Key Trends in Near-Term Quantum Hardware

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.

Google Unveils Willow: A Leap Forward in Quantum Computing Google Quantum AI has introduced Willow, a cutting-edge quantum chip designed to address two of the field’s most significant challenges: error correction and computational scalability. Willow, fabricated in Google’s Santa Barbara facility, achieves state-of-the-art performance, marking a pivotal step toward realizing a large-scale, commercially viable quantum computer. It gets way geekier from here – but if you’re with me so far… Exponential Error Reduction Julian Kelly, Director of Quantum Hardware at Google, emphasized Willow’s ability to exponentially reduce errors as the system scales. Utilizing a grid of superconducting qubits, Willow demonstrated a historic breakthrough in quantum error correction. By expanding arrays from 3×3 to 5×5 and then 7×7 qubits, researchers cut error rates in half with each iteration. This achievement, referred to as being “below threshold,” signifies that larger quantum systems can now exhibit fewer errors, a challenge pursued since Peter Shor introduced quantum error correction in 1995. The chip also achieved “beyond breakeven” performance, where arrays of qubits outperformed the lifetimes of individual qubits, which is key to ensuring the feasibility of practical quantum computations. Ten Septillion Years in Five Minutes Willow’s computational capabilities were validated using the Random Circuit Sampling (RCS) benchmark, a rigorous test of quantum supremacy. According to Google’s estimates, Willow completed a task in under five minutes that would take a modern supercomputer ten septillion years—a timescale exceeding the age of the universe. This achievement underscores the rapid, double-exponential performance improvements of quantum systems over classical alternatives. While the RCS benchmark lacks direct commercial applications, it remains a critical indicator of quantum computational power. Kelly noted that surpassing classical systems on this benchmark solidifies confidence in the broader potential of quantum technology. Building Toward Practical Applications Google’s roadmap aims to bridge the gap between theoretical quantum advantage and real-world utility. The team is now focused on achieving “useful, beyond-classical” computations that solve practical problems. Applications in drug discovery, battery design, and AI optimization are among the potential breakthroughs quantum computing could unlock. Willow’s advancements in quantum error correction and computational scalability highlight its transformative potential. As Kelly explained, “Quantum algorithms have fundamental scaling laws on their side,” making quantum computing indispensable for tasks beyond the reach of classical systems. Quantum computing is still years away, but this is an exciting milestone. Considering the remarkable rate of technological improvement we’re experiencing right now, practical quantum computing (and quantum AI) may be closer than we think. -s

PsiQuantum Achieves Breakthrough in Mass-Producing Light-Powered Quantum Chips American quantum computing startup PsiQuantum has announced a major breakthrough in manufacturing scalable photonic quantum chips, marking a significant step toward making practical quantum computing a reality. The company, which emerged from stealth mode in 2021, has been working on a light-powered (photonic) quantum computing approach, which was previously considered impractical due to hardware limitations. Why Photonic Quantum Computing? • Photonic quantum computers encode data in individual particles of light (photons), rather than in superconducting circuits like many other quantum systems. • This approach has key advantages: • Low noise compared to superconducting qubits. • High-speed operation due to the natural speed of light. • Seamless integration with fiber-optic networks, which could make quantum internet feasible. • However, the challenge has always been scaling up, as photons are difficult to control, detect, and stabilize in large-scale computations. PsiQuantum’s Breakthrough • In a paper published in Nature, the company unveiled a manufacturing process that enables large-scale production of photonic quantum chips. • The new hardware design solves key engineering problems, making it possible to reliably manipulate and measure photons at scale. • Unlike previous photonic quantum systems, which struggled with extreme hardware demands, PsiQuantum’s solution reduces errors and improves stability in complex computations. Implications for the Future of Quantum Computing • Scalability Achieved – This breakthrough could allow for mass production of quantum chips, removing a key bottleneck in commercial quantum computing development. • Quantum Networking Potential – With natural fiber-optic compatibility, photonic quantum computers could lead to highly secure quantum communications networks. • New Industrial Applications – The technology may soon be applied to optimization problems, cryptography, and materials science, revolutionizing industries that require complex simulations. The Bigger Picture PsiQuantum’s ability to mass-produce photonic quantum chips puts light-powered quantum computing in direct competition with other approaches, such as superconducting and trapped-ion quantum systems. If successful, it could make quantum computing more accessible, scalable, and commercially viable—a leap forward in the race to achieve practical quantum supremacy.

The quantum landscape is shifting faster than most people realize. In the last 72 hours alone, we’ve seen three signals that define where the next decade is heading: 1. Industrial quantum manufacturing is no longer theoretical. Companies capable of building repeatable, export‑ready quantum systems at scale are separating from the pack. The shift from prototype culture to manufacturing culture is now the real competitive frontier. 2. Frontier materials science just broke a thermal barrier. University of Southern California ’s new 1300°F (700°C) memristor demonstrates that computation can survive and compute in environments where silicon dies instantly. That unlocks AI and quantum‑adjacent systems for aerospace, geothermal, fusion, and defense applications previously considered impossible. 3. Quantum materials are beginning to harvest energy from the environment. The nonlinear Hall effect (NLHE) work from QUT/NTU shows that imperfections and lattice vibrations can be engineered to convert ambient AC signals directly into DC power. Imagine sensors, chips, and edge devices operating without batteries powered by the quantum behavior of the material itself.These aren’t isolated breakthroughs. They’re converging.Quantum is becoming an industrial ecosystem spanning manufacturing, materials, energy, and computation.And the organizations that understand how these pieces fit together will define the next era of infrastructure.For teams navigating this transition from national programs to enterprise R&D I help map these signals into strategy: manufacturing readiness, substrate alignment, deployment pathways, and cross‑ecosystem positioning.The next decade belongs to the builders who can see the whole board.🖤🔥 #QuantumComputing #QuantumHardware #DeepTech #QuantumMaterials #IndustrialQuantum #AIInfrastructure #NextGenElectronics #QuantumEcosystem

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

On March 2026, Google Quantum AI shared that it is expanding into neutral atom quantum computing. For over a decade, Google’s hardware work has focused on superconducting qubits. That remains their core platform. What is changing is this. They are now adding neutral atoms as a new research direction, not as a replacement, but as a complementary path. This matters. Superconducting systems are fast and support deep circuits. But they become harder to scale physically. Neutral atoms offer a different advantage. They can form large arrays with flexible connectivity, which may help with scaling qubit counts over time. Google is not saying it will run two production systems in parallel. But it is clear they want to explore multiple paths to solve the same long-term problem. The effort is being led by Adam Kaufman and is connected to the Boulder atomic physics ecosystem. My take: Relying on a single hardware approach is a risk at this stage of the industry. Exploring multiple architectures is not a pivot. It is a hedge. It reflects where quantum computing really is today. Still in the research phase, still searching for the most scalable path forward.

IBM’s latest roadmap update marks a pivotal moment in quantum computing, not just for its technical milestones but for the strategic clarity it brings. With the introduction of two new processors, IBM Quantum Nighthawk and IBM Quantum Loon, the company is drawing a deliberate line between near-term utility and long-term ambition. Nighthawk is engineered to deliver quantum advantage on real-world problems by 2026, while Loon is built to support scalable, error-corrected quantum systems by 2029. This isn’t just about more qubits or faster gates; it’s about architectural intent and a maturing vision for what practical quantum computing can look like. What stands out is IBM’s decision to pursue both timelines in parallel, rather than forcing a single path to do it all. That duality, solving useful problems now while laying the groundwork for fault tolerance later, feels like a turning point in how the field defines progress. The roadmap also hints at deeper integration with HPC and more efficient error mitigation, suggesting a future where quantum systems are not isolated marvels but deeply embedded in broader computational workflows. Worth watching closely. #QuantumComputing #IBMQuantum #Nighthawk #Loon #TechStrategy #FutureOfComputing #DeepTech

2026 is quantum's deployment year—and the infrastructure layer is wide open. IBM just confirmed what we've been positioning for: quantum advantage hits next year. Not benchmarks. Not demos. Real problems that classical computing can't solve. Here's what changed: The hardware is crossing the utility threshold. IBM's Kookaburra system (1,386 qubits, 5,000+ gate operations) isn't a science project anymore. It's enterprise-ready infrastructure. That's the difference between "interesting" and "investable." Sovereign capital is flooding in. The UK alone committed £1.67B through 2030—front-loaded. When governments move from grants to infrastructure budgets, the risk profile shifts. This is the semiconductor playbook, circa 1987. The winner won't be the best qubit—it'll be the best integration layer. The real value capture happens in middleware: error correction, hybrid classical-quantum orchestration, and vertical-specific tooling. That's where our portfolio is concentrated. The gap between "believers" and "deployers" is closing fast. The companies building quantum-ready workflows now—in pharma simulation, financial modeling, materials discovery—will own their categories. Everyone else will rent. LPs positioning today have 18 months of alpha. By the time quantum advantage is proven in production, institutional capital will reprice every adjacent market. We're seeing it already in our pipeline—deal flow quality is up 3x since Q3. The question for allocators: are you funding quantum research, or are you capturing the infrastructure layer before it gets crowded? We're deploying into the latter. DM if you want the full thesis and access

This week, D-Wave disrupted the quantum ecosystem with its announcement to acquire Quantum Circuits, Inc.., a move that sharply accelerates its #gatebased quantum computing roadmap and raises the bar for competitors. More importantly, this acquisition underscores how quantum developmental roadmaps are evolving. Advancing toward scalable, #faulttolerant systems increasingly requires system-level integration across qubits, control electronics, packaging, and error correction. As these challenges grow more complex, vendors that rely solely on incremental, in-house development risk falling behind peers that are willing, and able, to integrate complementary technologies through acquisition. In this context, D-Wave’s acquisition highlights a broader trend IDC expects to intensify in 2026: strategic consolidation focused on closing architectural gaps, not simply expanding portfolios. While hardware acquisitions will remain central, particularly around qubit architectures, cryogenic control, and error correction, software will be an equally important focus. Quantum software spanning compilers, control stacks, hybrid orchestration, and application development will be critical for translating hardware advances into usable, enterprise-relevant systems. Vendors that treat software as an afterthought risk creating powerful hardware platforms that are difficult to program, integrate, or operationalize. In this environment, acquisitions will not be about scale for scale’s sake. Success will hinge on how well new capabilities align with existing architectures, roadmaps, and go-to-market strategies. Vendors that remain closed to acquisition-driven growth may find themselves constrained by internal development timelines, while more agile competitors redefine expectations and reset market leadership. In my latest IDC Link, I examine why D-Wave’s announcement matters, what it signals for the future of quantum development, and how enterprises should evaluate vendor claims in a market where disruption can emerge quickly and roadmaps matter more than ever. Alan Baratz Trevor Lanting Michelle Maggs Murray Thom Kelley Noblet Robert Sutor Ashish Nadkarni Dave Pearson Peter Rutten Mike Houston Brian Lenahan Rick Villars Matt Eastwood https://lnkd.in/eZbNingG

⚛️ Quantum Computing – Strategic Recommendations for the Industry 📜 This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior. ℹ️ Erdman et al - 2026