Quantum Computing: Atomic Clocks Make for Longer-Lasting Qubits

Cesium atoms and laser traps offer a more robust type of quantum computer

4 min read
Image: Weiss Laboratory/Penn State
Bright Qubits: Lasers hold cesium atoms in place in a 5-by-5 grid for a neutral-atom quantum-computer prototype built at Penn State. The atoms’ quantum states can be used to store information.
Image: Weiss Laboratory/Penn State

A decade ago, quantum computing was still something of a parlor game. Quantum-computer advocates could make bold claims about one promising technology or another because no one had yet figured out how to string together more than a handful of quantum bits (qubits).

Times have changed. IBM now has a 50-qubit machine, Intel is at 49 qubits, and Google has developed a 72-qubit device. And in September, Pennsylvania State University researchers announced they’d built the framework for a 125-qubit compute engine.

Keep Reading ↓ Show less

Stay ahead of the latest trends in technology. Become an IEEE member.

This article is for IEEE members only. Join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum’s articles, podcasts, and special reports. Learn more →

Membership includes:

  • Get unlimited access to IEEE Spectrum content
  • Follow your favorite topics to create a personalized feed of IEEE Spectrum content
  • Save Spectrum articles to read later
  • Network with other technology professionals
  • Establish a professional profile
  • Create a group to share and collaborate on projects
  • Discover IEEE events and activities
  • Join and participate in discussions

The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

Keep Reading ↓ Show less