Quantum Circuit Knitting for Adaptive Qubit Group Allocation
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Solution Overview
Problem
Quantum hardware is limited and expensive, with long queuing times and inefficient utilization of qubits due to static job allocation, leading to idling resources and suboptimal computation throughput.
Innovation Solution
Implement workload splitting techniques to dynamically allocate quantum algorithms across usable qubit groups, leveraging piggy-backing on existing jobs by determining cut strategies and sharding circuits to maximize qubit utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static job allocation is used in quantum cloud platforms, then job scheduling is simple, but qubit utilization is inefficient and resources idle
Solution Approach 1:
The patent implements dynamic load management by continuously monitoring qubit availability and job requirements, then adapting job allocation in real-time. The system dynamically assigns jobs to qubit groups based on current resource availability, replacing static allocation with adaptive scheduling that responds to changing quantum hardware states and job demands.
Solution Approach 2:
The patent divides the quantum algorithm into separate qubit groups that can be independently allocated and executed. By segmenting the quantum circuit into distinct qubit groups, the system can selectively assign portions of the algorithm to available quantum resources, enabling finer-grained resource utilization and reducing idle time.
2Adaptability or versatility
If circuit knitting is used to execute quantum algorithms, then large quantum circuits can be executed on limited QPUs, but simulation overhead scales exponentially
Solution Approach 1:
The patent segments the quantum algorithm into multiple qubit groups that can be executed in parallel on different quantum processors. This segmentation allows the system to distribute the computational workload across multiple QPUs simultaneously, reducing the simulation overhead that would otherwise scale exponentially for large circuits executed on a single device.
Solution Approach 2:
The patent introduces a new dimension to circuit execution by organizing qubits into groups that can be allocated across multiple quantum processors simultaneously. This multi-dimensional approach to algorithm execution allows parallel processing of quantum circuits, reducing the effective simulation overhead compared to sequential execution on a single QPU.
3Productivity
If qubits are allocated to jobs without dynamic adjustment, then allocation is fast, but computation throughput is suboptimal due to idling
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor qubit availability, job status, and system load. Based on this feedback, the load manager dynamically adjusts job allocation decisions, prioritizing jobs that can utilize currently available qubits and reducing queuing time for jobs that match available resource capabilities.
Solution Approach 2:
The patent performs preliminary analysis of job requirements and qubit availability before finalizing allocations. By pre-processing job characteristics and matching them with currently available qubit groups, the system can prepare and schedule jobs more efficiently, reducing queuing time and optimizing computation throughput.
Data Source
AI summary
In an approach to improve usage efficiency in quantum machines embodiments determine a plurality of cut strategies for a quantum algorithm and determine a plurality of usable qubit groups of a quantum system. Additionally, embodiments cut the quantum algorithm based on each portion of a cut algorithm being computable on a portion of the plurality usable qubit groups. Further, embodiments, apply a portion of the cut algorithm to the portion of the plurality of usable qubit groups.