Scientists have successfully translated an entire viral genome into a format a quantum computer can process and analyze — the first time this has been accomplished at full-genome scale.

The work, which involved encoding the genetic sequence of a virus as quantum data, was published and reported by researchers who say the approach could eventually allow quantum systems to identify viral mutations, model genomic interactions, and assist in drug discovery at speeds classical computers cannot match.

What the researchers did

The team converted the nucleotide sequence of a complete viral genome — the ordered string of adenine, thymine, guanine, and cytosine base pairs that encodes all viral instructions — into qubits, the basic unit of quantum information. Unlike classical bits, which hold a value of either 0 or 1, qubits exploit superposition to represent both states simultaneously, allowing a quantum processor to evaluate many possible sequence configurations at once.

Translating a genome for quantum analysis requires mapping each nucleotide or group of nucleotides to a corresponding quantum state. The process is non-trivial: genomic data is inherently sequential and high-dimensional, while current quantum hardware operates with limited qubit counts and high error rates.

Getting a complete viral genome — even a relatively compact one — to fit within those constraints required data compression and encoding strategies tailored to the quantum architecture.

The viral genome selected for this experiment is small relative to bacterial or human genomes, but the encoding itself represents a meaningful proof of concept for how biological sequence data can interface with quantum processing systems.

Why quantum hardware is being applied to genomics

Classical genomic analysis tools — including BLAST for sequence alignment and machine learning models trained on genomic databases — are computationally intensive. Comparing a novel pathogen’s genome against a reference library, or predicting how mutations affect protein folding, demands significant processing time and energy when done at scale.

Quantum computing offers a theoretical speed advantage for certain classes of problems, including optimization and pattern matching across large combinatorial spaces.

Genomic sequence analysis, which often involves searching for the best alignment among millions of possibilities, fits that profile. Quantum technology is also being explored for improvements in semiconductor physics, with researchers finding that quantum phenomena in solid-state materials may open new device architectures.

For genomics specifically, the potential application is not just speed. Quantum algorithms such as the Harrow-Hassidim-Lloyd (HHL) algorithm are designed to solve linear systems exponentially faster than classical counterparts, which could transform how researchers model gene expression networks or viral replication dynamics.

Hardware constraints and open questions

Current quantum computers operate in the noisy intermediate-scale quantum (NISQ) era — devices with dozens to a few hundred physical qubits that are prone to decoherence errors. Encoding an entire genome does not mean a NISQ device can analyze it with the same fidelity as a fault-tolerant quantum computer would. The present achievement is closer to a compatibility demonstration than a functional genomic analysis pipeline.

Error correction remains a central obstacle. Physical qubits must be grouped into logical qubits with redundancy to suppress noise, which multiplies the hardware requirement substantially.

For reference, a truly fault-tolerant quantum computer capable of running complex genomic algorithms at scale could require millions of physical qubits — far beyond today’s hardware. The research is framed as an early but concrete step toward quantum-enabled biology, not an operational system.

There is also the question of data input bandwidth. Loading large biological datasets into a quantum system — a process called quantum random access memory (QRAM) — remains an active area of research with no widely agreed-upon hardware implementation.

Path to practical application

Translating a viral genome for quantum processing establishes that the conceptual pipeline — from biological sequence to quantum representation — is viable at the genome scale. The next engineering challenge is running meaningful quantum algorithms on that data and benchmarking their output against classical tools.

Pandemic preparedness is one area where faster genomic analysis would have concrete value. During the COVID-19 pandemic, tracking variant emergence required continuous re-sequencing and comparison of viral genomes across global databases.

A quantum-accelerated version of that workflow could, in principle, compress the timeline from sequence detection to variant characterization. Whether current or near-term quantum hardware can deliver that in practice remains an open question that this research alone cannot answer.

Researchers operating a deep-sea submersible have found the world’s largest known whale graveyard on the ocean floor, uncovering dozens of whale carcasses and a thriving ecosystem of creatures — many believed to be new to science — feeding off the remains.

What the submersible found

The discovery was made during a submarine dive that brought cameras and sensors to depths where light does not penetrate. Researchers observed multiple whale carcasses scattered across the seafloor in close proximity, forming what scientists are now calling the largest concentration of whale fall sites ever recorded in a single location.

Whale falls — the term for a whale carcass that sinks to the seafloor after death — are known to sustain entire biological communities for decades. Soft tissue attracts mobile scavengers first, followed by collagen-consuming organisms, and finally chemosynthetic bacteria that break down lipids embedded in the bones. Each stage can support a distinct faunal assemblage, and the full succession can span 50 to 100 years on a single carcass.

At this site, researchers reported seeing animals that they believe had not been previously documented by science. The density of carcasses in one location is particularly unusual; most documented whale falls are isolated, stumbled upon by chance during unrelated deep-sea surveys.

The biology of a whale fall

A whale carcass functions as a nutrient island in an otherwise food-scarce environment. In the deep ocean, organic material from the surface is sparse by the time it drifts to abyssal depths.

A single large whale can deliver tens of thousands of pounds of organic carbon to the seafloor in a single event — a concentrated pulse of energy that would otherwise take decades to accumulate through marine snow, the slow drift of organic particles from surface waters.

Chemosynthetic bacteria, analogous in ecological function to those found at hydrothermal vents, colonize whale bones during the final decomposition stage. These bacteria oxidize hydrogen sulfide produced by the anaerobic breakdown of bone lipids, generating energy without sunlight.

This process can sustain specialized invertebrates — including polychaete worms, clams, and gastropods — that are adapted exclusively to chemosynthetic environments.

Finding multiple whale falls in proximity means the overlapping succession stages may allow species to migrate between carcasses, effectively turning the graveyard into a connected habitat rather than a series of isolated biological islands. Whether that dynamic is occurring at this newly discovered site remains a question researchers will need to address through further sampling.

Limits of what is currently known

The full taxonomic scope of what lives at the site is not yet established. Identifying potentially new species from submersible footage alone is preliminary; formal species descriptions require physical specimens, morphological analysis, and genetic sequencing.

The claims about novel organisms, while scientifically plausible given the inaccessibility of the deep sea, remain unconfirmed until peer-reviewed taxonomy is completed.

The precise location and depth of the graveyard have not been publicly disclosed in detail, which limits independent verification at this stage. Deep-sea expeditions of this kind are expensive and logistically complex, meaning follow-up dives to collect physical samples may take months or years to organize.

The research was first published in the journal Nature.

In a landmark achievement for robotics, a bicycle-riding robot has performed the world’s first unassisted front flip, astonishing engineers and robotics enthusiasts alike. The feat, accomplished by a team at the University of Tokyo’s JSK Robotics Laboratory, marks a significant leap in the capabilities of autonomous machines and could have far-reaching implications for robotics research, mobility, and future transportation.

Engineering a robotic stunt rider

The robot, dubbed “Rider-Bot,” is built to mimic the movements and balance strategies of human cyclists. Unlike previous robotic stunts that required ramps, wires, or external supports, Rider-Bot executed the front flip entirely on its own—using only its onboard sensors, actuators, and algorithms.

The successful flip took place on a flat surface, with the robot accelerating, launching, rotating 360 degrees forward, and landing upright—all without human intervention.

According to the research team, achieving this level of acrobatic control required a combination of advanced gyroscopes, real-time feedback loops, and machine learning techniques. “This is a major step toward robots that can adapt dynamically to complex environments and perform tasks that were previously considered impossible for machines,” said Dr. Yuta Sato, lead researcher on the project.

Implications for robotics and beyond

The successful front flip demonstrates not only the mechanical resilience of Rider-Bot but also the sophistication of its AI-driven decision-making. The robot’s control system anticipates and compensates for shifts in momentum and balance, much like a skilled human cyclist would.

Researchers believe this breakthrough could inform the design of next-generation delivery robots, autonomous vehicles, and even disaster-response machines capable of navigating unpredictable terrain. “Our work shows that robots can be taught to handle extreme maneuvers, which opens up many possibilities for robust, adaptable mobility in real-world scenarios,” Dr. Sato noted.

Challenges and next steps

Despite the successful demonstration, the team acknowledges ongoing challenges. Perfecting the landing and ensuring consistency across different environments remain top priorities. The researchers plan to enhance the robot’s learning algorithms and test it in outdoor and obstacle-rich settings.

The team also hopes their achievement will inspire further research into dynamic locomotion and the integration of robotics into everyday life. “We’re just beginning to scratch the surface of what intelligent, mobile robots can do,” said Dr. Sato.

A milestone for human-robot collaboration

The Rider-Bot’s unassisted front flip not only pushes the boundaries of robotics but also highlights the growing intersection between human athleticism and machine intelligence. As robots become more capable of complex movement, their potential roles in society—from urban mobility to emergency response—are poised to expand dramatically.

With this record-breaking feat, the University of Tokyo’s robotics team has set a new benchmark for autonomous machines, paving the way for future innovations in robotic agility and intelligence.

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France is advancing plans to develop its own military artificial intelligence (AI) infrastructure, codenamed Arcadia, aiming to reduce its reliance on American defense technology firms—chiefly Palantir Technologies.

Supported by the French Ministry of Armed Forces, the Arcadia initiative forms part of a broader strategy in Paris to safeguard operational autonomy over military data systems. The urgency for such sovereignty has heightened as NATO allies increasingly adopt US-developed AI platforms for critical command-and-control operations.

Why France is building its own military AI platform

The French government’s concerns are both strategic and structural. Palantir’s Gotham and Artificial Intelligence Platform (AIP), which consolidate battlefield data for predictive analysis and decision support, are now central to US military operations and are being actively promoted to European partners.

French defense officials have raised serious concerns about the risks associated with channeling classified operational data through foreign-owned systems, even those operated by close allies. Issues around data sovereignty—specifically, control over access, auditing, and the potential for denial of service—have taken center stage in France’s defense procurement debates.

The French initiative appears to draw a sharper line by opting to develop indigenous capability rather than negotiate data-handling clauses with a foreign vendor.

Technical architecture: What a domestic platform requires

Developing a viable alternative to Palantir’s military solutions is a complex, long-term undertaking. Palantir’s Gotham platform, for instance, integrates structured and unstructured data from a variety of sources—signals intelligence, geospatial inputs, logistics, and human intelligence—into a unified system that allows analysts to query and identify patterns across multiple domains simultaneously.

To replicate this, France needs more than a data fusion engine; the project demands sophisticated ontology management, granular role-based access control, and modular pipelines capable of real-time data ingestion from diverse sensors.

France’s defense procurement agency, the Direction Générale de l’Armement (DGA), is familiar with commissioning sophisticated defense systems. However, developing military-grade AI at this scale requires continuous software iteration—something traditional defense contractors may not be fully equipped to handle.

The French defense technology sector, featuring giants like Thales and Airbus Defence alongside a burgeoning group of dual-use AI startups, is expected to play a central role. Whether the platform will leverage open-source tools or rely on proprietary frameworks remains to be determined.

Interoperability risks with NATO allies

A major technical hurdle is interoperability. If US, UK, and other NATO allies are using Palantir-based platforms during joint missions, a French-developed system built on distinct data ontologies and APIs could complicate integration.

Effective intelligence sharing, collaborative operational planning, and coordinated targeting all rely on seamless data exchange. A sovereign French platform that cannot interface smoothly with allied infrastructure risks introducing new vulnerabilities, even as it addresses concerns about foreign control.

France’s experience with the development of its national military AI strategy suggests the DGA is aware of this tension, but resolving it will require either building compatible APIs from the start or negotiating data-exchange standards with NATO’s C3 architecture bodies — a process that can take years.

Procurement timeline and industrial capacity

No firm delivery timeline has been confirmed publicly, and that ambiguity itself carries operational risk. Military AI platforms require continuous retraining on current threat data, red-team adversarial testing, and integration with live classified networks — all of which take time even after initial software delivery.

Rushing a system to fielding without adequate validation can create the kind of brittle AI behavior that defense analysts warn can lead to catastrophic misclassification in high-tempo scenarios.

The French effort, as reported by Military AI, represents a calculated bet that sovereignty over defense AI infrastructure is worth the cost and delay of domestic development.

Whether Arcadia can match the operational maturity of a system that Palantir has refined through years of real-world deployment in US and allied theaters remains the central unanswered question.

A process originally developed to decarbonize steel production may also function as a large-scale electrical energy storage system, according to research into molten oxide electrolysis (MOE) and iron-based flow battery chemistry.

The convergence of these two approaches has drawn attention from engineers looking for ways to store renewable electricity without relying on lithium or other scarce materials.

How molten oxide electrolysis works in iron production

Molten oxide electrolysis involves dissolving iron ore — primarily iron oxide, or Fe2O3 — in a molten oxide electrolyte at temperatures exceeding 1,500°C (2,732°F). An electrical current is passed through the melt, reducing the iron oxide at the cathode to produce liquid metallic iron, while oxygen is released at the anode.

The process produces no direct carbon dioxide emissions, unlike the conventional blast furnace route that relies on coke as a reductant. Boston Metal has been among the companies advancing this approach at pilot scale, and the company’s progress on emissions-free steel illustrates how capital-intensive the transition away from fossil-fuel-based metallurgy will be.

Iron-air and iron flow batteries as a parallel technology

Separately, iron-based flow batteries and iron-air batteries have been gaining traction as candidates for long-duration energy storage. Iron-air cells cycle between oxidation states of iron — essentially rusting and de-rusting the electrode material — to store and release electricity.

Form Energy has publicly discussed iron-air battery systems targeting 100-hour storage duration at costs intended to compete with natural gas peaker plants. Iron flow batteries, meanwhile, use dissolved iron salts in an aqueous electrolyte circulated through electrochemical cells, with the energy stored in the oxidation state difference between iron(II) and iron(III) ions.

The dual-use concept: steelmaking reactor as energy storage

The more technically ambitious idea explored in recent research is whether a single electrochemical system could perform both functions — reducing iron ore while storing energy during periods of excess renewable generation, and releasing that energy on demand. In this model, the electrolysis cell charges when electricity is cheap and abundant, reducing iron oxide to iron metal.

When electricity is needed, the process reverses: the iron is re-oxidized, generating current. The iron ore feedstock effectively becomes the storage medium.

This approach draws on the well-understood electrochemistry of the Fe2+/Fe3+ redox couple, but scaling it to industrial steelmaking temperatures and throughput rates introduces significant materials challenges.

Electrode and refractory materials must survive prolonged exposure to both highly oxidizing and highly reducing conditions at extreme temperatures, and the round-trip energy efficiency of a reversible high-temperature system remains an open engineering question. Thermal losses alone at 1,500°C (2,732°F) represent a substantial efficiency penalty compared to room-temperature flow batteries.

Grid integration and practical constraints

From a grid-storage perspective, coupling an industrial process to electricity dispatch introduces scheduling conflicts. A steel plant optimized for continuous production cannot freely pause or reverse its electrochemical reactors to follow an electricity price signal without affecting throughput and product quality.

Partial decoupling — using dedicated MOE cells purely for storage, separate from production cells — would recover flexibility but at increased capital cost and facility footprint.

Iron as a storage material does offer one practical advantage over lithium-ion systems: it is abundant, geographically well-distributed, and carries no significant supply chain concentration risk.

The electrochemical reactions are also well-characterized, which lowers the barrier for modeling system behavior over thousands of charge-discharge cycles.

The path from laboratory electrochemistry to a commercially viable dual-use plant requires resolving electrode degradation rates, thermal management at scale, and the economic trade-offs between storage revenue and steel output.

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