Researchers have mathematically shown that a quantum neural network could help measure hard-to-access properties of quantum objects, potentially cheating the Heisenberg uncertainty principle. By injecting randomness into the network, scientists might determine multiple incompatible properties more precisely. This approach could speed up applications in quantum computing and chemistry.
The Heisenberg uncertainty principle limits how precisely certain quantum properties, like position and momentum, can be measured simultaneously. For quantum objects such as molecules or qubits in quantum computers, this makes predictions and assessments challenging, as measurements can interfere with each other.
Duanlu Zhou at the Chinese Academy of Sciences and his colleagues have proved mathematically that a quantum neural network (QNN) could overcome these issues. Traditional operations on qubits are often incompatible due to the uncertainty principle, similar to performing conflicting calculations on a number. However, a QNN with randomly chosen steps from a set can resolve this incompatibility.
Previous work showed randomness helps QNNs measure single properties effectively. Zhou's team extended this to multiple properties, including those constrained by the uncertainty principle. By applying consecutive random operations and unraveling results with statistical methods, the approach yields more precise outcomes than repeated single operations.
This is particularly useful for quantum computers, where understanding qubit properties is essential for benchmarking devices or emulating molecules and materials. Robert Huang at the California Institute of Technology notes that efficiently measuring many incompatible properties would allow scientists to learn about quantum systems much faster, aiding applications in chemistry and materials science, as well as scaling up quantum computers.
The method seems plausible for practical implementation, though its success may hinge on outperforming other randomness-based quantum measurement techniques. The findings appear in Physical Review B (DOI: 10.1103/qz9c-m3z4).