Catenaa, Saturday, January 17, 2026- Researchers have proposed that quantum neural networks (QNNs) may allow measurements of multiple quantum properties previously restricted by the Heisenberg uncertainty principle, according to a study by the Chinese Academy of Sciences.
The principle limits simultaneous precision of certain quantum properties, such as position and momentum, complicating predictions for molecules or qubits in quantum computers.
The team, led by Duanlu Zhou, mathematically demonstrated that injecting controlled randomness into a QNN can extract information from incompatible quantum measurements.
By applying many consecutive random operations and using specialized statistical methods, the network can reconstruct more precise estimates of several properties than traditional sequential measurements allow.
This approach addresses challenges in benchmarking qubits and simulating quantum systems. In standard methods, certain operations interfere with subsequent measurements, limiting the accuracy of property determinations.
QNNs overcome this by spreading the measurement process across randomized operations that can later be analyzed collectively.
Experts note the method could accelerate research in chemistry, materials science, and quantum computing by enabling faster and more efficient characterization of quantum systems.
Robert Huang of the California Institute of Technology said the technique could improve understanding of larger quantum devices while highlighting that practical success depends on its performance relative to other randomness-based measurement strategies.
While still theoretical, the findings suggest that quantum machine learning could partially bypass traditional measurement constraints, offering new avenues for extracting information from quantum systems and improving quantum computational models.
