Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States

TL;DR

This paper presents two digital methods for preparing coherent states in quantum circuits, demonstrating high fidelity through variational algorithms and circuit decomposition, advancing quantum state generation techniques.

Contribution

Introduces novel digital approaches for coherent state preparation using circuit decomposition and variational algorithms, with analysis of quantum resources and high fidelity results.

Findings

High fidelity coherent states achieved via circuit decomposition.

Variational algorithms effectively generate coherent states.

Quantum resources analyzed for optimized circuit learning.

Abstract

Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning,and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources -- such as numbers of quantum gates, layers and iterations -- are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.