Quantum AI Advancements in Today's Tech Industry

For those tracking progress in Quantum… As my colleague Hartmut Neven has predicted, real-world applications possible only on quantum computers are much closer than people think – as near as five years, even though fully error corrected quantum computers may be further away.  Recently, my colleagues on our Quantum AI team at Google Research took another important step on that path with a new set of results we published last week in Nature that share a promising new approach to applications on today’s quantum computers. Our analog-digital quantum simulator using super-conducting qubits shows performance beyond the reach of classical simulations in cross-entropy benchmarking experiments. Simulations with the level of experimental fidelity in this simulator would require more than a million years on a Frontier supercomputer. The simulator brings together digital’s flexibility and control with the analog’s speed – and provides a path towards applications that cannot be accomplished on a classical computer. Along the way, my colleagues also made a scientific discovery – they observed the breakdown of a well-known prediction in non-equilibrium physics, the Kibble-Zurek mechanism - an important result in our understanding of magnetism, and also useful in various kinds of quantum simulations. Congratulations to Trond Andersen, Nikita Astrakhantsev, and the rest of the team on this exciting step – much more to come! You can read the Nature paper here: https://lnkd.in/gg2En5qe 

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

Headline: AI and Quantum Computing Unite: A New Era of Intelligent, Energy-Efficient Machines Introduction: Artificial intelligence and quantum computing—once separate frontiers of tech innovation—are now converging. Each is amplifying the other’s potential: AI is helping design smarter, more stable quantum systems, while quantum computing could soon supercharge AI, enabling breakthroughs in efficiency, security, and discovery. Key Details: 1. AI Drives Quantum Progress Machine learning is accelerating quantum research by modeling qubit behavior and reducing “noise” errors that plague quantum processors. Nvidia and Google Quantum AI demonstrated that simulations once taking a week now finish in minutes. AI tools are being used to improve circuit design and develop real-time quantum error correction—vital steps toward stable, fault-tolerant systems. 2. Quantum Power Boosts AI Quantum processors are ideal for optimization problems, making them valuable for fraud detection, drug development, and materials research. They can generate synthetic training data, helping train large AI models when real data is limited. Experts also anticipate future energy savings, as quantum-enhanced algorithms may cut the enormous electricity demand of current AI training. 3. Building Hybrid Supercomputers IBM and others are merging classical and quantum computing into shared infrastructures, enabling AI and quantum algorithms to run side by side. The challenge: quantum hardware still requires cryogenic cooling and controlled environments, slowing broad deployment. 4. Black Box and Security Risks Both technologies suffer from “black box” opacity—AI for its inscrutable algorithms, quantum for its unmeasurable quantum states. Their convergence could make future systems doubly hard to audit, complicating regulation and trust. Meanwhile, quantum decryption threats loom, with bad actors hoarding encrypted data today to unlock once quantum power matures (“harvest now, decrypt later”). Why It Matters: The fusion of AI and quantum computing could redefine how the world processes data—driving scientific discovery, advancing national security, and transforming energy efficiency. Yet this power comes with profound ethical and cybersecurity challenges. Whether collaboration or competition prevails will shape the next great computing revolution. I share daily insights with 28,000+ followers and 10,000+ professional contacts across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

The AI race isn’t just about building smarter models—it’s about redefining what’s possible. Google’s latest quantum computing breakthrough has me convinced they’re not just competing; they’re paving the road ahead. If you missed the news, Google announced their quantum computer solved a problem in minutes that would take a supercomputer thousands of years. This isn’t just a headline—it’s the foundation for the next era of AI. Having spent almost 16 years at Google, I’ve seen how they approach innovation. They don’t just iterate on the present; they build for the future. Quantum computing is their latest move, and it’s a game-changer. Quantum and AI: What’s Next in 10 Years? Here’s how I think quantum computing will transform AI over the next decade: - Exponential Model Growth Today’s AI models are already pushing the limits of traditional compute. Quantum computing will break through these barriers, allowing us to train models at scales we can’t even imagine today. Models with trillions of parameters could become the norm, driving hyper-accurate predictions and complex decision-making. - Revolutionizing Real-Time AI Quantum computing will dramatically accelerate data processing. Think real-time, high-fidelity AI for autonomous vehicles, climate modeling, and even personalized healthcare. Imagine AI systems capable of analyzing and acting on global-scale data streams in seconds. - Breaking Through Optimization Challenges AI has struggled with problems like protein folding, material science, and logistics optimization. Quantum computing’s ability to solve these challenges could lead to breakthroughs in drug discovery, sustainable energy solutions, and next-gen manufacturing processes. - AI Meets General Intelligence As quantum-enabled AI models become faster and smarter, they’ll inch closer to what we dream of as general intelligence. While still speculative, the quantum-boosted ability to simulate and synthesize massive datasets could bring us closer to AI that genuinely understands context and solves problems like humans. At Google, I saw how they consistently pushed boundaries. They don’t just dabble in moonshots—they commit, iterate, and build the ecosystems needed to sustain them (e.g Self Driving). Quantum computing is no exception. It’s not a side project; it’s part of a long-term vision to reshape computing and AI itself. The AI race isn’t just about now—it’s about what’s next. And with quantum, Google just showed us a glimpse of what’s possible. What do you think? How do you see quantum computing shaping the future of AI? #AI #QuantumComputing #Google #Innovation #FutureTech

The best part of being a Deeptech VC is how it feeds your curiosity. Arguably one of the most fundamental skills in venture capital. The bleeding edge of research is where I like meandering to anticipate possible commercialisation trends. Quantum computing is one of these areas. I've already made a couple of bets here, and the field continues to advance through both commercial milestones and research breakthroughs. Hybrid systems for chemistry computation are a particularly interesting field to immerse yourself on a Sunday 🤓☕ Take this recently published research: Korean researchers just achieved something remarkable with photonic qudits - think of them as quantum Lego blocks that can hold multiple states instead of just 0s and 1s. While Google and IBM struggle with 12-qubit systems requiring complex error corrections on superconducting architectures, this team achieved 16-dimensional calculations using a single quantum unit by using photonics. The numbers are striking: • Chemical accuracy of 0.00146 Hartree for H2 molecules • No error correction needed • 48 iterations vs traditional systems' hundreds • 5x faster convergence than previous approaches Think of it as the difference between building with individual blocks (qubits) versus using pre-fabricated sections (qudits). Why this matters for investors: 1. Resource efficiency = lower operational costs 2. Scalability without the error cascade nightmare 3. Room temperature operation = practical deployment The quantum-AI race in chemistry is finally heating up 🏎️🏁 • AI can handle 100k atoms but struggles with quantum precision • Quantum systems nail accuracy but only for smaller molecules • Hybrid approaches could bridge this gap This is where the next wave of quantum products may emerge - at the intersection of classical AI and quantum advantages. For LPs, angels and VCs looking at quantum in their portfolios: watch the hybrid plays. AI alone may be too resource intensive to advance much further in these areas. Quantum solutions are just getting started. The future isn't necessarily binary anymore - it may be hybrid. Just like these qudits. #QuantumComputing #DeepTech #VentureCapital #FutureOfComputing #AI Thoughts? 🤔

AI’s energy challenge is driving big new infrastructure ideas, including space-based data centers. It’s a bold vision. But we do not have to wait for space to improve compute efficiency. Quantum computing can help now. At D-Wave, we’re already applying annealing quantum computing to complex problems in science, industry, and AI-related workflows. In work with Japan Tobacco’s pharmaceutical division, now part of Shionogi, we demonstrated a quantum AI proof of concept for generative molecular design. And in published magnetic materials simulation work, we showed that a problem solved on our Advantage2 quantum computer in minutes would have taken a classical supercomputer nearly one million years and more than the world’s annual electricity consumption to solve. The question is not only where compute goes next. It’s how efficiently we compute today. Read my recent article for more on why quantum computing should be part of the AI energy conversation now. #AI #QuantumComputing #EnergyEfficiency #DWave

Most people still see Quantum Computing as a “someday” technology. But if you zoom out, something important becomes clear: Quantum is not competing with GenAI. It is unlocking the places GenAI cannot reach on classical hardware. Here is why this matters: 1. AI is hitting real physical limits. Frontier models require computations at a scale that would take thousands of years on a single processor. GPUs made this possible through massive parallelism, but even they are beginning to reach practical and economic ceilings. 2. Quantum changes the math itself. Superposition, entanglement and interference are not faster versions of today’s chips. They are new computational behaviors that let us explore search spaces, molecular structures and high dimensional patterns in ways classical systems cannot approximate efficiently. 3. This matters for real problems, not theoretical ones: • Drug discovery with atom level accuracy • Financial modeling across thousands of variables • Supply chain design with true combinatorial complexity • Material science and energy breakthroughs • And eventually, more efficient building blocks for next generation model training 4. Quantum does not replace AI. It expands what AI can be applied to, especially in domains that are computationally unreachable today. Classical AI is impressive but bounded. Quantum combined with AI opens new frontiers that remain closed on classical hardware. A grounded nuance: Quantum hardware is still early. Most near term progress will come from hybrid quantum classical workflows, not fully quantum systems. But understanding this shift now gives you a more realistic view of where meaningful breakthroughs may emerge. If you are serious about the future of AI, pay attention to how Quantum will shape the next wave of models, optimization methods and scientific discovery. 💾 Save this 🔁 Repost to help others see where the AI curve is heading 👉 Follow Gabriel Millien for more clarity on AI, LLM architectures and the technologies shaping the next decade CC: Bhavishya Pandit, give him a follow!

Boards face a stark choice: prepare for quantum computing now or risk being outpaced by competitors and adversaries alike. A revolution is brewing in the shadows of today’s AI frenzy, and leaders can’t afford to ignore it. Headlines celebrated OpenAI’s jaw-dropping $300B, five-year deal with Oracle, demanding 4.5 gigawatts of power...more than two Hoover Dams. Oracle shares surged 42%. Its chairman’s fortune jumped $100B overnight. But this wasn’t just a triumph of scale. It was a warning. AI’s projected $2.9T market by 2028 (Morgan Stanley) is straining classical infrastructure, ballooning energy demands, persistent compute shortages, and unsustainable economics. OpenAI alone is projected to lose $44B before profitability in 2029. Bigger data centers are no longer a strategy; they’re a liability. Enter quantum computing, the quieter, truly transformative force. Unlike classical bits (0 or 1), qubits leverage superposition and entanglement, unlocking problems once considered impossible: • Real-time global supply-chain optimization • Atomic-level drug discovery • AI training compressed from years to hours, with orders-of-magnitude less energy This isn’t hype. It’s happening now: China has made quantum a national imperative, investing billions. Origin Quantum’s Tianji 4.0 and SpinQ’s planned 100-qubit system underscore why China is a leader in quantum communication (Belfer Center). The U.S. still holds an edge in computing and sensing. Google’s 105-qubit Willow chip achieved a 10% error-rate reduction in 2025. Startups are accelerating: • Rigetti: 36-qubit multi-chip system at 99.5% fidelity • D-Wave: 42% revenue growth to $3.1M • IonQ: $20.7M Q2 revenue, guidance raised to $82–100M Microsoft, MIT, and Nvidia are actively weaving quantum into AI workflows. Europe is closing the gap through aggressive public-private programs. The upside is enormous: quantum-AI hybrids could revolutionize drug discovery, climate modeling, and critical-infrastructure security. The risks are just as real: broken encryption, amplified surveillance, and weaponized technologies in the wrong hands. It’s no accident the UN designated 2025 as the International Year of Quantum Science and Technology. Boards should take note. The Oracle–OpenAI deal proves we’re pushing classical systems to the brink. True leadership won’t come from trillion-dollar buildouts on strained power grids. It will come from bold U.S. investment in quantum R&D, education, and talent, paired with regulation that accelerates innovation rather than stifles it. Hesitation isn’t prudence. It’s surrender. Quantum won’t just speed up AI. It will redefine it—and the rules of tomorrow.

Quantum computers are still in their infancy, but in this week's column I look at the ways in which the age of quantum software is already taking shape. We have a few working examples of what quantum software can do, thanks to the ability of early-stage quantum machines and classical computers that simulate what is coming. IBM said it now generates revenue from deploying quantum systems and services to more than 250 customers. The company said it is using quantum systems with Wells Fargo to potentially improve AI technology, for example, by testing and implementing new machine learning generative models. IBM’s quantum scientists and the E.ON have developed an algorithm for managing weather risk using a quantum computer that IBM says could outperform classical methods. Illinois Gov. JB Pritzker said the Quantum and Microelectronics Park being developed in Chicago will be home to IBM’s Quantum System Two, the company’s most advanced quantum computer, which will be used to explore quantum applications across industries. IBM says it expects Quantum System Two in Chicago to be up and running in the coming year. And PsiQuantum, says it will have the world’s first large-scale, fault-tolerant, commercially useful quantum computer up and running in Australia in 2027—and a second one in operation at Chicago’s park in 2028. Terra Quantum AG is running quantum-based software on high-performance traditional computers for clients in finance, energy and life sciences, according to founder and Chief Executive Markus Pflitsch. Its work on optimizing collateral in finance improves lenders’ costs by 6 basis points compared with more conventional approaches, according to the company. IonQ typically uses simulated quantum computers during the early stages of a client engagement and actual quantum computers to validate results, according to Masako Yamada, IonQ’s director of applications development. One of the great challenges of the field, and there are many, is that fragile qubits generate errors as they tackle problems, with the error rate increasing as the platforms get larger. Qubits are the basic units of quantum computation.  Google says Willow, the new quantum chip it announced this month, switches this dynamic, reducing errors with the addition of more qubits. It will be around three years before the error rate reaches the target, according to Google, and the company doesn’t expect its quantum systems to reach full scale until the end of the decade. “I’ll say upfront, the vast majority of applications that people are excited about are going to really be unlocked with a fault-tolerant quantum computer,” said Charina Chou, chief operating officer at Google Quantum AI. “That’s not where we are right now and we expect even more software development to happen at that stage.”

Sometimes, it feels like the future is creeping up faster than we can process. Back in 2001, researchers used a 7-qubit quantum computer to factor the number 15- a symbolic, an important demo that proved physical qubits could run real algorithms. Two decades later, we’ve gone from theoretical proofs to verifiable performance. The pace of progress in quantum computing isn’t linear anymore, it’s compounding. Researchers from Google, MIT, Stanford, and Caltech just achieved what they call a verifiable quantum advantage using Google’s new Willow processor. It performed a specific physics simulation ~13,000× faster than today’s top supercomputers. Days later, Nvidia announced NVQLink- a system designed to connect quantum processors (QPUs) with AI/GPU supercomputers. Jensen Huang called it “the Rosetta Stone connecting quantum and classical supercomputers.” If Willow shows the engine works, NVQLink builds the road network it can run on. For investors and enterprises, this dual breakthrough matters because it de-risks the full compute stack. We’re entering the Hybrid Compute Era where AI and quantum don’t compete, they co-evolve. AI will stabilize qubits, interpret noisy outputs, and orchestrate workloads. Quantum will solve problems that today’s AI models can’t solve such as molecular design to next-gen cryptography. The line between them is going to blur. And when it does, the real advantage won’t lie in algorithms it’ll lie in orchestration: who controls the layer that makes both worlds talk. #QuantumComputing #AI #DeepTech #Nvidia #GoogleAI #Innovation #Supercomputing #TechInvesting #FutureOfTech #QuantumAdvantage Image: An illustration showing three NVQLinks connecting quantum processors and classical supercomputers. NVIDIA