This is a preview edition of Quantum Campus, which shares the latest in quantum science and technology. Read by more than 1,900 researchers, we publish on Fridays and are always looking for news from across the country. See something interesting? Be sure to share it.

Science magazine reviewed the current state of device-independent quantum key distribution, considering a new demonstration out of China. It used the security protocol across a pair of trapped Rydberg atoms 100 kilometers apart. The story features insights from ETH Zürich, ID Quantique, Universität Innsbruck, and the team at the University of Science and Technology of China.

It also offers a primer on device-independent QKD: "Entanglement is fundamentally monogamous: If two particles are maximally entangled then no other particle can join the connection. If the sender and receiver entangle a pair of particles across the network, they can each perform tests that confirm the particles’ properties are strongly correlated, well beyond chance. After this 'handshake,' they can be sure they’re the only ones on the channel. Then, other measurements on the entangled particles can establish a key, which can be shared with confidence that nobody can decipher it."

This research was published in Science. Read the Science news story.

A team led by QuTech at Delft University demonstrated a fast, single-measurement technique for reading out the fermionic parity that holds state information on Marjorana-based qubits. They used a minimal Kitaev chain with two quantum dots coupled by a superconductor. An RF resonator is connected to the superconductor, which senses the joint state of the two-dot system by measuring how charge can flow into and out of the superconducting condensate, according to the team.

This work was published in Nature. Another team at QuTech this week introduced a new chip architecture in Nature Electronics that could make it easier to build semiconductor spin-qubit processors.

AWS and QuEra released a technical report describing their quantum reservoir learning algorithm. They ran on Rydberg-atom quantum computers for machine learning tasks in image classification, time series prediction, and molecular property prediction. Reservoir learning is an attractive approach to ML because of its low training costs and because it can be run on a variety of quantum-based and classical systems.

Read the report on AWS’s quantum technologies blog. QuEra published on their quantum reservoir learning approach to molecular property prediction last year in Machine Learning and Deep Learning.

Quantum Campus is edited by Bill Bell, a science writer and marketing consultant who has covered physics and high-performance computing for more than 25 years. Disclosure statement.