A Theoretical Framework for Thermally Resilient Quantum Memory Using Engineered Lattice Structures
Year: 2025
Type: preprint
Abstract: Quantum repeaters are indispensable for extending quantum communication beyond metropolitan scales, yet their core component that is quantum memory, remains constrained by cryogenic requirements and sub-millisecond storage times. This work develops a comprehensive theoretical framework for thermally resilient quantum memory via engineered lattice structures, with the goal of identifying the design conditions under which robust room-temperature operation could be achieved. We derive closed-form Lindblad-master-equation bounds on thermal decoherence, establish coherence-time limits as functions of material parameters and temperature, and quantify classical communication capacity via Holevo-bound analysis. We propose phononic band-gap engineering criteria for crystal lattices that, under ideal assumptions, suppress spin-phonon coupling by up to two orders of magnitude, and design concatenated XY dynamical-decoupling sequences optimized for broadband thermal noise at 300 K. In addition, we outline bias-preserving quantum error-correction codes tailored to amplitude-damping and dephasing asymmetries in thermal environments. End-to-end simulations predict coherence times of 10-50 ms under realistic parameter assumptions, with optimistic projections reaching 100 ms at 300 K contingent on achieving Q mode ≳ 6×10 8 and phononic suppression factors S lat ∼ 10 −3 not yet demonstrated at room temperature. Conservative scenarios still yield entanglement-generation rate improvements of 100-1000× in two-node repeater links under the assumptions-(Q mode = 6 × 10 8 , S lat = 10 −3 , SDD = 0.1, εp = 10 −4 , σt = 0.8 ns, τcyc = 0.5 ms, τ dec = 0.2 ms). These results are predictive and simulation-based, providing scaling laws, performance bounds, and design principles that can guide future experimental efforts toward practical, room-temperature quantum memories.
Author: Yalla Jnan Devi Satya Prasad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4414523455
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