Towards a quantum decision tree in a laser pumped four-level system

TL;DR

This paper proposes a scalable method to implement quantum decision trees using a laser-driven four-level atomic system with a diamond configuration, analyzed via Lie-algebraic formalism, for potential quantum computing applications.

Contribution

It introduces a novel framework for quantum decision trees in a four-level system, scalable to N-levels, with population control via pulse amplitude manipulation.

Findings

Population redistribution achieved with identical pulse profiles

Method scalable to N-level systems

Potential applications in quantum computing and decision-making

Abstract

In this study, we examine an innovative framework towards implementing quantum decision trees utilizing a laser-driven four-level system. We discuss a diamond-shaped atomic configuration, in which we apply Lie-algebraic formalisms to analyze the dynamics of the system. The system is perturbed by a Stokes pulse, represented as $\beta_j(t)$ (for $j=1,2$), which interacts with the atomic states $|0\rangle, |3\rangle$ and $|1\rangle, |2\rangle$. In addition, a pump laser, denoted as $\alpha_j(t)$, couples the states $|0\rangle, |1\rangle$ and $|2\rangle, |3\rangle$. By employing pulse profiles that possess identical temporal behavior but differ in amplitude, one can effectively redistribute the population from the initial ground state to the other energy levels. This technique facilitates the mimicry of a quantum decision tree. We highlight that the proposed methodology is scalable to N-level systems, enhancing its adaptability and potential utility in quantum computing and various decision-making applications. We introduce a novel framework for implementing quantum decision trees using a four-level laser-driven atomic system. Employing a diamond-shaped energy configuration, we analyze system dynamics through Lie-algebraic methods. Using pulse profiles with identical temporal structures but varying amplitudes, we achieve controlled population redistribution among energy levels, effectively simulating a quantum decision tree. This methodology is scalable to systems of \(N\) levels, offering potential applications in quantum computing and decision-making processes.