How to make Fire Opal download quantum data more intelligent and more accurate

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Summary and I. Introduction

the second. Overview of ways to reduce errors

Third. methodology

Fourth. Results, discussions, and references

The study is studying the effectiveness of the Opal fire error and the IBM computing circle of computing platform in IBM for the supply of multimedia distribution. Using the Kullback-Leibler (KL) as a quantitative error analysis, the results indicate that Fire OPAL can improve in the time-based distributions resulting from the 30-40 %-of-cuminous reproductive algorithm compared to simulation results. In addition, Fire OPAL performance remains consistent with complex circles despite the needs of more experiments. The research concludes that the Fire OPAL error and improving the circle greatly enhances quantum computing processes, highlighting its potential for practical applications. In addition, the study also reviews the leading mistake strategies, including the extrapolation of the zero noise (ZNE), canceling the probability error (PEC), Pauli, alleviating the measuring error, machine learning methods, evaluating its advantages and disadvantages in terms of technical implementation, quantitative resources, and expansion.

I. Introduction

Download data is very important for many algorithms and quantum applications. However, it is a difficult problem when implementing the quantum NISQ devices due to the depth of the longest circle in this quantum sub -routine. In this report, we show the benefit of suppressing errors and improving the circle that supports AI in Fire OPAL to download multimedia distributions on IBM Kyoto as it was implemented in the Netterrum Commonancan (C-SGAN). Specifically, the distributions created with Fire OPAL on quantum devices produced results much closer to the ideal distributions, and showed an approximate improvement of 30 % – 40 % in the results for operation without the KL diverse analysis on this.

Previously, we suggested a new quantity algorithm known as C-SGAN [1] To download multiple uniform distributions using the status records. Then the data download technology was applied by assessing the quantum capacity [2] To evaluate the complex financial tool is called the Asian option. It turns out that the preparation of the C-SGAN states is lower altogether than other known technologies such as GRODRudolph [3] And QGAN [4] Which can deny acceleration [5] When random processes are close.

Due to the loud nature of the current NISQ devices, various errors have been applied to enable the improvement of outputs when running quantum algorithms on quantum devices. The techniques of relieving error are post -processing algorithms, which occur at the program level, which can improve in the distorted values ​​obtained from quantum devices due to various noise sources. In addition, these technologies have been applied to improvement in many variable quantum algorithms [6] It has many important applications in quantum learning, chemistry and improvement. However, there can be possible defects for many error relief techniques as they require classic calculation resources and an additional method for restoring noise values, which only limit their applications for short circles [7]. As a result, our report aims to explore the technique of repression of errors, which occurs at the level of devices, as it was implemented in Fire Opal to understand its benefit in the state presentation.