标签：#quantum

Like its U.S. counterpart, the European Chips Act aims to foster the semiconductor industry — in part thanks to state subsidies. One of the beneficiaries is QuantumDiamonds, a German startup that applies a novel approach to inspecting chips.

France is accelerating its transition to post-quantum encryption: France’s cybersecurity agency ANSSI said on Tuesday it would stop certifying security products that lack quantum-resistant encryption, a move that will force government bodies and critical operators to shift away from older systems. Samih Souissi, ANSSI’s chief of staff, said at the France Quantum conference that the agency would halt such certifications from 2027, and that businesses should be buying only quantum-safe products by 2030. ANSSI approval is required for use in French government agencies and critical infrastructure, making the policy a de facto phase-out of older encryption...

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[Excerpted from THE QUANTUM COLLAPSE CHRONICLES — not science fiction, but a grounded forecast of what may come when quantum computation dismantles the cryptographic foundations of our digital civilization. These articles explore the collapse of computational trust and the brutal reconstruction of the world that follows.] The history of human civilization is often defined by sudden, violent shifts in the nature of power. We speak of the fall of empires, the industrial revolutions, and the splitting of the atom. But in the mid-2030s, the world experienced a collapse that was not made of steel or stone, but of mathematics. It was a quiet, clinical, and utterly devastating unraveling of the digital fabric that held modern society together. To understand The Quantum Collapse , one must look past the headlines of the era and into the humming, sub-Kelvin depths of the dilution refrigerators that changed everything. This is the story of how the transition from probabilistic experimentation to deterministic computation rendered the world's secrets transparent and its economies obsolete. The Death of Noise: The Rise of Dr. Aris Thorne For the first three decades of the 21st century, quantum computing was a game of chance. Scientists operated in the era of Noisy Intermediate-Scale Quantum (NISQ) devices—machines so temperamental and prone to error that every calculation was a desperate struggle against environmental noise. In those days, a single stray photon or a microscopic fluctuation in temperature could collapse a delicate superposition, turning a groundbreaking calculation into useless digital static. The turning point arrived in 2036 at the Institute for Advanced Quantum Engineering (IAQE) in the High Sierras. The air in the facility didn't vibrate with the erratic drone of the late 2020s; instead, it carried a heavy, rhythmic thrum—the sonic signature of the Lattice-Array-9 (LA-9). At the center of this revolution was Dr. Aris Thorne, the lead architect of the LA-9 pr

Quantum Space says SPACs aren't dead as it seeks a $1.2 billion deal to build military spacecraft.

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Qisquiz: A Qiskit v2.X Certification Prep App I built Qisquiz , a web app for learning Qiskit v2.X and preparing for the IBM Certified Quantum Computation using Qiskit v2.X Developer - Associate certification exam. You can try the app here: https://qisquiz.vercel.app/ The GitHub repository is here: https://github.com/dorakingx/qisquiz The concept of Qisquiz is simple: Master Qiskit, one quiz at a time. In other words, Qisquiz is a quiz-based certification prep app that helps learners study Qiskit one question at a time. The target exam is: Exam C1000-179: Fundamentals of Quantum Computing Using Qiskit v2.X Developer Why I Built Qisquiz Qiskit is one of the most important development tools for learning and building quantum computing applications. It is useful for creating quantum circuits, running simulations, using IBM Quantum hardware, and experimenting with quantum algorithms. However, Qiskit v2.X includes several APIs and concepts that learners need to understand carefully. For example, certification prep requires knowledge of topics such as: Qiskit Runtime SamplerV2 EstimatorV2 PUBs, or Primitive Unified Blocs BackendV2 backend.target Transpilation ISA circuits Dynamic circuits OpenQASM 3 Result object handling Little-endian and big-endian interpretation These topics can be learned by reading documentation, but I felt that active practice through quizzes is especially useful for exam preparation. That is why I built Qisquiz , a quiz-based learning app focused on Qiskit v2.X. What Is Qisquiz? Qisquiz is an independent quiz-based learning app for Qiskit v2.X. The current version is organized around the 8 sections of the IBM Qiskit v2.X Developer certification exam. The current question bank includes: 120 original questions 44 code-based questions 40 hard questions 8 sections 15 questions per section Qisquiz is not an official IBM or Qiskit product. It is an independent learning tool that I built to help myself and other learners prepare more effectively. Covered E

Quantinuum, a quantum computing startup, is losing millions. Investors want in anyway.

Meta-Optimized Continual Adaptation for coastal climate resilience planning with zero-trust governance guarantees It started with a nagging feeling of inadequacy. I was deep into a research project on adaptive AI for infrastructure planning, studying how reinforcement learning agents could optimize sea-wall placements and evacuation routes. The models worked—beautifully, in fact—on static datasets. But the moment I fed them real-time satellite imagery of a rapidly eroding coastline or a sudden storm surge, they stumbled. They forgot previous strategies, overfit to the new event, or, worse, made decisions that violated basic safety constraints. I realized then that the problem wasn't just about better AI; it was about trust and adaptation in the face of chaos. My exploration of this challenge led me down a rabbit hole of meta-learning, continual learning, and cryptographic governance. What emerged was a framework I now call Meta-Optimized Continual Adaptation (MOCA) with zero-trust governance guarantees—a system designed not just to learn, but to learn how to learn in dynamic, high-stakes coastal environments, all while ensuring that every decision is auditable and tamper-proof. This article shares that journey, the technical breakthroughs, and the hard-won lessons from my experiments. Technical Background: The Three Pillars of MOCA The core insight behind MOCA is that coastal climate resilience planning requires three seemingly contradictory properties: Continual adaptation – The system must update its models as new data streams in (e.g., sea-level rise, storm frequency, erosion patterns) without catastrophic forgetting. Meta-optimization – It must learn the learning algorithm itself, so that adaptation becomes faster and more sample-efficient over time. Zero-trust governance – Every model update and decision must be cryptographically verifiable, with no single point of failure or authority. In my research, I found that existing approaches tackled these individually

Probabilistic Graph Neural Inference for deep-sea exploration habitat design for extreme data sparsity scenarios Introduction: The Abyssal Classroom It was 3 AM, and I was staring at a screen filled with bathymetric data from the Mariana Trench—or rather, the absence of it. The dataset I had painstakingly compiled from oceanographic surveys, autonomous underwater vehicle (AUV) logs, and satellite altimetry had 97% missing values. My initial approach—a standard deep learning model for habitat design—failed catastrophically, producing predictions that were physically impossible (like habitats floating 200 meters above the seafloor). That night, as I watched the loss curve plateau into nonsense, I realized something profound: deep-sea exploration habitat design isn't just an engineering challenge; it's an inference problem under extreme uncertainty. My learning journey into probabilistic graph neural inference began that night. While exploring how to model the sparse, irregularly sampled data from hydrothermal vent fields, I discovered that traditional neural networks treat observations as independent, ignoring the inherent relational structure of the deep-sea environment. Through studying geometric deep learning and Bayesian inference, I realized that graph neural networks (GNNs) could capture the complex dependencies between seafloor features—but only if we could handle the missing data probabilistically. This article documents what I learned from building a probabilistic graph neural inference system for deep-sea habitat design, where data sparsity isn't a bug but a feature. Technical Background: Why Graph Neural Networks for the Abyss? Deep-sea habitats—from hydrothermal vent chimneys to cold seep mounds—are not randomly distributed. They form interconnected networks governed by geological processes, fluid dynamics, and biological colonization patterns. In my research, I found that this relational structure is perfectly suited for graph neural networks. However, th