Quantum sensing with nonclassical mechanical oscillators

Project funded by Research Council of Finland, grant 352189. Participating organisations named by the source: Aalto University. Funded amount: EUR 208,739, as stated by the source. Runs 2022-09-01 to 2026-06-30. Keywords: Physical sciences. Source: OpenAIRE Graph, from the funder's project data (collected by OpenAIRE on 2026-07-23); national funding data, not CORDIS. Abstract given by the source: Recent breakthroughs have led to creating true quantum states of solid-state mechanical objects. In the project we develop new protocols for sensing that make use of the unique quantum-mechanical properties of mechanical oscillators. For the purpose, we use different realizations in cavity optomechanics, quantum acoustics with superconducting qubits, and nano-electro-mechanics. While quantum limits to noise and sensitivity have long been an important consideration in laboratory tests of some mechanical sensors, our proposal aims to make use of distinctly quantum properties such as non-Gaussianity, multi-mode entanglement, and single-phonon nonlinearities to enhance the performance of these systems. In other words, analogously to what has long been done in atomic or spin-based sensors, we will explore using quantum states as a new resource for enhancing the capabilities of mechanical sensors.

Original source.

Source: OpenAIRE Graph: projects of national funders, OpenAIRE, from the project data of Research Council of Finland. Retrieved 2026-10-22. Licence: Creative Commons Attribution 4.0 (OpenAIRE Graph). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: Creative Commons Attribution 4.0 (CC BY 4.0).

AI-assisted summary: The project "Quantum sensing with nonclassical mechanical oscillators" is funded by the Research Council of Finland with a grant of EUR 208,739, running from September 1, 2022, to June 30, 2026, involving Aalto University. Suggested technology pillar: sensing. Generated by AI from the public source text; it may contain errors and the original source is authoritative.