Generative Data Intelligence

Tag: displacement

What Is a Prompter? Super Prompts Explained

Chatbots and AI-generative art have become hugely popular in a short space of time, but it can be tricky using prompts to get the...

Capacitive displacement nanosensors are made to measure in extreme environments

From synchrotron light sources to long-term space missions, nanopositioning specialist Queensgate is eyeing niche markets and extreme operating conditions for its...

Material memories can be erased

Disordered materials can “remember” deformations they have previously experienced – and they can be made...

Local and scalable detection of genuine multipartite single-photon path entanglement

Quantum 6, 671 (2022).

https://doi.org/10.22331/q-2022-03-22-671

How can a multipartite single-photon path-entangled state be certified efficiently by means of local measurements? We address this question by constructing an entanglement witness based on local photon detections preceded by displacement operations to reveal genuine multipartite entanglement. Our witness is defined as a sum of three observables that can be measured locally and assessed with two measurement settings for any number of parties $N$. For any bipartition, the maximum mean value of the witness observable over biseparable states is bounded by the maximum eigenvalue of an $Ntimes N$ matrix, which can be computed efficiently. We demonstrate the applicability of our scheme by experimentally testing the witness for heralded 4- and 8-partite single-photon path-entangled states. Our implementation shows the scalability of our witness and opens the door for distributing photonic multipartite entanglement in quantum networks at high rates.

Bitcoin Is Not Democratic Part Three: The Separation Of Economy And Politics

Part three of the “Bitcoin Is Not Democratic” series explores the building blocks necessary to construct a civilization that transcends politics.

Lessons Learned on Language Model Safety and Misuse

The deployment of powerful AI systems has enriched our understanding of safety and misuse far more than would have been possible through research alone. Notably:

  • API-based language model misuse often comes in different forms than we feared most.
  • We have identified limitations in existing language model evaluations that we are

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