Adaptive Circuit Learning for Quantum Metrology
Quantum sensing is an important application of emerging quantum technologies.
We explore whether a hybrid system of quantum sensors and quantum circuits can
surpass the classical limit of sensing. In particular, we use optimization
techniques to search for encoder and decoder circuits that scalably improve
sensitivity under given application and noise characteristics. Our approach
uses a variational algorithm that can learn a quantum sensing circuit based on
platform-specific control capacity, noise, and signal distribution. The quantum
circuit is composed of an encoder which prepares the optimal sensing state and
a decoder which gives an output distribution containing information of the
signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio
(SNR). Furthermore, this learning algorithm can be run on real hardware
scalably by using the "parameter-shift" rule which enables gradient evaluation
on noisy quantum circuits, avoiding the exponential cost of quantum system
simulation. We demonstrate up to 13.12x SNR improvement over existing fixed
protocol (GHZ), and 3.19x Classical Fisher Information (CFI) improvement over
the classical limit on 15 qubits using IBM quantum computer. More notably, our
algorithm overcomes the decreasing performance of existing entanglement-based
protocols with increased system sizes.
Ziqi Ma Pranav Gokhale Tian-Xing Zheng Sisi Zhou Xiaofei Yu Liang Jiang Peter Maurer Frederic T. Chong