How does fire opal quantum data make it smarter and more accurate?

Connection table

Summary and I. Introduction

Ii. Overview of error reduction methods

III. Methodology

IV. Results and discussions and references

The study examines the efficiency of the fire opal error suppression and AI circuit optimization system integrated with the quantum information processing platform for IBM’s multi -mode distribution loading algorithm. As a quantitative error analysis, using the use of Kulluback-Leds (KL), the results show that the fire opal can improve the distribution of the time on the time produced by our conditional quantum productive competing algorithm. In addition, Fire Opal’s performance continues to be consistent for complex circuits, despite the need to experiment more. The research concludes that OPAL’s error suppression and circuit optimization significantly increases quantum information processing processes and emphasizes the potential of practical practices. In addition, the study also evaluates the leading error reduction strategies such as zero noise extraapolate (ZNE), probability error (PEC), Pauli TWIRling, Measurement Error Reduction and Machine Learning Methods, technical application, quantum resources and scalability.

I. Introduction

Data loading is critical for many quantum algorithms and applications. However, this is a challenging problem when carried out on NISQ quantum equipment due to the longer circuit depth in the quantum subroutin. In this report, we show the benefits of error suppressing and AI circuit optimization to install very modal distributions as applied in our Conditional Quantum Productive Network (C-QGAN) algorithm to IBM Kyoto. In particular, the distributions produced with fire opal on quantum hardware produced much closer to the ideal distribution, and the results showed approximately 30% -40% improvement compared to the study without CT -based KL deviation analysis.

We suggested a new quantum algorithm known as C-QGAN. [1] To install multiple uniform distribution using the status records. This data loading technique was later applied with quantum amplitude estimation. [2] Evaluating complex financial tools called Asia option. C-Qgan-based state preparation has been shown to be expensive in terms of less calculation than other well-known technique, such as Groverrudolph. [3] And qgan [4] Potentially rejecting the powerful speed of the algorithm like QAE [5] When they approach stochastic processes.

Due to the noisy nature of the existing NISQ hardware, various error reduction techniques have been applied to improve the outputs when quantum algorithms were run on quantum equipment. Error reduction techniques are post -processing algorithms at the software level that can improve the distorted values ​​obtained from quantum equipment due to different noise sources. In addition, these techniques are applied to heal many variational quantum algorithms. [6] Quantum machine has many important applications in learning, chemistry and optimization. However, a potential disadvantage of many error reduction techniques is that they need extra classic and quantum calculation sources to save noiseless values, which limits their applications only for short -term circuits [7]. As a result, our report aims to discover the technique of error suppressing at the hardware level as applied in Fire Opal to understand the benefits of the state preparation process.