The biggest bottleneck in quantum computing is not the qubits themselves. It is everything required to keep them alive — the cryogenic cooling systems, the drive wires, the room-sized energy infrastructure that makes scaling past a few thousand qubits an engineering nightmare.

A team led by CSIRO, the University of Queensland, and the Okinawa Institute of Science and Technology (OIST) just published a theoretical framework that attacks this problem directly. Their paper, “Powering Quantum Computation with Quantum Batteries,” appeared in Physical Review X on January 29, 2026.

Quantum Batteries Recycle Energy Instead of Wasting It

The core idea: embed quantum batteries inside the quantum computer itself. Unlike classical power sources that dump energy into the system from the outside, quantum batteries maintain quantum coherence with the qubits they power. Energy gets recycled rather than dissipated as heat.

Dr. James Quach, CSIRO’s quantum batteries research lead, explained that the computers use significantly less energy because internal quantum batteries recycle energy within the system. The team’s modeling shows this approach achieves near-zero energy dissipation for certain computations.

The practical result: fewer wires, less heat, and four times more qubits packed into the same physical space.

The Math Behind the Multiplier

Lead author Yaniv Kurman, a CERC postdoctoral fellow at CSIRO, and co-authors Kieran Hymas, Arkady Fedorov, and William J. Munro demonstrated that initializing a bosonic quantum battery in a Fock state can supply the energy for arbitrary unitary gates regardless of circuit depth. Allowing quantum battery-qubit entanglement during computation lowers the initial energy requirements below previously established energy-fidelity bounds.

Translation: the batteries do not just power the machine — they make the computations themselves more efficient.

Speed Gets a Boost Too

The modeling revealed an unexpected benefit. The architecture enables quantum superextensivity — a phenomenon where adding more qubits actually makes each qubit operate faster. More scale means more speed. That flips the conventional scaling problem on its head.

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. The quantum computing race is fundamentally an infrastructure race disguised as a science race. The companies and nations that solve the energy and cooling bottleneck first will control the hardware layer that everything else — cryptography, AI training, drug discovery — depends on. CSIRO publishing this openly in Physical Review X rather than locking it behind corporate R&D is a deliberate strategic move. Watch who licenses it.

What This Means for Everyday People

Quantum computers will not show up in your living room. But they will reshape the systems you depend on — from how your financial assets are secured to how new medicines get developed. The energy problem is the gate. If quantum batteries work in practice the way they work in theory, that gate opens wider and faster than current timelines project. Every government and major tech company tracking quantum supremacy is watching this paper closely.

The work is theoretical. Experimental validation is next. But the team says the approach is feasible within existing quantum hardware platforms — which means this is not a decade-out concept. It is an engineering challenge with a clear path forward.

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Quantum computing has quietly stepped up again. IBM followed through on promises made back in June, debuting two new quantum processors that mark a leap toward error-corrected systems. Meanwhile, IonQ and Quantum Art made their own announcements that signal how quickly quantum innovation is moving from the lab into something that looks more like the next computing industry.

IBM’s new Quantum Loon and Nighthawk chips

IBM confirmed the large-scale manufacturing of its Quantum Loon processor, the company’s new architecture for logical qubits. Loon represents a major shift in IBM’s hardware strategy, moving to a square grid layout that connects each qubit to four neighbors instead of the older hex pattern. This setup allows for faster operations and a smoother path toward error-corrected quantum computing.

The second chip, Nighthawk, uses the same grid design but without Loon’s long-distance connections. It’s focused on improving error rates so researchers can begin testing algorithms for quantum advantage, cases where quantum systems outperform classical ones.

IBM also launched a public GitHub repository that lets researchers share performance data across classical and quantum algorithms. It’s part of a broader effort to track where quantum hardware actually provides a measurable edge.

IonQ’s record-breaking error rate

While IBM was refining architecture, IonQ made headlines with a new record-low error rate for two-qubit gates achieving over 99.99 percent fidelity. That number may sound small, but in quantum computing it’s enormous. Each percentage point gained means fewer hardware qubits are needed to stabilize a system, which brings practical quantum hardware closer to reality.

IonQ’s method builds on technology acquired from Oxford Ionics, which used electromagnetic fields instead of lasers to handle qubit operations. The breakthrough reduces the time required for cooling ions between operations, allowing the entire machine to run faster while maintaining stability.

Quantum Art and Nvidia team up

The final announcement came from Quantum Art, which revealed a partnership with Nvidia to create a more efficient compiler for its trapped-ion systems. While Nvidia isn’t directly building quantum chips, it’s using GPUs to model, optimize, and support the computations quantum hardware needs.

Quantum Art’s design is unusual instead of performing operations on one or two qubits at a time, its system groups large clusters of ions into what it calls quantum cores. Each core operates on many qubits at once, using laser “pins” to isolate and move groups efficiently. The result could be multicore quantum computing, a concept that mirrors classical chip architecture.

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This move also reflects a growing trend, traditional computing giants like Nvidia are embedding themselves deeper into the quantum ecosystem, preparing for a hybrid future where quantum and GPU systems coexist.

The next stage of the race

Taken together, these updates show that 2025 is shaping up to be a year of practical momentum for quantum hardware. IBM is building the foundation for scalable, error-corrected machines. IonQ is proving the physics can handle real-world precision. And Quantum Art is testing entirely new models for how quantum cores might work in the future.

If these companies keep their current pace, the next few years might finally deliver what the field has promised for decades: quantum computing that actually does something classical machines cannot.

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For more context on the evolving quantum race:

Salt Unlocks Metallic Nanotubes for Quantum and High Speed Tech

Will quantum be bigger than AI?

– John Timmer Senior Science Editor – 

Quantum technology has always been hard to explain. It deals with particles so small and strange that even experts struggle to describe what is happening. Yet behind the complexity lies a truth that is starting to surface. Quantum might soon become as transformative as artificial intelligence and possibly even bigger.

The field that once lived only in theory is now moving into hardware. Companies like Microsoft, Google, and Quantinuum are racing to build quantum computers, sensors, and communication systems. Microsoft’s latest Majorana chip is one example, designed to make quantum systems more stable and less error-prone.

For years, AI dominated the headlines. It was easy to understand, easy to use, and quick to deploy. Quantum, on the other hand, required labs, lasers, and near absolute-zero temperatures. But things are changing fast.

Researchers are finding ways to build qubits, the core building blocks of quantum computers with synthetic diamonds, allowing them to work at higher temperatures and with greater efficiency. Companies like Element Six, a subsidiary of De Beers, are now creating industrial-grade quantum diamonds in partnership with Amazon Web Services, paving the way for more accessible hardware.

The potential scale is enormous. Analysts at McKinsey project the quantum sector could reach 97 billion dollars by 2035. That is still smaller than AI’s trillion-dollar forecast, but the real measure is not in money, it is in impact.

Professor Sir Peter Knight of Imperial College London describes it simply: “Problems that would take the age of the universe to solve could one day be computed in seconds.”

That kind of power could change everything from drug discovery to energy systems. In healthcare, quantum computers could map endless combinations of molecules to design precise, personalized medicines. Google’s new Willow chip already demonstrated how a quantum processor could solve a problem in five minutes that would take the world’s fastest supercomputer ten septillion years.

The same capability could transform global industries. Airbus is testing quantum algorithms to load cargo more efficiently, saving thousands of kilos of fuel. The UK National Grid is investing in quantum models to optimize how power flows across thousands of generators. Even navigation could change, researchers at Imperial College London recently tested a quantum compass that works underground where GPS fails.

These breakthroughs are not limited to science. They also touch national security. Experts warn that quantum systems will eventually break today’s encryption standards, unlocking everything from government secrets to personal data. This looming moment is known as Q-day, when a fully operational quantum computer becomes powerful enough to decrypt traditional systems.

Governments and tech firms are already preparing. Apple and Signal have rolled out post-quantum encryption keys designed to withstand these future attacks. But older encrypted data remains vulnerable. Intelligence agencies are already harvesting and storing data they cannot yet read, waiting for the day quantum decryption becomes possible.

Professor Alan Woodward of the University of Surrey calls it “harvest now, decrypt later.” Once Q-day arrives, everything encrypted by older systems could become transparent overnight.

Still, many experts believe quantum’s benefits will outweigh its risks. Quantum sensors already enable more precise brain scans, helping doctors study movement disorders and childhood epilepsy without keeping patients still. In transportation, quantum navigation could keep airplanes and subways connected even when satellite signals drop.

As Rajeeb Hazra, CEO of Quantinuum, told the BBC, “We as consumers will touch the impacts of quantum computing in almost every walk of our lives. It could be as big as AI — if not bigger.”

The question is no longer whether quantum works. It is how soon we will be ready for it. The technology that once lived in cold labs is warming up fast. And when it arrives, the world may have to rethink not only how we compute, but how we secure, measure, and even understand information itself.

Read the original report by Zoe Kleinman at BBC.

For more Laterstack analysis, explore Quantinuum Helios quantum computer could bring quantum breakthroughs closer to real life and NVIDIA just built the bridge between quantum and classical computing.