GRADIENT CALCULATION FOR QUANTUM CIRCUIT USING TENSOR FLOW QUANTUM

Abstract

Although AI (ML) does not exactly duplicate frameworks in nature, it can discover a model of a framework and predict its performance. In recent years, traditional ML models have proven to be reliable in handling logical concerns, advancing picture preparation for immoral discovery, assessing seismic tremor post-quake tremors, predicting fantastic climatic examples, and establishes rules exoplanets. With the continued growth in quantum processing, the emergence of the most recent quantum ML models could have a significant impact on the world's most pressing concerns, resulting in breakthroughs in the fields of medicine, materials, detection, and correspondences. A quantum model can communicate with and summarize the data from a quantum mechanical source. However, to achieve quantum models, four categories will constantly be provided: cognitive knowledge and cross-breeding quantum old style models. Quantum information displays superposition and tangle, resulting in potential exchanges that would require an exponential number of local computational as-sets to communicate with or store. In this paper, we provide Tensor Flow Quantum in session 1, Concepts of Quantum Machine Learning with session 2, as well as a hybrid quantum-classical model and gradient computations for a proposed quantum circuit in session 3, which compares and contrasts two different models.