Non-Stationary Decoherence in Superconducting Qubits: Memory Multi-Fractional Brownian Motion and a Time-Dependent Quantum Brownian Motion Extension
Mahboob Ul Haq

TL;DR
This paper introduces a unified stochastic model using memory multi-fractional Brownian motion for superconducting qubits, capturing non-stationary noise and long-range correlations, leading to more accurate predictions of coherence times and decay behaviors.
Contribution
It develops a novel time-dependent quantum noise model with adaptive memory kernels and spectral density, improving understanding of non-Markovian effects in superconducting qubits.
Findings
Time-varying H(t) matches experimental 1/f spectra more accurately.
Simulations predict coherence times consistent with charge noise dominance.
Non-exponential decay patterns reveal limitations of Markovian models.
Abstract
Building upon our prior work [1], we present a unified stochastic drift model (SdM) for superconducting charge qubits based on memory multi-fractional Brownian motion (mmFBM). The classical sector employs a time-dependent Hurst exponent H(t) and adaptive memory kernel K(t,s), capturing non-stationary 1/f^beta noise and long-range temporal correlations inaccessible to conventional models. The quantum extension is formulated via a time-dependent Caldeira--Leggett environment with spectral density J(omega;t) = eta(t) omega_c^{1-s(t)} omega^{s(t)} exp(-omega/omega_c), where s(t) = 2H(t)-1, consistently reproducing beta(t) = 2H(t)-1. Four central results emerge: (1) relaxation and noise amplitudes act independently on energy decay; (2) time-varying H(t) matches experimental 1/f spectra more accurately than any constant exponent; (3) adaptive kernel dynamics preserve correlations without…
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