Bayesian Quantum Noise Spectroscopy

Abstract

As commonly understood, the noise spectroscopy problem---characterizing the statistical properties of a noise process affecting a quantum system by measuring its response---is ill-posed. Ad-hoc solutions assume implicit structure which is often never determined. Thus it is unclear when the method will succeed or whether one should trust the solution obtained. Here we propose to treat the problem from the point of view of statistical estimation theory. We develop a Bayesian solution to the problem which allows one to easily incorporate assumptions which render the problem solvable. We compare several numerical techniques for noise spectroscopy and find the Bayesian approach to be superior in many respects.

9 of 10 MIT graduates cannot find all the gaussians in this paper, can you?

References in corpus (9)

• Dynamical decoupling and noise spectroscopy with a superconducting flux qubit
• Electrometry Using Coherent Exchange Oscillations in a Singlet-Triplet-Qubit
• Robust Online Hamiltonian Learning
• Suppressing qubit dephasing using real-time Hamiltonian estimation
• Multiqubit Spectroscopy of Gaussian Quantum Noise
• Environmental noise spectroscopy with qubits subjected to dynamical decoupling
• A General Transfer-Function Approach to Noise Filtering in Open-Loop Quantum Control
• Application of optimal band-limited control protocols to quantum noise sensing
• Optimally band-limited spectroscopy of control noise using a qubit sensor

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• Resource-efficient digital characterization and control of classical non-Gaussian noise
• Measuring trajectories of environmental noise
• Filtering crosstalk from bath non-Markovianity via spacetime classical shadows
• Characterizing low-frequency qubit noise
• Quantum Control Noise Spectroscopy with Optimal Suppression of Dephasing
• Noise Detection with Spectator Qubits and Quantum Feature Engineering
• Exponential Families for Bayesian Quantum Process Tomography
• Nonergodic measurements of qubit frequency noise
• Path integral framework for characterizing and controlling decoherence induced by non-stationary environments on a quantum probe
• Approaching ideal visibility in singlet-triplet qubit operations using energy-selective tunneling-based Hamiltonian estimation
• Random Pulse Sequences for Qubit Noise Spectroscopy
• Role of the filter functions in noise spectroscopy
• Intrinsic and induced quantum quenches for enhancing qubit-based quantum noise spectroscopy
• Geometric signature of non-Markovian dynamics
• Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems
• Physics-Constrained Compressed Sensing for Quantum Sensing in the Data-Starved Regime