Scientists have built a compact physical qubit with built-in error correction, and now say it could be scaled into a 1,000-qubit machine that is small enough to fit inside a data center. They plan to release this machine in 2031.
Category: supercomputing
New hybrid quantum–classical computing approach used to study chemical systems
Caltech professor of chemistry Sandeep Sharma and colleagues from IBM and the RIKEN Center for Computational Science in Japan are giving us a glimpse of the future of computing. The team has used quantum computing in combination with classical distributed computing to attack a notably challenging problem in quantum chemistry: determining the electronic energy levels of a relatively complex molecule.
The work demonstrates the promise of such a quantum–classical hybrid approach for advancing not only quantum chemistry, but also fields such as materials science, nanotechnology, and drug discovery, where insight into the electronic fingerprint of materials can reveal how they will behave.
“We have shown that you can take classical algorithms that run on high-performance classical computers and combine them with quantum algorithms that run on quantum computers to get useful chemical results,” says Sharma, a new member of the Caltech faculty whose work focuses on developing algorithms to study quantum chemical systems. “We call this quantum-centric supercomputing.”
It’s elementary: Problem-solving AI approach tackles inverse problems used in nuclear physics and beyond
Solving life’s great mysteries often requires detective work, using observed outcomes to determine their cause. For instance, nuclear physicists at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility analyze the aftermath of particle interactions to understand the structure of the atomic nucleus.
This type of subatomic sleuthing is known as the inverse problem. It is the opposite of a forward problem, where causes are used to calculate the effects. Inverse problems arise in many descriptions of physical phenomena, and often their solution is limited by the experimental data available.
That’s why scientists at Jefferson Lab and DOE’s Argonne National Laboratory, as part of the QuantOm Collaboration, have led the development of an artificial intelligence (AI) technique that can reliably solve these types of puzzles on supercomputers at large scales.
Post-Alcubierre Warp-Drives
Researchers are actively exploring and revising the concept of Alcubierre warp drive, as well as alternative approaches, to potentially make superluminal travel feasible with reduced energy requirements and advanced technologies ## ## Questions to inspire discussion.
Practical Warp Drive Concepts.
🚀 Q: What is the Alcubierre warp drive? A: The Alcubierre warp drive (1994) is a superluminal travel concept within general relativity, using a warp bubble that contracts space in front and expands behind the spacecraft.
🌌 Q: How does Jose Natario’s warp drive differ from Alcubierre’s? A: Natario’s warp drive (2001) describes the warp bubble as a soliton and vector field, making it harder to visualize but potentially more mathematically robust.
🔬 Q: What is unique about Chris Van Den Broeck’s warp drive? A: Van Den Broeck’s warp drive (1999) uses a nested warp field, creating a larger interior than exterior, similar to a TARDIS, while remaining a physical solution within general relativity. Energy Requirements and Solutions.
💡 Q: How do Eric Lent’s hyperfast positive energy warp drives work? A: Lent’s warp drives (2020) are solitons capable of superluminal travel using purely positive energy densities, reopening discussions on conventional physics-based superluminal mechanisms.
Advanced algorithm to study catalysts on material surfaces could lead to better batteries
A new algorithm opens the door for using artificial intelligence and machine learning to study the interactions that happen on the surface of materials.
Scientists and engineers study the atomic interactions that happen on the surface of materials to develop more energy efficient batteries, capacitors, and other devices. But accurately simulating these fundamental interactions requires immense computing power to fully capture the geometrical and chemical intricacies involved, and current methods are just scratching the surface.
“Currently it’s prohibitive and there’s no supercomputer in the world that can do an analysis like that,” says Siddharth Deshpande, an assistant professor in the University of Rochester’s Department of Chemical Engineering. “We need clever ways to manage that large data set, use intuition to understand the most important interactions on the surface, and apply data-driven methods to reduce the sample space.”
How AI & Supercomputing Are Reshaping Aerospace & Finance w/ Allan Grosvenor (MSBAI)
Excellent Podcast interview Allan Grosvenor!…” How Allan built MSBAI to make super computing more accessible.
How AI-driven simulation is speeding up aircraft & spacecraft design.
Why AI is now making an impact in finance & algorithmic trading.
The next evolution of AI-powered decision-making & autonomous systems”
What if AI could power everything from rocket simulations to Wall Street trading? Allan Grosvenor, aerospace engineer and founder of MSBAI, has spent years developing AI-driven supercomputing solutions for space, aviation, defense, and even finance. In this episode, Brent Muller dives deep with Allan on how AI is revolutionizing engineering, the role of supercomputers in aerospace, and why automation is the key to unlocking faster innovation.
“China’s Quantum Leap Unveiled”: New Quantum Processor Operates 1 Quadrillion Times Faster Than Top Supercomputers, Rivalling Google’s Willow Chip
IN A NUTSHELL 🚀 Chinese scientists have developed the Zuchongzhi 3.0 quantum processor, which is significantly faster than the world’s top supercomputers. 🔍 The processor features 105 superconducting qubits and demonstrates unprecedented speed, completing tasks in seconds that would take traditional supercomputers billions of years. 💡 With enhanced coherence time, gate fidelity, and error correction.
How physicists used antimatter, supercomputers and giant magnets to solve a 20-year-old mystery
Physicists are always searching for new theories to improve our understanding of the universe and resolve big unanswered questions.
But there’s a problem. How do you search for undiscovered forces or particles when you don’t know what they look like?
Take dark matter. We see signs of this mysterious cosmic phenomenon throughout the universe, but what could it possibly be made of? Whatever it is, we’re going to need new physics to understand what’s going on.
New quantum battery design promises nanoscale energy storage
In the coming years, batteries so tiny yet powerful could revolutionize everything from smartphones to supercomputers.
Energy storage is about to take a massive leap forward, with the new concept of “topological quantum battery” at the forefront.
A theoretical study by researchers at the RIKEN Center for Quantum Computing and Huazhong University of Science and Technology has shown how to efficiently design a quantum battery.
Star quakes and monster shock waves: Researchers simulate a black hole consuming a neutron star
Across the cosmos, many stars can be found in pairs, gracefully circling one another. Yet one of the most dramatic pairings occurs between two orbiting black holes, formed after their massive progenitor stars exploded in supernova blasts. If these black holes lie close enough together, they will ultimately collide and form an even more massive black hole.
Sometimes a black hole is orbited by a neutron star—the dense corpse of a star also formed from a supernova explosion but which contains less mass than a black hole. When these two bodies finally merge, the black hole will typically swallow the neutron star whole.
To better understand the extreme physics underlying such a grisly demise, researchers at Caltech are using supercomputers to simulate black hole–neutron star collisions. In one study appearing in The Astrophysical Journal Letters, the team, led by Elias Most, a Caltech assistant professor of theoretical astrophysics, developed the most detailed simulation yet of the violent quakes that rupture a neutron star’s surface roughly a second before the black hole consumes it.