GeSn Use Cases in Quantum Computing

Is Quantum Machine Learning (QML) Closer Than We Think? Select areas within quantum computing are beginning to shift from long-term aspiration to practical impact. One of the most promising developments is Quantum Machine Learning, where early pilots are uncovering advantages that classical systems are unable to match. 🔷 The Quantum Advantage: Quantum computers operate on qubits, which can represent multiple states simultaneously. This enables them to process complex, interdependent variables at a scale and speed that classical machines cannot. While current hardware still faces limitations, consistent progress in simulation and optimization is confirming the technology’s potential. 🔷 Why QML Matters: QML combines quantum circuits with classical models to unlock performance improvements in targeted, data-intensive domains. Early-stage experimentation is already showing promise: • Accelerated training for complex models • More effective handling of high-dimensional and sparse datasets • Greater accuracy with smaller sample sizes 🔷 The Timeline Is Shortening: Quantum systems are inherently probabilistic, aligning well with generative AI and modeling under uncertainty. Just as classical computing advanced despite hardware imperfections, current-generation quantum systems are producing measurable results in narrow but high-value use cases. As these outcomes become more consistent, enterprise adoption will follow. 🔷 What Enterprises Can Do Today: Quantum hardware does not need to be perfect for companies to begin exploring value. Practical entry points include: • Simulating rare or complex risk scenarios in finance and operations • Using quantum inspired sampling for better forecasting and sensitivity analysis • Generating synthetic datasets in regulated or data scarce environments • Targeting challenges where classical AI struggles, such as subtle anomalies or low signal environments • Exploring use cases in fraud detection, claims forecasting, patient risk stratification, drug efficacy modeling, and portfolio optimization 🔷 Final Thought: Quantum Machine Learning is no longer confined to research. It is becoming a tool with real strategic potential. Organizations that begin investing in awareness, experimentation, and talent today will be better positioned to lead as the ecosystem matures. #QuantumMachineLearning #QuantumComputing #AI

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

“We make cars. What could quantum possibly do for us?” a representative from a major car company asked me this week. “And besides,” they added, “we already use AI — so we’re probably covered.” Fair question. And no, quantum won’t make trucks teleport (ever). But it will reshape how cars are designed, produced, powered, and maintained — often together with #AI. In fact, companies like Volkswagen Group, Mercedes-Benz AG, and Porsche AG are already exploring quantum use cases today: ⚡ Battery breakthroughs - car manufacturers are working with companies developing quantum hardware to simulate lithium-sulfur battery materials using #QuantumComputing. The idea is to improve charge capacity, energy density, and battery life for electric vehicles. ⚡ ⚡ Production optimization - another use case is to apply quantum to simulate welding and other processes, identifying potential defects before they happen on the factory floor. And this is just the beginning. Let’s unpack how quantum will act as a force multiplier for AI — especially in industrial sectors like automotive, logistics, and mobility: 🔹 Faster training of AI models Training large models for autonomous driving or fleet management takes serious compute. Quantum computing could speed up complex math operations in deep learning — shaving training time from months to days. 🔹 Smarter supply chain optimization Quantum algorithms like QAOA could help AI find faster, better solutions to complex problems like routing, scheduling, and resource allocation — critical in global automotive supply chains. 🔹 Next-gen R&D simulations AI + quantum chemistry = a leap in simulating materials, structures, and battery components, before building anything physical. That means faster, smarter innovation. 🔹 Safer autonomy through better NLP Vehicle perception systems rely on understanding nuance and context. Quantum-enhanced NLP may help AI interpret rare edge cases more accurately — a big win for autonomous driving safety. 🔹 Richer data analytics Quantum machine learning could unlock insights from massive, high-dimensional datasets — from predictive maintenance to customer behavior modeling. Bottom line? Quantum won’t replace AI. But it will unlock a new scale of possibility. We’re moving from “maybe someday” to “what can we pilot now?” And those who start early — even with hybrid quantum-classical approaches — will build real strategic advantage. Curious what you think: 👉 Where do you see quantum enhancing AI in your industry? Let’s exchange ideas, in comments below!

How Quantum Computing Will Unlock Trillions in Financial Value ⚛️📈 Quantum computing & finance: this isn’t science fiction — it’s strategy. In 2024, Goldman Sachs revealed that quantum models could cut computation times from days to seconds. BlackRock, JPMorgan, HSBC — they’re not watching from the sidelines. They’re already testing quantum use cases. Why? Because traditional systems can’t handle the sheer complexity of some core financial problems — even with supercomputers. Here are 3 real-world applications — with clear examples and hard numbers VCs will appreciate: 1️⃣ Portfolio optimization (NP-complete problems) 🎯 Imagine choosing 40 assets from 5,000, under constraints like liquidity, volatility, ESG scores, risk exposure… ⚠️ Classic algorithms need to test billions of combinations — a computational nightmare. ✅ Quantum algorithms (like QAOA) dramatically reduce the complexity, helping generate better portfolios in less time. 💰 For large asset managers, improving performance by just 0.5% means millions in added returns annually. 2️⃣ Pricing complex options 💡 Structured products with multiple underlyings require multi-factor models. Monte Carlo simulations can take hours even on advanced infrastructure. 🚀 Quantum models simulate probability distributions natively, enabling pricing in seconds. 📉 That time edge = better arbitrage = real financial alpha. 3️⃣ Fraud & anomaly detection at scale 🔍 A bank like Citi processes over 30 billion transactions per year. Finding subtle or cross-channel anomalies in real time? Nearly impossible. 🧠 Quantum systems can analyze massive datasets simultaneously, mapping complex correlations that classical systems can’t. ➡️ Think of flagging coordinated fraud across accounts, markets, and behavior patterns — before it even happens. 🎯 For investors: • The quantum finance market could surpass €850M by 2030 (source: McKinsey). • Early adopters are building deep tech moats — from IP to specialized data pipelines. • The ecosystem is still forming — there’s huge upside in early-stage SaaS, quantum APIs, hybrid modeling tools, and hardware interfaces. 💬 Quantum hardware is still maturing. But the financial use cases are already validated — and the upside is real. 📌 Just like AI in 2015: those who invest before the explosion will shape the next generation of financial infrastructure. #QuantumComputing #Finance #VC #Fintech #DeepTech #StartupInvesting #NextGenFinance #Innovation #PortfolioOptimization #FraudDetection #OptionPricing

The high-energy physics (HEP) community is particularly poised to benefit from quantum computing due to the intrinsic quantum nature of its most complex computational challenges. These include theoretical models that are hard to tackle with classical computers and the complex data analysis required for the interpretation of experiments like those carried out at the Large Hadron Collider. In a collaborative effort led by CERN, DESY, and IBM, a roadmap has been created to outline the current state of quantum computing in the HEP community. This roadmap highlights both theoretical and experimental applications that can be pursued with near-term quantum computers. This work emphasizes the potential of quantum computing to address challenging problems in HEP and aims to encourage continued exploration and development of quantum applications in this field. I look forward to see the roadmap overviewed in this paper get closer to fruition, and to the next published paper that will come of our working groups, pushing for near-term use cases for quantum computing. Read the paper here https://lnkd.in/eCibpTg2