Quantum-enhanced magnetometry by phase estimation algorithms with a single artificial atom

Phase estimation algorithms are key protocols in quantum information processing. Besides applications in quantum computing, they can also be employed in metrology as they allow for fast extraction of information stored in the quantum state of a system. Here, we implement two suitably modified phase estimation procedures, the Kitaev and the semiclassical Fourier-transform algorithms, using an artificial atom realized with a superconducting transmon circuit. We demonstrate that both algorithms yield a flux sensitivity exceeding the classical shot-noise limit of the device, allowing one to approach the Heisenberg limit. Our experiment paves the way for the use of superconducting qubits as metrological devices which are potentially able to outperform the best existing flux sensors with a sensitivity enhanced by few orders of magnitude. Quantum computing algorithms can improve the performance of a superconducting magnetic field sensor beyond the classical limit. A qubit’s time evolution is often influenced by environmental factors like magnetic fields; measuring this evolution allows the magnetic field strength to be determined. Using classical methods, improvements in measurement performance can only scale with the square root of the total measurement time. However, by exploiting quantum coherence to use so-called phase estimation algorithms during the measurements, the scaling with measurement time can be driven beyond the classical limits. Andrey Lebedev at ETH Zurich and colleagues in Finland, Switzerland and Russia have applied this approach to superconducting qubits. They demonstrate both superior performance and improved scaling compared to the classical approach, and show that in principle superconducting qubits can become the highest-performing magnetic flux sensors.