Noise Removal in IBM Real Time Quantum Computer with Deviation Quantum Algorithm in Comparison with Variational Quantum Eigensolver Algorithm
P. Venkata Ramana, T. Suresh Balakrishnan · 2024
The fundamental purpose of this study is to reduce the amount of noise that is produced by IBM quantum computers that are running in real time. This will be accomplished by adopting quantum computing methodologies that are more exact and are drawn from real-world settings. The Constituents and the Methods involved: It is possible to choose from a total of twenty different iterations for both of the groups. On the other hand, the algorithm that constitutes Group 2 is known as the Quantum Eigensolver Algorithm Variational, and the algorithm that constitutes Group 1 is known as the Deviation Quantum Algorithm. There are ten iterations in both Group 1 and Group 2, and each of them has a confidence interval of 95% and a G power range of 80%. Both groups are comprised of one another. Observations and discoveries: The Deviation Quantum method (32%), which is more accurate in terms of accuracy, is more accurate than the Variational Quantum Eigensolver Method (24%), which is less accurate with regard to accuracy. It was found that the hypothesis was accurate after the results of the independent sample T test were analyzed. Furthermore, it is worth noting that the mean number of standard deviations for accuracy detection was two, and the significance value was 0.000 (p<0.05). Due to the fact that this is the case, it is feasible to arrive at the conclusion that the Deviation Quantum technique has an accuracy of 32%, which surpasses the Variational Quantum's precision Eigensolver method, which is 24%.