Approaching optimal entangling collective measurements on quantum computing platforms

Entanglement is a fundamental feature of quantum mechanics and holds great promise for enhancing metrology and communications. Much of the focus of quantum metrology so far has been on generating highly entangled quantum states that offer better sensitivity, per resource, than what can be achieved classically. However, to reach the ultimate limits in multi-parameter quantum metrology and quantum information processing tasks, collective measurements, which generate entanglement between multiple copies of the quantum state, are necessary. Here, we experimentally demonstrate theoretically optimal single- and two-copy collective measurements for simultaneously estimating two non-commuting qubit rotations. This allows us to implement quantum-enhanced sensing, for which the metrological gain persists for high levels of decoherence, and to draw fundamental insights about the interpretation of the uncertainty principle. We implement our optimal measurements on superconducting, trapped-ion and photonic systems, providing an indication of how future quantum-enhanced sensing networks may look.

Automated summary

Entanglement, a fundamental feature of quantum mechanics, shows great potential for enhancing metrology and communications. Current focus in quantum metrology has been on generating highly entangled states for improved sensitivity. However, to achieve ultimate limits in multi-parameter quantum metrology and information processing, collective measurements that generate entanglement between multiple copies of quantum states are necessary. Optimal single and two-copy collective measurements for estimating non-commuting qubit rotations have been experimentally demonstrated, enabling quantum-enhanced sensing even under high decoherence levels and providing insights into the uncertainty principle. Superconducting, trapped-ion, and photonic systems were used to implement these optimal measurements, giving a glimpse into future quantum-enhanced sensing networks.