Researchers Nickholas Gutierrez, Rodrigo Araiza Bravo, and Susanne Yelin from the University of Connecticut have published new findings in the field of quantum metrology, exploring how quantum neural networks can enhance the performance of quantum sensors. Their work, titled “Enhanced Squeezing and Faster Metrology from Layered Quantum Neural Networks,” was published in the journal Physical Review Letters.
Quantum metrology leverages the principles of quantum mechanics to achieve ultra-precise measurements. One key technique in this field is spin squeezing, which reduces the uncertainty in the collective spin of a system, enhancing measurement sensitivity. The researchers compared three different quantum architectures—quantum reservoir computers (QRCs), quantum perceptrons, and multi-layer quantum neural networks (QNNs)—to determine their effectiveness in generating and reversing spin squeezing during a sensing protocol.
The study found that a single quantum perceptron can generate the same optimal sensitivity as a QRC but does so more quickly due to the steering effect of the output qubit. When multiple perceptrons are stacked into a QNN, the squeezing time is further reduced. Specifically, a 2-layer QNN with N_in input qubits and N_out output qubits can reduce the optimal squeezing time by a factor of N_out while maintaining Heisenberg-limited phase sensitivity, which is the theoretical limit of precision for quantum measurements.
Moreover, the researchers discovered that using the layers sequentially—first to squeeze the inputs and then the inputs to squeeze the outputs—results in a constructive addition of the responses. This approach yields a sensitivity enhancement of sqrt(2) over a QRC when N_in equals N_out and requires a shorter total squeezing time. Extending this to L layers, the metrological gain scales as sqrt(L), and the required squeezing time decreases as 1/N_l, where N_l is the number of qubits per layer.
These findings demonstrate that the structure of quantum neural networks can be harnessed not only for computational tasks but also to engineer faster and more sensitive quantum sensors. This research could have significant implications for the energy sector, particularly in applications requiring highly precise measurements, such as quantum sensing for oil and gas exploration, monitoring of nuclear reactors, and advanced materials research for energy storage and conversion. By improving the speed and sensitivity of quantum sensors, these technologies could enhance the efficiency and accuracy of energy-related measurements, ultimately contributing to more sustainable and reliable energy solutions.
This article is based on research available at arXiv.