Using Quantum Devices for Data Generation

Two competitors just told you the playbook. IonQ and Microsoft published a joint essay this week arguing that quantum computers don’t need to run every simulation themselves. They just need to generate the training data that makes AI models accurate enough to do the work on classical hardware. That’s the hybrid model. Quantum generates the truth. AI scales it. They call it “bending Jacob’s Ladder.” Instead of climbing rung by rung toward more expensive computation, you use quantum-generated data to skip rungs entirely. Microsoft already tested the concept. AI screened 32 million candidate materials for battery electrolytes. Narrowed to 800 in under a week. One of those candidates was synthesized and worked. Here’s what most people will miss about this story: The bottleneck was never computation. It was data quality. Classical simulations approximate electron behavior. Those approximations compound. By the time you’re modeling a real chemical reaction, you’re guessing. Quantum doesn’t guess. It simulates electrons the way electrons actually behave. Even a small amount of that data, fed into the right model, changes everything downstream. This is why infrastructure matters more than hardware specs. The company that builds the data generation layer, the training pipeline, the deployment architecture, wins. Not the company with the most qubits. Two competitors figured that out at the same time. That should tell you something about where this is going. 🖤🔥 #QuantumComputing #ArtificialIntelligence #MaterialsScience #Infrastructure #DeepTech #QuantumChemistry #MachineLearning #DrugDiscovery #FutureOfComputing #QuantumAdvantage

Is this the first real-world use case for quantum computers? True randomness is hard to come by. And in a world where cryptography and fairness rely on it, “close enough” just doesn’t cut it. A new paper in Nature claims to present a demonstrated, certified application of quantum computing, not in theory or simulation, but in the real world. Led by Quantinuum, JPMorganChase, Argonne National Laboratory, Oak Ridge National Laboratory, and The University of Texas at Austin, the team successfully ran a certified randomness expansion protocol on Quantinuum’s 56-qubit H2 quantum computer, and validated the results using over 1.1 exaflops of classical computing power. TL;DR is certified randomness--the kind of true, verifiable unpredictability that’s essential to cryptography and security--was generated by a quantum computer and validated by the world’s fastest supercomputers. Here’s why that matters: True randomness is anything but trivial. Classical systems can simulate randomness, but they’re still deterministic at the core. And for high-stakes environments such as finance, national security, or fairness in elections, you don’t want pseudo-anything. You want cold, hard entropy that no adversary can predict or reproduce. Quantum mechanics is probabilistic by nature. But just generating randomness with a quantum system isn’t enough; you need to certify that it’s truly random and not spoofed. That’s where this experiment comes in. Using a method called random circuit sampling, the team: ⚇ sent quantum circuits to Quantinuum’s 56-qubit H2 processor, ⚇ had it return outputs fast enough to make classical simulation infeasible, ⚇ verified the randomness mathematically using the Frontier supercomputer ⚇ while the quantum device accessed remotely, proving a future where secure, certifiable entropy doesn’t require trusting the hardware in front of you The result? Over 71,000 certifiably random bits generated in a way that proves they couldn’t have come from a classical machine. And it’s commercially viable. Certified randomness may sound niche—but it’s highly relevant to modern cryptography. This could be the start of the earliest true “quantum advantage” that actually matters in practice. And later this year, Quantinuum plans to make it a product. It’s a shift— from demos to deployment from supremacy claims to measurable utility from the theoretical to the trustworthy read more from Matt Swayne at The Quantum Insider here --> https://lnkd.in/gdkGMVRb peer-reviewed paper --> https://lnkd.in/g96FK7ip #QuantumComputing #CertifiedRandomness #Cryptography

Quest - ION Everything Scientists are turning light into multidimensional quantum shapes. Light has always been strange. But scientists are now shaping it in ways that were once pure theory — turning simple photons into powerful tools. A review outlines a rapidly growing field called quantum structured light, where researchers manipulate several properties at once: polarization, spatial patterns, and frequency. By controlling these “degrees of freedom,” they create high‑dimensional quantum states that go beyond the simple on/off bits used in traditional computing. In most quantum systems, information is stored in qubits. These are two‑state quantum objects, like a photon that can be horizontal or vertical in polarization. But structured light uses qudits — quantum states with more than two levels. One qudit can carry far more information than a qubit, and doing this with a single photon means you can send more data without needing more particles. For quantum communication, this expansion means stronger security. Each high‑dimensional photon can carry more information and resist noise and interference better than conventional light signals. That’s critical when data is encrypted or sent across networks where eavesdropping must be minimized. In quantum computing, structured light simplifies circuit designs and makes it easier to build complex quantum states needed for advanced simulations. Instead of stringing together many qubits, researchers can encode more information in fewer, richer quantum objects. Structured light is also opening new doors in imaging and measurement. Holographic quantum microscopes, for example, use these techniques to image delicate biological samples without damaging them. And quantum correlations in light waves are being used to build sensors with extraordinary sensitivity. But challenges remain. Scientists still struggle to maintain these states over long distances. But as on‑chip sources and compact control systems improve, quantum structured light is moving out of the lab and into real‑world applications. Read the study: "Progress in quantum structured light.” Nature Photonics, 2025.

Quantum computing just got real—and it might be the boost AI has been waiting for. At Google, our Quantum AI team recently hit a major milestone with the Willow chip—a quantum processor that solved a problem in under 5 minutes that would’ve taken a supercomputer 10 septillion years. (Yes, that’s a 1 followed by 25 zeros.) But the real leap wasn’t just speed—it was error correction. Willow showed it’s possible to scale quantum systems reliably, something the field has been chasing for decades. And we shared full performance benchmarks—not just hype, but hard data. So why does this matter for AI? Today’s frontier models are hitting a wall. We’re running out of clean, high-quality data to feed them. Quantum computers can simulate complex systems and generate entirely new, physically accurate synthetic datasets—things classical machines can’t do. That means new fuel for models, especially in areas like materials science, biotech, and energy. And in the future? We could see hybrid quantum-AI systems—using quantum processors for parts of the workflow that are just too complex for classical compute. It’s early. But if AI was the last big platform shift, quantum might be the next—and together, they could unlock problems we once thought impossible. #QuantumComputing #GoogleQuantumAI #WillowChip #AI #SyntheticData #FutureOfTech #DeepTech #QubitsAndBeyond #AIxQuantum https://lnkd.in/eFRVHYk5

In an essay, researchers from IonQ and Microsoft suggest that quantum computers could help generate highly accurate data that can train artificial intelligence models to simulate chemical systems more efficiently. The approach would combine the precision of quantum simulations with the speed of AI models running on classical computers, potentially accelerating materials and drug discovery. The researchers say quantum-generated training data could improve predictions of electron behavior in molecules, helping scientists design catalysts, batteries and other materials while large-scale fault-tolerant quantum computers are still under development. https://lnkd.in/e3jqPT6z

When GPT-3 first emerged, we all recognized an inflection point where AI would change the world. While large language models have boosted productivity, especially in language-based tasks, AI hasn’t truly transformed the world – yet. Today’s AI is limited by the extent of training possible. Generative Quantum AI (Gen QAI) breaks through those barriers. Harnessing quantum-generated data, which is inaccessible to classical computers, will provide training data necessary for AI models to unlock transformative value for industry.  Oscar Hornstein at UKTN put it well: “LLM’s as we know them today generally get more powerful and accurate the more data is provided to them. No matter how much information is fed into these models, they will always be limited to the categories of data that are available to them. But those categories become infinitely broader when quantum computers are brought into the mix.” Whether it's identifying a better material to replace platinum as a catalyst in automotives, designing new medicines, or realizing hydrogen fuel cells at scale, Gen QAI will open doors to insights and solutions impossible for AI trained solely on classical computing. Quantum computing’s "ChatGPT moment" won’t come from a scientific breakthrough—it will come when we see the first results of Gen QAI.  And that moment is very near. https://lnkd.in/gsyuJe5T