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

Area of Science:

• Quantum Information Science
• Quantum Optics
• Quantum Computing

Background:

• Coherent states, also known as displaced vacuum states, are fundamental in quantum information processing, quantum machine learning, and quantum optics.
• Efficient preparation of coherent states is essential for advancing these fields.

Purpose of the Study:

• To introduce and analyze two distinct digital methods for preparing coherent states within quantum circuits.
• To assess the fidelity and resource requirements of these preparation techniques.

Main Methods:

• Decomposition of the displacement operator into Pauli matrices using ladder operators (creation and annihilation operators).
• Application of Variational Quantum Algorithms (VQAs) with various ansatzes for coherent state generation.
• Analysis of quantum resources including gates, layers, and iterations for VQA-based methods.

Main Results:

• High fidelity of digitally generated coherent states was verified against the Poissonian distribution in Fock space.
• Quantum circuit learning using VQAs demonstrated high fidelity in generating coherent states.
• Resource analysis for VQAs provides insights into the efficiency of different ansatzes.

Conclusions:

• Digital preparation of coherent states in quantum circuits is feasible with high fidelity using the presented methods.
• Variational Quantum Algorithms offer a flexible approach for learning and generating coherent states, with fidelity dependent on ansatz choice.