QCRAFT AutoScheduler: A library for maximizing qubit utilization on QPUs through quantum circuit multiprogramming
Year: 2025
Type: software-paper
Abstract: Quantum computing is emerging as a technology with the potential to solve complex problems in areas such as optimization, cryptography, and simulation beyond the capabilities of classical computers. Cloud-based quantum platforms have made quantum hardware accessible to researchers and developers worldwide. However, current usage models still face important limitations. Despite growing demand, quantum computers often remain under-utilized, as tasks are executed sequentially, blocking all available qubits regardless of their actual usage. This leads to long queue times, high execution costs, and inefficient resource utilization. To address these problems, the following paper presents QCRAFT AutoScheduler, a Python-based programming library that optimizes the use of Quantum Processing Units (QPUs). The proposed library automatically multiprograms a quantum circuit with multiple copies of itself until it reaches or approaches the maximum capacity of the processor. By multiprogramming circuits, the approach reduces idle qubits, reducing execution costs and reducing the number of required shots. To demonstrate these advantages, QCRAFT AutoScheduler has been validated on different real quantum computers provided by IBM Quantum and Amazon Braket. The obtained results demonstrate the effectiveness of this tool to improve resource utilization in quantum computing, obtaining an average shot reduction of 85% and 96% in AWS and IBM executions respectively. • A Python-based library for optimizing the execution of quantum circuits. • QCRAFT AutoScheduler provides a method to multiprogram quantum circuits for QPUs. • Maximizes the use of qubits, reduces the number of shots, and decreases execution costs. • Validated on real QPUs using multiple kinds of quantum algorithms.
Source: SoftwareX
Institution: Universidad de Extremadura
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W4415305987