Peter Shor's algorithm could break the internet - but he's not worried Few people have invented an algorithm with the potential to spark a worldwide crisis, so why is quantum computing pioneer Peter Shor so unconcerned? "So, he's the Beyoncé of this event?" a young woman standing behind me says to a colleague. The three of us are standing, looking at the back of a crowd, whose members are all looking at a bearded man in an orange sweater. Getting a look at him is like trying to see the - only fleeting glimpses are possible. "His algorithm is the algorithm that will break everything," the colleague says, as I briefly catch sight of people posing for selfies and getting their conference badges signed.

AI-powered attacks on Learning with Errors (LWE)--an important hard math problem in post-quantum cryptography--rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time-and compute-intensive and requires significant domain expertise. To fill this gap and accelerate AI research on LWE attacks, we propose the TAPAS datasets, a toolkit for analysis of postquantum cryptography using AI systems. These datasets cover several LWE settings and can be used off-the-shelf by AI practitioners to prototype new approaches to cracking LWE. This work documents TAPAS dataset creation, establishes attack performance baselines, and lays out directions for future work.

AI-powered attacks on Learning with Errors (LWE)--an important hard math problem in post-quantum cryptography--rival or outperform classical attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model training is time-and compute-intensive and requires significant domain expertise.

Quantum'Jamming' Could Help Unlock the Mysteries of Causality To keep communications secure in a post-quantum world, cryptographers are digging down into the concept of cause and effect. For the past few decades, researchers have understood that quantum computers should eventually be able to crack the widely used codes that secure much of the digital world. To protect against this fate, they've spent years developing new codes that appear to be safe from future safecrackers armed with quantum computers. At the same time, they've also devised ingenious ways to use the rules of quantum mechanics to keep communications secure. But quantum mechanics, just like the "classical" mechanics that preceded it, is just a theory of nature.

Quantum computers could cause a global security crisis that makes the once-feared millennium bug, or Y2K, look quaint. This infamous computer risk was averted through the persistent behind-the-scenes work of engineers across the world, but whether the new threat will be tackled similarly is an urgent yet unresolved question. Most digital communications and transactions are protected by cryptography based on mathematical problems that are unsolvable by conventional computers but are solvable by a sufficiently capable quantum computer. Researchers have understood this since the late 1990s, but the day when this capable-enough quantum computer comes online - or Q-Day - was thought to be very far in the future. Working quantum computers are now a reality, and recent leaps in how to use them are bringing Q-Day ever closer.

Charles Bennett and Gilles Brassard pioneered quantum information theory. Now they've been awarded the highest honor in computer science. Today it's widely acknowledged that the future of computing will involve the quantum realm . Companies like Google, Microsoft, IBM, and a few well-funded startups are frantically building quantum computers and routinely claiming advances that seem to bring this exotic, world-changing technology within reach. In 1979 all of this was unthinkable.

Large language models (LLMs) excel at many general-purpose natural language processing tasks. However, their ability to perform deep reasoning and mathematical analysis, particularly for complex tasks as required in cryptography, remains poorly understood, largely due to the lack of suitable data for evaluation and training. To address this gap, we present CryptoQA, the first large-scale question-answering (QA) dataset specifically designed for cryptography. CryptoQA contains over two million QA pairs drawn from curated academic sources, along with contextual metadata that can be used to test the cryptographic capabilities of LLMs and to train new LLMs on cryptographic tasks. We benchmark 15 state-of-the-art LLMs on CryptoQA, evaluating their factual accuracy, mathematical reasoning, consistency, referencing, backward reasoning, and robustness to adversarial samples. In addition to quantitative metrics, we provide expert reviews that qualitatively assess model outputs and establish a gold-standard baseline. Our results reveal significant performance deficits of LLMs, particularly on tasks that require formal reasoning and precise mathematical knowledge. This shows the urgent need for LLM assistants tailored to cryptography research and development. We demonstrate that, by using CryptoQA, LLMs can be fine-tuned to exhibit better performance on cryptographic tasks.

Political consultant Bradley Tusk has spent a fortune on mobile voting efforts. Now, he's launching a protocol to try to mainstream the technology. Joe Kiniry, a security expert specializing in elections, was attending an annual conference on voting technology in Washington, DC, when a woman approached him with an unusual offer. She said she represented a wealthy client interested in funding voting systems that would encourage bigger turnouts. Did he have any ideas?

Quantum networks rely on both quantum and classical channels for coordinated operation. Current architectures employ entanglement distribution and key exchange over quantum channels but often assume that classical communication is sufficiently secure. In practice, classical channels protected by traditional cryptography remain vulnerable to quantum adversaries, since large-scale quantum computers could break widely used public-key schemes and reduce the effective security of symmetric cryptography. This perspective presents a quantum-resistant network architecture that secures classical communication with post-quantum cryptographic techniques while supporting entanglement-based communication over quantum channels. Beyond cryptographic protection, the framework incorporates continuous monitoring of both quantum and classical layers, together with orchestration across heterogeneous infrastructures, to ensure end-to-end security. Collectively, these mechanisms provide a pathway toward scalable, robust, and secure quantum networks that remain dependable against both classical and quantum-era threats.

This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key distribution method and National Institute of Standards and Technology (NIST) approved quantum-resistant algorithms. By integrating AI-driven red teaming, automated penetration testing, and real-time anomaly detection, the research develops a framework for assessing and mitigating security risks in quantum networks. The findings demonstrate that AI can be effectively used to simulate adversarial attacks, probe weaknesses in cryptographic implementations, and refine security mechanisms through iterative feedback. The use of automated exploit simulations and protocol fuzzing provides a scalable means of identifying latent vulnerabilities, while adversarial machine learning techniques highlight novel attack surfaces within AI-enhanced cryptographic processes. This study offers a comprehensive methodology for strengthening quantum security and provides a foundation for integrating AI-driven cybersecurity practices into the evolving quantum landscape.