Predicting non-Markovian superconducting qubit dynamics from tomographic reconstruction

Abstract

Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence in quantum computers, yet is challenging to capture in terms of simple models. Here we show that a simple phenomenological dynamical model known as the post-Markovian master equation (PMME) accurately captures and predicts non-Markovian noise in a superconducting qubit system. The PMME is constructed using experimentally measured state dynamics of an IBM Quantum Experience cloud-based quantum processor, and the model thus constructed successfully predicts the non-Markovian dynamics observed in later experiments. The model also allows the extraction of information about cross-talk and measures of non-Markovianity. We demonstrate definitively that the PMME model predicts subsequent dynamics of the processor better than the standard Markovian master equation.

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• Analyzing the impact of time-correlated noise on zero-noise extrapolation
• Dissipative Dynamics of Graph-State Stabilizers with Superconducting Qubits
• Markovian and non-Markovian master equations versus an exactly solvable model of a qubit in a cavity
• Benchmarking quantum gates and circuits
• Modeling error correction with Lindblad dynamics and approximate channels
• Master Equation Emulation and Coherence Preservation with Classical Control of a Superconducting Qubit
• Limitations to Dynamical Error Suppression and Gate-Error Virtualization from Temporally Correlated Nonclassical Noise
• Empirical learning of dynamical decoupling on quantum processors
• Experimental realization of quantum non-Markovianity through the convex mixing of Pauli semigroups on an NMR quantum processor
• Nature and origin of the operators entering the master equation of an open quantum system
• Lindblad-like quantum tomography for non-Markovian quantum dynamical maps
• Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations
• Continuous quantum correction on Markovian and Non-Markovian models
• Lindbladian reverse engineering for general non-equilibrium steady states: A scalable null-space approach
• Data-Efficient Quantum Noise Modeling via Machine Learning
• Spectral analysis of the Koopman operator as a framework for recovering Hamiltonian parameters in open quantum systems