require separate processors and cannot efficiently share qubits across different tasks.
Innovation Solution
The method identifies matching qubit groups across multiple quantum jobs and models based on starting state and gate structure, allowing for dynamic reset and reuse, and generates a single compressed quantum job or model that can share qubits, thereby optimizing qubit utilization and reducing redundant calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate processors are used for each quantum job and model, then reliability is improved, but device complexity increases and qubit utilization decreases
Solution Approach 1:
The patent merges multiple quantum jobs and models onto a single quantum processor by identifying and sharing common qubit groups. The system combines quantum circuits that use identical qubits with matching starting states and gate structures, allowing them to execute simultaneously on the same processor without interference, thus reducing processor complexity while maintaining execution reliability.
Solution Approach 2:
The patent makes quantum processors universal by enabling a single processor to handle multiple different quantum jobs and models simultaneously. The system achieves this by dynamically identifying qubit groups that can be reused across different computational tasks, allowing the processor to adaptively serve multiple functions without requiring dedicated hardware for each task.
2Reliability
If qubits are allocated separately for each quantum job and model, then reliability is improved, but qubit utilization decreases
Solution Approach 1:
The patent implements a qubit recovery and reuse mechanism where qubits are dynamically released back to the pool after completing their computational task, and then reallocated to new tasks. The system tracks qubit usage states and automatically recovers qubits from completed quantum jobs, making them available for subsequent jobs and models, thereby increasing overall qubit utilization while maintaining computation reliability through proper state management.
Solution Approach 2:
The patent introduces dynamic qubit allocation and deallocation based on real-time computational needs. The system continuously monitors quantum job execution states and dynamically adjusts qubit assignments, allowing qubits to transition between being allocated to specific jobs and being available for new assignments. This dynamic management optimizes qubit utilization while ensuring reliable execution through proper state tracking.
3Productivity
If quantum jobs and models are executed simultaneously on the same processor, then productivity increases, but device complexity increases
Solution Approach 1:
The patent segments quantum jobs and models into discrete quantum circuits with identified qubit groups. By breaking down complex computational tasks into smaller circuit segments that can be independently analyzed and scheduled, the system enables simultaneous execution on the same processor. The segmentation allows the scheduler to manage multiple jobs through structured circuit representations, increasing productivity while controlling scheduling complexity through systematic organization.
4Quantity of substance
If qubit groups are shared across multiple quantum jobs and models, then qubit utilization increases, but manufacturing precision requirements increase
Solution Approach 1:
The patent performs preliminary analysis and matching of qubit groups before executing quantum jobs. The system pre-identifies qubits with identical starting states and gate structures across different jobs and models, creating a matching database that guides subsequent qubit sharing decisions. This preliminary action ensures high precision in qubit selection and matching, enabling safe sharing that increases utilization while maintaining the required manufacturing precision through careful pre-screening of compatible qubits.
Data Source
AI summary
A method, system, and computer program product for qubit sharing across simultaneous quantum job and/or model execution. Qubit groups within quantum jobs and/or trained models that match with respect to a starting state and a gate structure are identified. Furthermore, qubit groups that are considered for dynamic quantum job and/or model reset and reuse for another computation during a simultaneous quantum job and/or model execution are identified. Based on such identified qubit groups, a record of potential quantum job and/or model minimizations is created. A potential quantum job and/or model minimization is removed one at a time from the record until the quantum jobs and/or models can be positioned on the coupling map. Once that occurs, single compressed quantum jobs and/or models are generated that each use two or more quantum jobs and/or models that can share qubits based on the current record of potential quantum job and/or model minimizations.