Recently, scientists have experimentally proved that machine learning can reconstruct quantum systems based on a small amount of data. This approach allows scientists to complete problems that have not been resolved for thousands of years in a matter of hours.
Particle systems such as electrons can exist in many different combinations, each with a specific probability of occurrence. In the field of quantum, unobserved systems do not exist in any combination, but are considered to be all possible combinations. This system is like Schrödinger's cat. When scientists measure it, the whole system collapses. In a specific form of composition.
Thus, this characteristic of the quantum system shows that scientists cannot observe the complex characteristics of the whole system through one experiment, but continuously study and analyze after repeated measurements until the state of the whole system can be determined.
But in this process, as the number of quantum numbers used in quantum systems increases, the complexity of the system will increase exponentially. For example, each electron has an upward or downward spin, and 5 electronic systems have 32 possible Combination; 100 electronic systems have 2 combinations of 100 times. In addition, quantum entanglement will also deepen the complexity of the quantum system, so the traditional method is not enough.
In the latest study, Giuseppe Caleo, an associate researcher at the Center for Computational Quantum Physics in New York, and Canadian scientists, used machine learning techniques to circumvent these limitations. They provide experimental measurements of quantum systems to software tools based on artificial neural networks. The software learns and attempts to mimic the behavior of the system. Once the software acquires enough data, it can accurately reconstruct the complete quantum system.
The researchers tested the software using simulation experimental data sets based on different quantum systems. The results show that the software is far superior to traditional methods, new technologies can handle larger systems, and help scientists verify that quantum computers are properly set up, and that quantum software is operating as expected.
In the future, this research will greatly promote the research and development of quantum computers. (Author: Lynn)
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