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  3. Green Function Unlocks Quantum Topological Superconductivity
Quantum Computing

Green Function Unlocks Quantum Topological Superconductivity

Posted on November 13, 2025 by Jettipalli Lavanya4 min read
Green Function Unlocks Quantum Topological Superconductivity

Green Function Unlocks Driven Topological Superconductivity, a Quantum Physics Breakthrough

A key theoretical framework for describing Floquet topological phases in driven superconductor semiconductor hybrids has been devised by researchers using the Green function technique. By appropriately taking into account the self-energy created by the superconducting proximity effect, particularly in the presence of periodic driving, this advancement solves a fundamental constraint in modelling these systems.

In order to create exotic quasiparticles like Majorana zero modes (MZMs), which are crucial for achieving fault-tolerant quantum computing, hybrid superconductor semiconductor devices are important platforms. Floquet topological superconductivity, where systems are periodically pushed to create increased tunability and stability, has been the focus of current theoretical work, whereas early tests concentrated on static systems. Majorana pi modes (MPMs), which exist at a non-zero energy set by the drive, are one type of unique excitation that can also arise as a result of driving.

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Overcoming the Approximation Barrier

By ignoring the frequency dependence of the self-energy created in the semiconductor, the superconducting component in hybrid devices is typically viewed simply as a Cooper pair reservoir, simplifying the study. But when periodic driving is added, researchers have now found that this conventional approximation breaks down.

The semiconductor inevitably links to the superconducting bath’s metallic-like bands when driving is applied. Unwanted level broadening and dissipation are introduced by this connection, which typically masks the hybrid’s topological characteristics. A method that appropriately takes this level broadening into account is needed to anticipate Floquet topological phases.

The Green Function Formalism: A Comprehensive Tool

The quasi-energy operator (QEO) in the new framework is defined using the Green function formalism. Topological invariants in a regularly driven system can be computed using this method. The method is to convert the time-dependent Hamiltonian into a time-independent effective Floquet Hamiltonian by applying Floquet theory. The system’s Fourier harmonics in the Nambu space are then used to generate a Floquet Green’s function, which is further examined.

One of the main benefits of this approach is its capacity to manage intricate systems and interactions, like electron-photon coupling. Crucially, it provides comprehensive details on bulk and edge features, which are essential for comprehending Majorana modes.

The exact approach for defining the topological phase diagrams is one of the main conclusions of this work. Only the hermitian portion of the semiconductor’s retarded Green function is used to construct the QEO. The quasi energy band structure and the topological phase transitions are determined by this hermitian component.

However, the level broadening of the quasi-energy eigenvectors is computed using the independent anti-Hermitian element of the self-energy. This broadening is crucial in limiting the experimental observability of the phases, even though it has no effect on the topology itself. Together with the dampening effects of broadening, this separation provides a sharper image of the architecture of the bands.

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Application to Driven Nanowires

A single-channel Rashba nanowire tunnel connected to a conventional superconductor was subjected to both static and time-periodic components of a Zeeman magnetic field oriented along the nanowire’s axis to test the theoretical approach in a real-world hybrid system. This particular driven hybrid enables topologically non-trivial superconducting phases with integer winding numbers for both Majorana zero modes and Majorana pi modes, hence respecting a dynamical chiral symmetry.

Different topological and broadening effects across several driving frequency regimes were identified by the analysis:

Low Driving Frequencies (much smaller than the pairing gap): Broadening effects are minimal at low driving frequencies (far lower than the pairing gap) since the self-energy is almost entirely hermitian in this regime. Both MZMs and MPMs are very resistant to dissipation and appear throughout large parameter ranges.

Intermediate Driving Frequencies (comparable to the pairing gap): Broadening effects become important at intermediate driving frequencies, which are comparable to the pairing gap. The bands surrounding the pi quasi energy are significantly more sensitive to broadening, whereas quasi energy bands close to zero energy (MZMs) are only marginally impacted. This significant level broadening implies that it is challenging to observe the topological characteristics linked to Majorana pi modes in experiments conducted in this frequency range.

High Driving Frequencies (much larger than the pairing gap): At high driving frequencies (far greater than the pairing gap), the Majorana pi modes cease to exist and the system exhibits quasi-static behavior. The broadening is negligible, and the topological phase diagram for the zero modes is nearly the same as that of the undriven system.

The foundation for further research on driven topological superconductivity is provided by this Green function framework. The results indicate that low driving frequencies, where broadening is minimal, are essential for designing robust Majorana pi modes. The paradigm opens the door to investigating novel driving system-specific phenomena, like pi-mode-mediated alternating current Majorana Josephson effects.

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Tags

Floquet topologicalFloquet topological superconductivityGreen Function FormalismMajorana pi modesMajorana zero modesMZMs and MPMsQuasi-energy operator

Written by

Jettipalli Lavanya

Jettipalli Lavanya is a technology content writer and a researcher in quantum computing, associated with Govindhtech Solutions. Her work centers on advanced computing systems, quantum algorithms, cybersecurity technologies, and AI-driven innovation. She is passionate about delivering accurate, research-focused articles that help readers understand rapidly evolving scientific advancements.

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