Entangled states from simple quantum graphs
Alison A. Silva, D. Bazeia, Fabiano M. Andrade
TL;DR
The paper addresses how to generate and control entanglement between two open quantum graphs by using a coherent controlled scattering operation that ties the entanglement to graph topology and tunable parameters. The authors model two QGs with two leads and derive explicit conditions for maximal entanglement, showing that maximal EE occurs when $|r_A|^2 = |t_A|^2 = \tfrac{1}{2}$ and appropriate relations hold for Bob’s amplitudes; introducing a controlled phase $\varphi$ enables a controlled-$Z$-like operation with maximal entanglement under $\varphi = (2n+1)\pi$. They illustrate gate realizations on a star graph $S_4$ and map parameter choices to identity, Pauli, and Hadamard gates, and they analyze a two-graph setup to map the EE landscape as a function of wave numbers and phase. The work suggests experimental feasibility with microwave networks and outlines avenues for extending the framework to multi-graph and multi-lead configurations, highlighting the potential of QG-based entanglement generation as a complementary quantum-information resource.
Abstract
Entanglement is a fundamental resource for many applications in quantum information processing. Here, we investigate how quantum transport in simple quantum graphs, modeled as controlled two-level quantum systems, can be utilized to generate entangled states through coherent control operations between two simple quantum graphs. A controlled operation is defined such that the scattering behavior of one quantum graph dynamically modifies the other. Our analysis reveals the precise conditions under which maximal entanglement or separability arises, including configurations that can be implemented via phase shifts in graph structures. Our findings demonstrate that the maximal entanglement in this system is closely related to recent results on randomized quantum graphs. These results provide new pathways for engineering entanglement using simple quantum graphs and suggest experimental feasibility using microwave networks.
