Efficient Quantum State Tracking in Noisy Environments
arXiv:2205.06389 · doi:10.1088/2058-9565/aca049
Abstract
Quantum state tomography, which aims to find the best description of a quantum state -- the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of matrix-exponentiated gradient tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
arXiv comment10 pages, 3 figures
References in corpus (26)
- Prescription for experimental determination of the dynamics of a quantum black box
- On mutually unbiased bases
- Efficient quantum state tomography
- Practical Quantum Error Mitigation for Near-Future Applications
- Quantum process tomography of a controlled-NOT gate
- The Bloch Vector for N-Level Systems
- Optimal, reliable estimation of quantum states
- Measuring Entangled Qutrits and Their Use for Quantum Bit Commitment
- Qudit Quantum State Tomography
- Efficient tomography of a quantum many-body system
- Sample-optimal tomography of quantum states
- Sample-efficient learning of quantum many-body systems
- Recursively Adaptive Quantum State Tomography: Theory and Two-qubit Experiment
- Self-guided quantum tomography
- Practical Bayesian Tomography
- Experimental Estimation of Quantum State Properties from Classical Shadows
- Identifying an Experimental Two-State Hamiltonian to Arbitrary Accuracy
- Quantum process tomography via completely positive and trace-preserving projection
- Robust and Efficient High-dimensional Quantum State Tomography
- Quantum state reconstruction via continuous measurement
- Least-squares inversion for density-matrix reconstruction
- Continuous Measurement of a Non-Markovian Open Quantum System
- Online Learning of Quantum States
- Efficient online quantum state estimation using a matrix-exponentiated gradient method
- Quantum states tomography with noisy measurement channels
- Dynamic state reconstruction of quantum systems subject to pure decoherence