Close Menu
  • Home
  • AI Models
    • DeepSeek
    • xAI
    • OpenAI
    • Meta AI Llama
    • Google DeepMind
    • Amazon AWS AI
    • Microsoft AI
    • Anthropic (Claude)
    • NVIDIA AI
    • IBM WatsonX Granite 3.1
    • Adobe Sensi
    • Hugging Face
    • Alibaba Cloud (Qwen)
    • Baidu (ERNIE)
    • C3 AI
    • DataRobot
    • Mistral AI
    • Moonshot AI (Kimi)
    • Google Gemma
    • xAI
    • Stability AI
    • H20.ai
  • AI Research
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Microsoft Research
    • Meta AI Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding & Startups
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • Expert Insights & Videos
    • Google DeepMind
    • Lex Fridman
    • Matt Wolfe AI
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • Matt Wolfe AI
    • The TechLead
    • Andrew Ng
    • OpenAI
  • Expert Blogs
    • François Chollet
    • Gary Marcus
    • IBM
    • Jack Clark
    • Jeremy Howard
    • Melanie Mitchell
    • Andrew Ng
    • Andrej Karpathy
    • Sebastian Ruder
    • Rachel Thomas
    • IBM
  • AI Policy & Ethics
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
    • EFF AI
    • European Commission AI
    • Partnership on AI
    • Stanford HAI Policy
    • Mozilla Foundation AI
    • Future of Life Institute
    • Center for AI Safety
    • World Economic Forum AI
  • AI Tools & Product Releases
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
    • Image Generation
    • Video Generation
    • Writing Tools
    • AI for Recruitment
    • Voice/Audio Generation
  • Industry Applications
    • Finance AI
    • Healthcare AI
    • Legal AI
    • Manufacturing AI
    • Media & Entertainment
    • Transportation AI
    • Education AI
    • Retail AI
    • Agriculture AI
    • Energy AI
  • AI Art & Entertainment
    • AI Art News Blog
    • Artvy Blog » AI Art Blog
    • Weird Wonderful AI Art Blog
    • The Chainsaw » AI Art
    • Artvy Blog » AI Art Blog
What's Hot

Perplexity AI Predicts XRP, Shiba Inu, Pepe Prices by 2025

Paper page – Efficient Machine Unlearning via Influence Approximation

Endless Announces Stability AI Integration to Accelerate Decentralized AI

Facebook X (Twitter) Instagram
Advanced AI News
  • Home
  • AI Models
    • OpenAI (GPT-4 / GPT-4o)
    • Anthropic (Claude 3)
    • Google DeepMind (Gemini)
    • Meta (LLaMA)
    • Cohere (Command R)
    • Amazon (Titan)
    • IBM (Watsonx)
    • Inflection AI (Pi)
  • AI Research
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Meta AI Research
    • Microsoft Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • AI Experts
    • Google DeepMind
    • Lex Fridman
    • Meta AI Llama
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • The TechLead
    • Matt Wolfe AI
    • Andrew Ng
    • OpenAI
    • Expert Blogs
      • François Chollet
      • Gary Marcus
      • IBM
      • Jack Clark
      • Jeremy Howard
      • Melanie Mitchell
      • Andrew Ng
      • Andrej Karpathy
      • Sebastian Ruder
      • Rachel Thomas
      • IBM
  • AI Tools
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
  • AI Policy
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
  • Industry AI
    • Finance AI
    • Healthcare AI
    • Education AI
    • Energy AI
    • Legal AI
LinkedIn Instagram YouTube Threads X (Twitter)
Advanced AI News
MIT News

Light has two identities that are impossible to see at the same time

By Advanced AI EditorAugust 2, 2025No Comments5 Mins Read
Share Facebook Twitter Pinterest Copy Link Telegram LinkedIn Tumblr Email
Share
Facebook Twitter LinkedIn Pinterest Email


Two centuries ago, the double-slit experiment revealed the strange nature of light – somewhat wave and somewhat particle. This quietly launched modern quantum theory.

Now, a team at the Massachusetts Institute of Technology (MIT) has stripped that classroom favorite to its bare bones and proved, once again, that you can never observe light behaving as both a particle and a wave at the same time.


EarthSnap

Wolfgang Ketterle of MIT’s Research Laboratory of Electronics (RLE) led the work with graduate student Vitaly Fedoseev and colleagues. They made use of ultracold atoms spaced just a few ten-thousandths of an inch apart in a vacuum lattice.

Their results reset the century-old argument between Albert Einstein and Niels Bohr – this time, in favor of Bohr’s quantum view.

Updating a quantum certainty classic

The original demonstration, performed by Thomas Young in 1801, let sunlight pass through two narrow slits and produced an interference pattern, proving that light can travel as a wave.

More than a century later, Einstein proposed adding a delicate balance to each slit so that a passing photon would nudge it.

This setup, he argued, would let observers detect which path the particle took while still preserving the interference stripes.

Bohr responded that the Heisenberg uncertainty principle would make any such measurement blur the pattern beyond recognition, preserving the mystery.

Physicists have since tried countless versions of the experiment, using electrons, neutrons, and even full-sized molecules – each time confirming Bohr’s stance.

A recent study even formalized a conservation rule: the more information one gains about a particle’s path, the less visible its wave behavior becomes.

“Einstein and Bohr would have never thought that this is possible – to perform such an experiment with single atoms and single photons,” said Ketterle. His group went further by removing every classical component except the light and the scatterers.

Atoms stand in for slits

The researchers cooled more than 10,000 rubidium atoms to about 1 microkelvin – just above absolute zero – so that the atoms barely moved.

Laser beams arranged them into a crystal-like grid, with each site roughly 0.00004 inches apart. This spacing allowed any two neighboring atoms to act as the tiniest conceivable double slit.

A faint laser sent photons in, one by one; each photon scattered off the two adjacent atoms before reaching a camera that recorded interference fringes.

Because every atom was identical, the team could repeat the trial millions of times and build up crisp statistics without the noise that plagued earlier setups that used moving slits.

The heart of the design was controllable “fuzziness.” By loosening the trapping laser for a selected pair of atoms, the physicists enlarged each atom’s quantum position spread.

This increased the chance that an incoming photon would leave a telltale recoil – or which-way information.

When the atoms were sharply localized, the camera recorded bright, evenly spaced stripes – hallmarks of wave interference. Making the atoms fuzzier dissolved those stripes into a speckled blob, revealing particle-like hits instead.

Chasing the limits of certainty

Half-wave, half-particle operation came when the lattice depth was tuned so that only about fifty percent of the photons left detectable recoil.

That mix matched the trade-off predicted by complementarity, linking interference visibility to path knowledge.

To be sure the lattice itself was not acting like Einstein’s spring, the team briefly shut off the trapping light after each shot. This left the atoms freely floating for a millionth of a second before they fell under gravity.

Even without the “spring,” probing the path still erased the stripes, proving that it is the entanglement between photon and atom, not any macroscopic support, that decides the outcome.

“In many descriptions, the springs play a major role,” said Fedoseev, the study’s first author. “But we show, no, the springs do not matter here; what matters is only the fuzziness of the atoms.”

The finding dovetails with a recent analysis that simulated a tunable recoiling-slit scenario and reached an identical verdict. The measuring device can be virtual as long as it steals enough momentum information.

Einstein’s spring meets modern lasers

Einstein imagined a real mechanical balance that would move by about one ten-millionth of an inch, a heroic engineering task for 1927.

Today’s optical lattices create forces a thousand times smaller yet still track them, thanks to single-photon detectors cooled to near 0 °F.

Because the MIT arrangement uses atoms that are “Heisenberg-uncertainty limited,” every recoil event instantly entangles the photon with the atomic state.

As a result, the scattered light carries a fringe pattern only when the atom remains unperturbed. This mirrors Richard Feynman’s famous remark that the double-slit “contains the only mystery” of quantum mechanics.

The team’s control also let them test intermediate fuzziness values and verify that interference visibility falls off in strict proportion to path knowledge.

That linear relation is a long-sought benchmark for quantum resource theories that treat information as a conserved quantity.

The experiment closes a conceptual gap left by molecular and neutron versions, which always relied on extended slits or diffraction gratings. Here, the “slit” is a single particle, so nothing classical can be blamed for the trade-off.

Fuzziness still matters

Light-based computers, precision sensors, and secure communication channels all hinge on balancing wave-like coherence against particle-like detection signals.

Engineers use precise knowledge of how entanglement reduces interference to decide how much information they can extract before a quantum state decoheres.

The MIT results arrive in the United Nations-declared International Year of Quantum Science and Technology (IYQ), a timely reminder that foundational questions still guide applied research. 

Future work will try the same protocol with molecules and superconducting qubits to test whether the visibility-information law is truly universal.

If it holds, textbooks may soon replace drawings of slits in screens with sketches of floating atoms. This would give students a more faithful picture of how nature hides her clues.

The study is published in Physical Review Letters.

—–

Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. 

Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

—–



Source link

Follow on Google News Follow on Flipboard
Share. Facebook Twitter Pinterest LinkedIn Tumblr Email Copy Link
Previous ArticleNew vision model from Cohere runs on two GPUs, beats top-tier VLMs on visual tasks
Next Article Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server
Advanced AI Editor
  • Website

Related Posts

MIT Just Proved Einstein Wrong in the Most Famous Quantum Experiment

August 1, 2025

MIT Develops Device to Address Type 1 Diabetes Complication

August 1, 2025

MIT Just Proved Einstein Wrong in the Famous Double-Slit Quantum Experiment

July 31, 2025

Comments are closed.

Latest Posts

Artist Tyrrell Winston Sues New Orleans Pelicans Over Instagram Posts

Blum Staffers Speak On Closure, Spiegler Slams Art ‘Financialization’

Theatre Director and Artist Dies at 83

France to Accelerate Return of Looted Artworks—and More Art News

Latest Posts

Perplexity AI Predicts XRP, Shiba Inu, Pepe Prices by 2025

August 2, 2025

Paper page – Efficient Machine Unlearning via Influence Approximation

August 2, 2025

Endless Announces Stability AI Integration to Accelerate Decentralized AI

August 2, 2025

Subscribe to News

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

Recent Posts

  • Perplexity AI Predicts XRP, Shiba Inu, Pepe Prices by 2025
  • Paper page – Efficient Machine Unlearning via Influence Approximation
  • Endless Announces Stability AI Integration to Accelerate Decentralized AI
  • Grok Imagine is coming: How to get AI video generator for X; who can get it
  • Anthropic Revokes OpenAI’s Access to Claude

Recent Comments

  1. vahoyhor on 1-800-CHAT-GPT—12 Days of OpenAI: Day 10
  2. Mag-sign up upang makakuha ng 100 USDT on MIT and Harvard Medical School announce a new research pathway to fight Alzheimer’s disease
  3. Binance推荐代码 on Stability AI and Arm Release Lightweight Tex-to-Audio Model Optimised for Fast On-Device Generation
  4. NekofenKed on 1-800-CHAT-GPT—12 Days of OpenAI: Day 10
  5. KexefHoalt on 1-800-CHAT-GPT—12 Days of OpenAI: Day 10

Welcome to Advanced AI News—your ultimate destination for the latest advancements, insights, and breakthroughs in artificial intelligence.

At Advanced AI News, we are passionate about keeping you informed on the cutting edge of AI technology, from groundbreaking research to emerging startups, expert insights, and real-world applications. Our mission is to deliver high-quality, up-to-date, and insightful content that empowers AI enthusiasts, professionals, and businesses to stay ahead in this fast-evolving field.

Subscribe to Updates

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

LinkedIn Instagram YouTube Threads X (Twitter)
  • Home
  • About Us
  • Advertise With Us
  • Contact Us
  • DMCA
  • Privacy Policy
  • Terms & Conditions
© 2025 advancedainews. Designed by advancedainews.

Type above and press Enter to search. Press Esc to cancel.