Republican Governor rips Trump for'MURDER' in Minneapolis as GOP erupts at ICE scandal Seven dead in private jet crash as audio reveals voice said'Let there be light' seconds before tragedy at snowy Maine airport Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Inside the secret double life of a beloved neurosurgeon whose gay love triangle ended... in an execution at his $2.5M mansion Queer Eye snitch reveals exactly what was said about Karamo Brown in a hot mic moment... that's torn the cast apart Kate Hudson's Oscar nomination torched as an'abomination' amid toxic family feud over Song Sung Blue Mystery of Egypt's Giza pyramids deepens as hidden megastructure 4,000 feet below is revealed America's best and worst states to retire revealed - and why Florida is no longer the obvious winner Prince Harry and Meghan Markle's Sundance screening sparks online row: 'Sussex Squad' brand claims event failed to sell out as'lies' despite photos showing'rows of empty seats' Kristi Noem's VERY unfortunate post shortly before Trump sent Tom Homan to Minneapolis to clean up mess after she lied about protester shot dead by her DHS officers NFL's'scripted' conspiracy theory resurfaces as fans find five-month old post hinting at Super Bowl 60 matchup Forensic video analysis of Alex Pretti's final 30 seconds exposes'John Wayne gun' question that can't be ignored Victoria and David Beckham make first public appearance together since son Brooklyn's damning statement as children Cruz, Romeo and Harper turn up to support her as she becomes a Knight of the Order of Arts and Letters Kristi Noem is dealt hammer blow live on Fox News as Trump lawyer trashes claim Minneapolis victim Alex Pretti was'domestic terrorist' Lauren Sanchez turns heads in a red skirt suit as she holds hands with billionaire husband Jeff Bezos at Schiaparelli's Paris Haute Couture Fashion Week show Scientists predict the date when quantum computing will crack all of Earth's digital encryption - with terrifying consequences READ MORE: Why'Q-DAY' could upend the world as we know it As terrifying as it might sound, experts believe the world will soon face a technological crisis that threatens to fundamentally overthrow digital secrecy. Known as'Q-Day', this is the moment when quantum computers will crack open all of Earth's digital encryption. From then, any information not secured by'post-quantum' protection will be laid bare - including financial transactions and military communications . So, when will this world-shattering moment arrive?

Greenland'will stay Greenland', former Trump adviser declares Donald Trump will not be able to force Greenland to change ownership, a former top adviser to the US president has told the BBC. IBM's vice chairman Gary Cohn, who advised Trump on the economy in his first term, said Greenland will stay Greenland and linked the need for access to critical minerals to his former boss's plans for the territory. Cohn is one of America's top tech bosses, a leader in the race to develop AI and quantum computing, and served under Trump as director of the White House National Economic Council. In a sign of how seriously business leaders are taking the crisis, he warned invading an independent country that is part of Nato would be over the edge. He also suggested the president's recent comments about Greenland may be part of a negotiation.

If your New Year's resolution is to understand quantum computing this year, take a cue from a 9-year-old podcaster talking to some of the biggest minds in the field, says quantum columnist Karmela Padavic-Callaghan Quantum computing seems to pop up in the news pretty often these days. You've probably seen quantum chips gracing your feeds and their odd, steampunk-ish cooling systems in the pages of magazines and newspapers. Politicians and business leaders are peppering their announcements with the word "quantum" more frequently, too. If you're feeling a little confused about it all, it's a good year for a New Year's resolution to finally figure out what quantum computing is all about. This is an ambitious goal, and the timing certainly makes sense.

Do quantum computers offer a way to vastly improve agriculture? As quantum computers continue to advance, identifying problems they can solve faster than the world's best conventional computers is becoming increasingly important - but it turns out that a key task held up as a future goal by quantum proponents may not need a quantum computer at all. The task in question involves a molecule called FeMoco, which plays a vital role in making life on Earth possible. That is because it is part of the process of nitrogen fixation, in which microbes convert atmospheric nitrogen into ammonia, making it biologically accessible to most other living organisms. How exactly FeMoco works during this process is complicated and not fully understood, but if we could crack it and replicate it on an industrial scale, it could drastically cut the energy involved in producing fertilisers, potentially leading to a boost in crop yields.

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum version of a neural network. Given, for example, a chemically useful molecule, how can you predict what properties it might have in an hour or tomorrow? To make such predictions, researchers start by measuring its current properties. But for quantum objects, including some molecules, this can be unexpectedly difficult because each measurement can interfere with or change the outcome of the next measurement.

Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a steep quantum cost for the loading of classical data and poor trainability of many quantum machine learning algorithms designed for near-term quantum hardware. In this work, we show that one can overcome these obstacles by using a linear Hamiltonian-based machine learning method which provides a compact quantum representation of classical data via ground state problems for k-local Hamiltonians. We use the recent sample-based Krylov quantum diagonalization method to compute low-energy states of the data Hamiltonians, whose parameters are trained to express classical datasets through local gradients. We demonstrate the efficacy and scalability of the methods by performing experiments on benchmark datasets using up to 50 qubits of an IBM Heron quantum processor.

A strange kind of quantumness may be key to quantum computers' success What is it about quantum computers that makes them more powerful than conventional machines? A new experiment shows that the property of "quantum contextuality" may be a key ingredient. Quantum computers are fundamentally different from all other computers because they harness uniquely quantum phenomena absent from conventional electronics. For instance, their building blocks, which are called qubits, are routinely put into superposition states - they seemingly assume two properties at once that are normally mutually exclusive - or they get connected through the inextricable link of quantum entanglement . Quantum computers have finally arrived, but will they ever be useful? Now, researchers at Google Quantum AI have used their Willow quantum computer to carry out several demonstrations showing that the property of quantum contextuality also plays a significant role.

Could 2026 be the year we start using quantum computers for chemistry? Whether quantum computers can actually solve practical problems is one of the biggest unanswered questions of this growing industry - and one that might be answered by researchers in industrial and medical chemistry in 2026. Calculating the structure, reactivity and other chemical properties of a molecule is an intrinsically quantum problem because it involves its electrons, which are quantum particles. But the more complex a molecule is, the harder these calculations become, in some cases posing a real challenge even for traditional supercomputers. On the other hand, because quantum computers are also intrinsically quantum, they should have an advantage when it comes to tackling these chemical calculations.

Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability--i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our scalable architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $\sim50\%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks, and scales to devices with 100+ qubits.