Taking the Noise out of Quantum Measurements Through Machine-Learning Algorithms
Quantum autoencoders to denoise quantum measurements
Points to note…
+ Many research groups worldwide are currently trying to develop instruments to collect high-precision measurements, such as atomic clocks or gravimeters. Some of these researchers have tried to achieve this using entangled quantum states, which have a higher sensitivity to quantities than classical or non-entangled states.
“Quantum machine learning is a very promising topic, as it can combine the versatility of machine learning with the power of quantum algorithms,” Bondarenko and Feldmann told Phys.org via email. “Machine learning is a ubiquitous method for data analysis.”
+ Just like traditional machine-learning algorithms, quantum machine-learning algorithms depend on a series of variational parameters that need to be optimized before an algorithm can be used to analyze data. To learn the correct parameters, the algorithm needs first to be trained on data related to the task it is designed to complete (e.g., pattern recognition, image classification, etc.).
+ “When we say quantum machine learning, we mean that the input and the output of the algorithm are quantum states, for example, of some number of qubits (quantum bits), which can be realized, for instance, using superconductors,” Bondarenko and Feldmann said. “The algorithm that maps the input state to the output state is meant to be implemented on a quantum computer. The variational parameters, which have to be optimized, are classical parameters of the transformations that are performed on the quantum computer.”
+ The two researchers wanted to test whether the quantum machine leaning algorithm previously developed by Bondarenko, Osborne and their other colleagues could be used to clean up data collected using quantum-enhanced metrology tools. This ultimately led to the development of the quantum autoencoders introduced in their recent paper.
+ While quantum machine learning techniques and quantum computers have been found to perform well in a variety of tasks, researchers are still trying to identify the practical applications for which they could be of most use. The recent study carried out by Bondarenko and Feldmann offers a clear example of how quantum machine learning methods could ultimately be used in real-world scenarios.
+ In the future, the quantum autoencoders developed by these two researchers could be used to improve the reliability of measurements collected using quantum-enhanced tools, particularly those using many-body entangled states. In addition, they could serve as interfaces between different quantum architectures.
Content may have been edited for style and clarity.