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This research proposes a novel method to engineer quantum memory effects by manipulating the spectral density of environmental baths, offering a practical pathway to enhance non-Markovianity in quantum technologies. By introducing band gaps into both bosonic and fermionic environments, the study provides a tunable strategy that could improve coherence times and control fidelity in future quantum devices. The work bridges theoretical physics with potential experimental implementations, directly advancing the toolkit for quantum information processing.
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Engineering non-Markovianity from spectral shaping of bosonic and fermionic baths

arXiv1 min read

arXiv:2609.14341v1 Announce Type: new Abstract: Non-Markovianity is the property that a physical system's future state depends on its previous dynamical history, namely the system has memory of its past. It is a key resource for quantum technologies but it is difficult to engineer without specific strategies. Here, we show that introducing a band gap in the environmental spectral density of both bosonic and fermionic baths provides a direct route to tuning non-Markovianity over a wide range. We demonstrate this effect by means of numerical simulations across bosonic and fermionic environments and discuss experimental tests of our predictions.

Abstract:Non-Markovianity is the property that a physical system's future state depends on its previous dynamical history, namely the system has memory of its past. It is a key resource for quantum technologies but it is difficult to engineer without specific strategies. Here, we show that introducing a band gap in the environmental spectral density of both bosonic and fermionic baths provides a direct route to tuning non-Markovianity over a wide range. We demonstrate this effect by means of numerical simulations across bosonic and fermionic environments and discuss experimental tests of our predictions.

arXiv:2609.14341 [quant-ph]

(or arXiv:2609.14341v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.14341 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 183 words

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