As quantum computing technology matures, the availability and performance of quantum devices are steadily improving. However, in the NISQ (Noisy Intermediate-Scale Quantum) era, the quantum bit error rate caused by quantum noise remains a significant constraint on computational accuracy and reliability. In quantum operating systems, quantum task scheduling is a core technology crucial for fully leveraging the performance potential of quantum processors. Currently, most multi-processor scheduling algorithms focus on task load balancing and do not directly consider the characteristics of quantum circuits and the differentiated error rates of quantum computing backends. In quantum computing, the impact of noise errors in single-qubit gates, two-qubit gates, and measurement gates on the fidelity of quantum gate circuits is crucial. Therefore, intelligently matching quantum tasks with the characteristics of backend processors to optimize computational accuracy is an urgent problem to be addressed. This paper proposes a new quantum task scheduling algorithm, which matches tasks with backends based on the weight of single-qubit gates, two-qubit gates, and measurement gates in quantum circuits, as well as the error rate characteristics of each quantum computing backend. Experimental results show that the proposed scheduling algorithm for improving the fidelity of quantum gate circuits outperforms static load balancing scheduling algorithms, resulting in a 4.39% increase in the fidelity of quantum tasks.
Error analysis is an important support for improving the precision of floating-point calculations. However, the floatingpoint error distribution does not have dominant characteristics, making it difficult to optimize floating-point calculation precision. This paper proposes the LFEE (Locating hot spots of floating-point expression errors) algorithm. The algorithm analyzes the error detection result of the expression to obtain the error hot spot, i.e., the step with the maximum error in the expression calculation sequence, followed by preforming precision optimization scheme for the error hot spot. The experimental results show that the LFEE algorithm can accurately locate the error hot spots and improve the accuracy of the function by optimizing the error hot spots.
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