Related Experiment Videos
Machine-Learned Tuning to Protected States by Probing Noise Resilience.
Rodrigo A Dourado1,2, Nicolás Martínez-Valero3, Jacob Benestad4
1Universidade Federal de Minas Gerais, Departamento de Física, C. P. 702, 30123-970, Belo Horizonte, Minas Gerais, Brazil.
Physical Review Letters
|July 10, 2026
Summary
Researchers developed a machine learning method to find protected quantum states, crucial for quantum technologies. This approach injects noise to identify the most resilient configurations, simplifying experimental tuning for Majorana bound states.
Area of Science:
- Quantum physics
- Condensed matter physics
- Quantum information science
Background:
- Protected quantum states offer inherent noise resilience, vital for advancing quantum technologies.
- Identifying these states experimentally is challenging due to their occurrence in narrow parameter spaces.
Purpose of the Study:
- To develop a machine learning method for efficiently tuning quantum systems to protected regimes.
- To demonstrate the method's effectiveness in finding noise-resilient configurations, specifically for Kitaev chains.
Main Methods:
- Utilized a machine learning approach involving noise injection into quantum systems.
- Employed the covariance matrix adaptation evolutionary strategy to minimize ground state splitting.
- Applied the method to short quantum dot-based Kitaev chains subjected to parameter fluctuations.
Main Results:
- Successfully tuned systems to protected configurations, exhibiting well-separated Majorana bound states.
- Demonstrated convergence to noise-resilient states through direct search for resilience.
- Verified method robustness against variations in system parameters and chain length.
Conclusions:
- The developed machine learning method reliably tunes quantum systems to protected states.
- This technique is applicable to finding various protected states, including isolated Majorana bound states.
- Provides a practical approach for experimental realization of noise-resilient quantum systems.
Related Concept Videos
01:19Survival Tree
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...
Building a Survival Tree
Constructing a survival tree begins...
01:25Multimachine Stability
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
01:25Distribution Reliability and Automation
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
01:14Avoidance Learning and Learned Helplessness
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...