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.

AI teachers and cybernetics - what could the world look like in 2050? The last 25 years has seen some mind-bending technological changes. At the start of the century, most computers connected to the internet with noisy dial-up connections, Netflix was an online DVD rental company, and the vast majority of people hadn't even heard of a smartphone. Fast forward two and a half decades, and innovations in AI, robotics and much else besides are emerging at an incredible rate. So we decided to ask experts what the next 25 years could bring.

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.

TorchQuantumDistributed (tqd) is a PyTorch-based [Paszke et al., 2019] library for accelerator-agnostic differentiable quantum state vector simulation at scale. This enables studying the behavior of learnable parameterized near-term and fault- tolerant quantum circuits with high qubit counts.

A vital ingredient for making quantum computers truly useful just might be conventional computers. That was the message from a gathering of researchers this month, which explained that classical computers are vital for controlling quantum computers, decoding the results of their calculations and even developing new techniques for manufacturing quantum computers in future. Quantum computers are made from qubits - quantum objects that may come in the form of extremely cold atoms or tiny superconducting circuits. The more qubits that a quantum computer has, the more computationally powerful it gets. But qubits are fragile, so they must be carefully calibrated, monitored and controlled.

Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the classical Machine Learning (ML) approaches are effective, they often struggle to meet the stringent constraints of robustness, timing, explainability, and safety in the MC domains. Quantum Artificial Intelligence (QAI), the fusion of machine learning and quantum computing (QC), can provide transformative solutions to the challenges faced by classical ML models. In this paper, we provide a comprehensive exploration of QAI for MC systems. We begin with a conceptual background to quantum computing, MC systems, and quantum machine learning (QAI). We then examine the core mechanisms and algorithmic principles of QAI in MC systems, including quantum-enhanced learning pipelines, quantum uncertainty quantification, and quantum explainability frameworks. Subsequently, we discuss key application areas like aerospace, defense, cybersecurity, smart grids, and disaster management, focusing on the role of QA in enhancing fault tolerance, real-time intelligence, and adaptability. We provide an exploration of the positioning of QAI for MC systems in the industry in terms of deployment. We also propose a model for management of quantum resources and scheduling of applications driven by timeliness constraints. We discuss multiple challenges, including trainability limits, data access, and loading bottlenecks, verification of quantum components, and adversarial QAI. Finally, we outline future research directions toward achieving interpretable, scalable, and hardware-feasible QAI models for MC application deployment.

This work addresses the challenge of enabling practitioners without quantum expertise to transition from classical to hybrid quantum-classical machine learning workflows. We propose a three-stage framework: starting with a classical self-training model, then introducing a minimal hybrid quantum variant, and finally applying diagnostic feedback via QMetric to refine the hybrid architecture. In experiments on the Iris dataset, the refined hybrid model improved accuracy from 0.31 in the classical approach to 0.87 in the quantum approach. These results suggest that even modest quantum components, when guided by proper diagnostics, can enhance class separation and representation capacity in hybrid learning, offering a practical pathway for classical machine learning practitioners to leverage quantum-enhanced methods.