Scientists have, for the first time, experimentally proven that angular momentum is conserved even when a single photon splits into two, pushing quantum physics to its most fundamental limits. Using ultra-precise equipment, the team captured this elusive process—comparable to finding a needle in a haystack—confirming a cornerstone law of nature at the photon level.
Terra Quantum has published a groundbreaking advance in quantum error correction that redefines how we scale quantum computing. In the peer-reviewed paper “QMM-Enhanced Error Correction: Demonstrating Reversible Imprinting and Retrieval for Robust Quantum Computation”, Terra Quantum scientists present QMM-Enhanced Error Correction, a hardware-validated, measurement-free method for suppressing quantum errors, based on principles derived initially from quantum gravity.
At the heart of this innovation is the Quantum Memory Matrix (QMM), a cosmology-inspired concept that models space-time as a lattice of finite-dimensional memory cells. Terra Quantum has now translated this deep theoretical idea into a functional quantum circuit. Validated on IBM’s superconducting processors, the QMM layer functions as a lightweight, unitary “booster” that enhances fidelity without mid-circuit measurements or added two-qubit gates, offering a powerful alternative to traditional surface codes.
“We have taken a concept rooted in quantum gravity and made it plug-and-play for today’s quantum processors,” said Florian Neukart, Chief Product Officer at Terra Quantum. “QMM-enhanced error correction works out of the box on existing hardware, requires no architectural changes, and delivers measurable gains. For industries building quantum solutions now, not in 10 years. This is a game-changer.
Terra Quantum has introduced QMM-Enhanced Error Correction, a hardware-validated, measurement-free method that suppresses quantum errors.
Researchers at the Johns Hopkins Applied Physics Laboratory (APL) in Laurel, Maryland, have demonstrated that a quantum algorithm can be used to speed up an information analysis task that classical computers struggle to perform.
The innovation tackles a key element of information operations: tracking and attributing topics and narratives as they emerge and evolve online, which can help analysts spot indications of potential terrorist acts, for example. This involves using computers to perform what’s known as semantic text similarity analysis, or comparing the similarities within a textual dataset — not just the similarity of the words, but the meaning behind them, which makes it possible to identify related texts even if they don’t share any common keywords.
“The amount of open-source text data online — on social media platforms especially — is growing dramatically, and our ability to analyze all of that data has not kept pace with our ability to collect it,” said Roxy Holden, a mathematician at APL and principal investigator of this effort. “Intelligence analysts have limited resources, so finding better ways to automate this kind of analysis is critical for the military and the intelligence community.”
APL researchers have demonstrated that a quantum algorithm can be used to speed up an information analysis task that classical computers struggle to perform.
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According to a growing body of data, dark energy is not constant. Instead, physicists claim, it’s slowly getting weaker. This has understandably shaken our understanding of the universe – this week, physicists show that the new data is compatible with string theory, and that the universe will eventually collapse. Let’s take a look.
Paper: https://arxiv.org/abs/2506.
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Dr. Richard Lieu, a physics professor at The University of Alabama in Huntsville (UAH), a part of The University of Alabama System, has published a paper in the journal Classical and Quantum Gravity that proposes a universe built on steps of multiple singularities rather than the Big Bang alone to account for the expansion of the cosmos. The new model forgoes the need for either dark matter or dark energy as explanations for the universe’s acceleration and how structures like galaxies are generated.
The researcher’s work builds on an earlier model hypothesizing gravity can exist without mass that has garnered 41,000 reads and numerous citations since its publication in 2024.
Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications.
In a Chemical Science article, Oak Ridge National Laboratory researchers demonstrated the ability to rapidly model salts in liquid and solid state with quantum chemical accuracy. Specifically, they looked at thermodynamic properties, which control how molten salts function in high-temperature applications. These applications include dissolving nuclear fuels and improving reliability of long-term reactor operations. The AI-enabled approach was made possible by ORNL’s supercomputer Summit.
“The exciting part is the simplicity of the approach,” said ORNL’s Luke Gibson. “In fewer steps than existing approaches, machine learning gets us to higher accuracy at a faster rate.”
Quantum technologies have had a meteoric rise and become a key area of prioritization for governments, academics, and businesses. Government funding commitments total almost $40 billion, while private investments since 2021 total nearly $8 billion. The US agency, National Institute of Standards and Technology, released this year three new post-quantum security standards, which governments classify as ‘critical resources’ for the economy and national defense. Meanwhile, users of quantum technologies experiment with them, from industry applications in drug development and materials science to energy grid optimization and logistics efficiency.
Yet, besides a few areas, such as quantum sensing, practical and impactful quantum technologies haven’t matured for widespread use. However, when combined with classical machine learning, practical use cases emerge.
This article delves into the impact and potential of artificial intelligence and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in quantum technologies and AI, as a potential collaborator for startups to deliver investment, resources, global networks, and tailored accelerator and incubator programs.
This article covers AI and quantum technologies with QAI Ventures, a financial partner and ecosystem builder in emerging technologies.