Recent Developments in Scalable Quantum Modeling

Last week, IBM announced its intent to acquire HRL Laboratories, LLC. This week, the cover of Nature features a significant milestone from the HRL quantum team: a digitally controlled silicon quantum processing unit that integrates exchange-only spin qubits, cryogenic CMOS control electronics, and a novel high-density superconducting interconnect into a single architecture. (https://lnkd.in/eMSj47jS) This work addresses one of the central challenges in quantum computing: how to scale quantum systems without an unmanageable increase in control hardware, wiring complexity, and power consumption. By moving quantum control into the cryostat, the team demonstrated an autonomous error correction routine using a fully integrated system rather than relying on racks of room-temperature electronics. The results include an order-of-magnitude improvement in exchange-only qubit performance, implementation of repetition-code error correction and quantum error detection, and a path toward manufacturing quantum processors and control systems using advanced semiconductor technologies. In other words, the researchers showed that instead of relying on entire rooms of electronics to manage fragile qubits, the system could autonomously perform key functions at cryogenic temperatures, paving the way for smaller, more efficient, and far more scalable quantum computers. At IBM Quantum, we recently published a blog introducing spin qubits and how they compare with superconducting qubits (https://lnkd.in/e5aSTgyc). In summary, these two different modalities are more complementary than adversarial, both leveraging state-of-the-art silicon fabrication and advanced manufacturing techniques.What makes this result particularly interesting is its focus on the systems architecture required to scale quantum computing, from qubits and cryogenic control to interconnects and error correction. Congratulations to the HRL team on having this achievement featured on the cover of Nature, a well-deserved recognition of both the scientific significance and systems-level engineering demonstrated in this work. Progress toward fault-tolerant quantum computing will require innovation across the entire stack. This paper is an excellent example of that approach and highlights why we are excited about the opportunity to bring HRL's exceptional quantum capabilities into IBM Research. Paper: https://lnkd.in/eFz5M2Wk Video: https://lnkd.in/eWCv63pc

The last two days have seen two extremely interesting breakthroughs announced in quantum computing. There is a long path ahead, but these both point to the potential for dramatically upscaling ambitions for what's possible in relatively short timeframes. The most prominent advance was Microsoft's announcement of Majorana 1, a chip powered by "topological qubits" using a new material. This enables hardware-protected qubits that are more stable and fault-tolerant. The chip currently contains 8 topologic qubits, but it is designed to house one million. This is many orders of dimension larger than current systems. DARPA has selected the system for its utility-scale quantum computing program. Microsoft believes they can create a fault-tolerant quantum computer prototype in years. The other breakthrough is extraordinary: quantum gate teleportation, linking two quantum processes using quantum teleportation. Instead of packing millions of qubits into a single machine—which is exceptionally challenging—this approach allows smaller quantum devices to be connected via optical fibers, working together as one system. Oxford University researchers proved that distributed quantum computing can perform powerful calculations more efficiently than classical systems. This could not only create a pathway to workable quantum computers, but also a quantum internet, enabling ultra-secure communication and advanced computational capabilities. It certainly seems that the pace of scientific progress is increasing. Some of the applications - such as in quantum computing - could have massive implications, including in turn accelerating science across domains.

Nu Quantum has released a paper this week which significantly accelerates the quantum computing timeline by showing a viable path to commercial quantum computing via 'scale-out' 🦾 👀 These results are a significant haircut to Jensen's 15-year prediction for *very useful* computers 👀 We explore a modular architecture of quantum processing units (QPUs) of intermediate size, networked via a photonic fabric made of qubit-photon interfaces and switches. Flexible entanglement topologies are made possible by the network, enabling the use of error correcting codes (Floquet codes) which require significantly lower physical-to-logical qubit ratios than the surface code. We demonstrate that this error-corrected distributed system is feasible to build, since it tolerates realistic network fidelities and doesn't need all-to-all connectivity. The sort of quantum network we are trailblazing at Nu Quantum. Finally, we demonstrate it's efficient - i.e. you don't need more total qubits that in a monolithic approach in order to introduce networking. This is really significant. The results are timely - with the Willow announcement and others, in 2024 the industry demonstrated for the first time that matter qubits can be high-quality enough for computing. So we now have the building blocks. The only remaining orders-of-magnitude challenge is scaling, from ~100 qubits to 10k-1Ms of qubits... -> Modular scaling via networking together near-term available QPUs shortens the time-to-impact of quantum computing and makes the timeline more predictable, since it moves the problem from an R&D one to a scalable manufacturing engineering & capital resource one (stamp-and-repeat of modules that we already know how to make). So proud of the Nu Quantum Quantum Error Correction team for this fantastic work! Link in comments 🙂

IBM and RIKEN just hit a massive milestone. They simulated a protein with 12,635 atoms. To put that in perspective? It is a 40x increase in size in just six months. And the accuracy improved by over 200x. 🚀 Here is why this theoretical and hardware leap changes everything. 1. The Hardware Tag-Team 🤝 They didn't do this with quantum alone. They used a powerful hybrid approach. Supercomputers broke the protein into computable fragments. Then, the IBM Heron quantum processor stepped in. It pushed its limits, utilizing 94 of its 156 superconducting qubits. It calculated the complex quantum mechanics of those specific fragments. Finally, the classical systems stitched the full molecular representation back together. It is the perfect marriage of classical scale and quantum precision. 2. The Algorithmic Breakthrough 🧠 Hardware is nothing without the right math. The real hero here is a novel hybrid algorithm. It is called EWF-TrimSQD. Traditional methods like VQE hit a wall when scaling past a handful of atoms. This new subspace quantum diagonalization approach bypassed that bottleneck entirely. It dramatically reduced the computational overhead. It allowed researchers to map complex chemistry directly onto quantum hardware without breaking the system. 3. Scaling the Physics 🧬 They proved the theory by steadily scaling the complexity. They started simple with a 10-atom molecule. Then they moved to a 303-atom protein. Now, they have successfully calculated the total energy of an enzyme with nearly 13,000 atoms. Modeling the quantum-mechanical behavior of a system this massive was previously unheard of. We are finally moving past the era of toy models. We are witnessing hardware and algorithms maturing hand in hand. The era of utility is officially here. ⚛️

Google and IBM believe first workable quantum computer is in sight - meanwhile Europe offers a more collaborative vision Yesterday, both Google and IBM signalled that quantum computing is entering its engineering phase: Google’s Willow chip, introduced in December 2024, demonstrated scalable error correction: as more qubits were added, error rates dropped exponentially. It completed a benchmark task in under five minutes - one that would take today’s fastest supercomputer an unimaginable 10⁻²⁵ years (i.e., 10 septillion years). IBM revealed a detailed blueprint for industrial-scale quantum, outlining a path to building a fault-tolerant quantum supercomputer by late 2029. Meanwhile, real-world applications are already emerging: IBM and Moderna have collaborated to simulate the longest mRNA sequence (60 nucleotides) ever modelled on a quantum computer, using 80 of the 156 qubits on IBM’s Heron chip. They applied a clever algorithm (CVaR-based VQA) that has made earlier attempts at 42 nucleotides seem modest. Now contrast that with Europe’s collaborative approach. Instead of centralised lab efforts, Europe is deploying nine quantum systems across at least seven countries - spanning superconducting, ion-trap, and annealing technologies - integrated with national supercomputing centers for shared access and resilience. I recently visited the Poznań Supercomputing Centre in Poland to witness one of these systems in action. Europe’s model is about collective strength, diversity, and building long-term quantum infrastructure - demonstrating that the race isn’t just about breakthroughs, but also how you organise for scale and inclusivity.

Six months ago, the IDC Worldwide Quantum Computing Forecast made a specific bet: the next phase of quantum computing wouldn't be driven by better hardware alone. It would come from combining increasingly capable quantum systems with AI, HPC, and domain expertise to solve problems beyond the practical reach of classical computing. Last week offered a compelling example of exactly that. IBM, Oak Ridge National Laboratory, and Cleveland Clinic used a hybrid quantum-classical workflow to model the chemistry of molten FLiBe salt, a leading candidate material for future fusion reactors. Rather than replacing classical computing, IBM's increasingly capable quantum hardware was applied to the portion of the problem where it provides the greatest computational advantage, while classical systems handled the remaining calculations. That's exactly the heterogeneous computing model we expect to define enterprise quantum adoption. What's equally important is where this work happened. The research is part of the U.S. Department of Energy's Genesis Mission, bringing together quantum computing, HPC, AI, and domain expertise across the national laboratory ecosystem. Read alongside recent initiatives like QuantumEAGLe, it reinforces a broader trend: government investment is evolving beyond advancing quantum hardware. It's increasingly focused on building the collaborative ecosystem needed to translate scientific breakthroughs into real-world applications. This is also why simulation continues to stand out in our enterprise research. Alongside optimization and quantum AI, simulation remains one of the leading quantum use cases organizations are exploring. Fusion materials research represents one of the most demanding examples imaginable, but the underlying challenge extends well beyond energy. Industries including pharmaceuticals, chemicals, advanced manufacturing, and materials science all face computational problems where heterogeneous computing could eventually deliver meaningful advantages. The remaining challenge isn't demonstrating that quantum can contribute to scientific discovery. It's making these capabilities accessible outside national laboratories. Today's breakthrough required quantum scientists, computational chemists, HPC researchers, and highly specialized workflows. The next phase of the market will depend on advances in both quantum hardware and the surrounding software ecosystem, development platforms, and workflow orchestration that allow domain experts to leverage quantum computing without becoming quantum specialists. I explore what this means for enterprise quantum adoption, heterogeneous computing, and the evolution of the quantum software ecosystem in our latest IDC Link: https://lnkd.in/gFQV95FF Ashish Nadkarni Jeff Janukowicz Jerry M. Chow Jay Gambetta Mike Houston Steven Malkiewicz #Quantum #QuantumComputing #HPC #AI #FusionEnergy #DOE #IDC