Real-World Uses of Quantum Agents

D-Wave’s Quantum Leap: Solving Ford’s Real-World Optimization Problem Quantum Annealing Meets Industry as D-Wave Tackles Automotive Challenges In a significant milestone for applied quantum computing, Palo Alto-based D-Wave Quantum Inc. has demonstrated how its hybrid quantum-classical platform can solve real-world industrial problems—most recently for global automobile giant Ford Motor Company. The breakthrough signals a shift from theoretical promise to practical implementation, as quantum computing begins to deliver measurable benefits in the manufacturing and logistics sectors. Quantum Computing’s Practical Edge • What Makes Quantum Different • Unlike classical computers that operate using bits (0s and 1s), quantum computers leverage quantum states, enabling them to process vast combinations of variables simultaneously. • This capability is particularly powerful for problems involving optimization, pattern recognition, and combinatorial complexity—areas where traditional supercomputers often hit limits. • D-Wave’s Unique Approach: Quantum Annealing • D-Wave uses a quantum annealing architecture, ideal for finding optimal solutions by simulating the way natural systems seek their lowest energy state. • Its hybrid system blends quantum processors with classical algorithms, making the platform ready for real-world use today, unlike more fragile gate-based quantum systems still in development. Ford’s Optimization Problem and D-Wave’s Solution • Industrial Workflow Optimization • Ford sought to improve operational efficiency in its manufacturing and logistics systems—complex processes involving thousands of interdependent variables. • Using D-Wave’s quantum annealing platform, the problem was modeled as an energy landscape, and the machine rapidly identified the lowest-energy (most efficient) configuration. • Real-World Impact • This approach led to more streamlined scheduling, reduced production delays, and optimized inventory management, demonstrating tangible ROI. • Ford’s case illustrates how quantum computing can already be integrated into existing enterprise workflows, offering a glimpse of how industry can benefit before universal quantum computers are available. Why It Matters for the Quantum Ecosystem • Bridging Theory and Application • D-Wave’s success highlights a commercially viable path for quantum technology through targeted problem-solving, particularly in logistics, finance, automotive, and pharmaceuticals. • The company’s hybrid architecture bypasses the need for error correction or extremely low error rates, giving it a first-mover advantage in real-world deployments. • Growing Momentum Across Sectors • This milestone reinforces the belief that quantum value creation doesn’t have to wait for fault-tolerant, general-purpose machines. • It also raises the bar for startups and tech giants competing in the quantum space, accelerating the push toward broader industrial adoption.

Breaking Quantum News: Real algorithms, real data, real quantum machines HSBC, in partnership with IBM, has delivered the world’s first quantum-enabled algorithmic trading trial. Using live, production-scale data from the European corporate bond market, HSBC integrated IBM’s quantum processors with classical systems—achieving up to a 34% improvement in predicting the probability of winning trades compared with classical methods alone. Why it matters: - Bond trading is one of the most complex, data-heavy challenges in finance. - Classical models struggle to capture hidden pricing signals in noisy markets. - By augmenting workflows with IBM Quantum Heron, HSBC uncovered insights classical systems could not. As Philip Intallura Ph.D, HSBC’s Global Head of Quantum Technologies, put it: “This is a tangible example of how today’s quantum computers could solve a real-world business problem at scale and offer a competitive edge.” And as IBM’s Jay Gambetta emphasized: breakthroughs come from combining deep financial expertise with cutting-edge quantum algorithms—demonstrating what becomes possible as quantum advances. This is not hype. It’s not distant. Quantum is entering the market—today. #QuantumComputing #Finance #Innovation #PQC #QuantumReady

Forget quantum computers for a second. The real revolution is happening in the air, miles above our heads. Imagine a commercial airliner flying through a severe conflict zone where GPS is completely jammed. Instead of losing its bearings, the aircraft relies on a quantum accelerometer—measuring the movement of trapped atoms to navigate flawlessly across the globe without a single satellite signal. Lets Learn #Quantum – Post 15: Quantum Sensors – The First Commercial Quantum Revolution. When people hear about quantum technology, they usually think about computing. But what if the first major quantum revolution doesn't come from computing at all? What if it comes from sensing the world with unprecedented precision? Quantum Sensors are changing the game. How Do Quantum Sensors Work? Quantum sensors leverage quantum properties such as: 1 Superposition: Detecting multiple possibilities simultaneously. 2 Quantum Coherence: Preserving extremely sensitive measurement states. 3 Atomic Precision: Using atoms themselves as ultra-accurate reference points. The result? Measurements that can be far more precise than many conventional technologies. Real-World Examples  Navigation Without GPS: Ships, submarines, and autonomous systems could navigate accurately deep underwater or in remote regions even when GPS signals are unavailable or intentionally disrupted.  Submarine Detection: Traditional sonar struggles to find modern, ultra-quiet submarines. Quantum magnetometers can detect the microscopic distortions a submarine’s steel hull creates in the Earth’s magnetic field from miles away, completely shifting the dynamics of maritime security.  Mineral and Resource Exploration: Instead of drilling expensive, speculative boreholes, mining companies can use quantum gravity sensors mounted on drones to "see" underground, identifying the exact density footprint of a copper deposit or lithium vein buried deep beneath the rock.  Medical Imaging: Current MRI machines require massive, freezing-cold magnets. Future medical clinics might use room-temperature quantum diamond sensors placed directly on a patient's scalp, mapping brain activity at the single-neuron level to detect Alzheimer's years before symptoms appear. Why Is This Important? Unlike quantum computing, many quantum sensing technologies are already moving from laboratories into real-world deployments. This means organizations may experience practical quantum benefits much sooner than expected. #QuantumTechnology #QuantumSensors #QuantumInnovation #DeepTech #EmergingTechnology #soyoucan Co-authored with Atul Tripathi Sundar Ram, Sachin Arora, Himanshu Ghawri, Azizur Rahman, Himadri Ganguly, Arun Rangaraju, Prasun Nandy, Navnit Nakra, Vinish Bawa, Rajesh Kumar Ojha, Dheeraj Gangrade, Indrojeet Bhattacharya (IN), Debankur Ghosh, Abhijit Chakraborty, Arihant Garg, Dr. Raghav Manohar Narsalay, Rajesh Sethi

Many of you will have seen the news about HSBC’s world-first application of quantum computing in algorithmic bond trading. Today, I’d like to highlight the technical paper that explains the research behind this milestone. In collaboration with IBM, our teams investigated how quantum feature maps can enhance statistical learning methods for predicting the likelihood that a trade is filled at a quoted price in the European corporate bond market. Using production-scale, real trading data, we ran quantum circuits on IBM quantum computers to generate transformed data representations. These were then used as inputs to established models including logistic regression, gradient boosting, random forest, and neural networks. The results: • Up to 34% improvement in predictive performance over classical baselines. • Demonstrated on real, production-scale trading data, not synthetic datasets. • Evidence that quantum-enhanced feature representations can capture complex market patterns beyond those typically learned by classical-only methods. This marks the first known application of quantum-enhanced statistical learning in algorithmic trading. For full technical details please see our published paper: 📄 Technical paper: https://lnkd.in/eKBqs3Y7 📰 Press release: https://lnkd.in/euMRbbJG Congratulations to Philip Intallura Ph.D , Joshua Freeland Freeland and all HSBC colleagues involved — and huge thanks to IBM for their partnership.

Interesting approach alert! QUBO-based SVM tested on QPU (Neutral Atoms). A recent study, "QUBO-based SVM for credit card fraud detection on a real QPU," explores the application of a novel quantum approach to a critical cybersecurity challenge: credit card fraud detection. Here are some of the key findings: * QUBO-based SVM model: The study successfully implemented a Support Vector Machine (SVM) model whose training is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach could leverage the capabilities of quantum processors. * Performance: The results demonstrate that a version of the QUBO SVM model, particularly when used in a stacked ensemble configuration, achieves high performance with low error rates. The stacked configuration uses the QUBO SVM as a meta-model, trained on the outputs of other models. * Noise robustness: Surprisingly, the study observed that a certain amount of noise can lead to enhanced results. This is a new phenomenon in quantum machine learning, but it has been seen in other contexts. The models were robust to noise both in simulations and on the real QPU. * Scalability: Experiments were extended up to 24 atoms on the real QPU, and the study showed that performance increases as the size of the training set increases. This suggests that even better results are possible with larger QPUs. Practical implications: This research highlights the potential of quantum machine learning for real-world applications, using a hybrid approach where the training is performed on a QPU and the testing on classical hardware. This approach makes the model applicable on current NISQ devices. The model is also advantageous because it uses the QPU only for training, reducing costs and allowing the trained model to be reused. * Ideal for cybersecurity and regulatory issues: The study also observed that the model preserves data privacy because only the atomic coordinates and laser parameters reach the QPU, and the model test is done locally. Here the article: https://lnkd.in/d5Vfhq2G #quantumcomputing #machinelearning #cybersecurity #frauddetection #neutralatoms #QPU #NISQ #quantumml #fintech #datascience

“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!

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

🔐💻 Everyone in #quantum #computing seems to be chasing hundreds or thousands of qubits — to break encryption, simulate chemistry, or power quantum machine learning. But what if just 4 high-quality qubits could already outperform classical systems in a real-world task? 🛰️ A recent preprint (https://lnkd.in/e6m2gcsm) on quantum-processing-assisted classical #communications shows exactly that: Using joint quantum measurements on codewords of weak optical signals, a quantum receiver with only 4 qubits can exceed the performance of any known classical optical decoder — even with realistic gate error rates and losses. ⚡️ This isn’t about quantum supremacy or exotic algorithms. It's about using small-scale quantum logic to unlock real advantages in classical communication — like deep-space links, secure low-power channels, or covert messaging. Pretty cool idea and approach. And it is a potentially really practical application of QC. ‼️ Good job from Harvard and MIT! I'm looking for an experimental demonstration!

Real use-case, real-data set, real quantum computer! We published a white paper together with Deutsche Bahn, assigning trains to scheduled services in a way that minimizes operational cost while satisfying a range of hard constraints. Using a real operational dataset from Deutsche Bahn, a schedule of 190 trips across five German cities translating into roughly 98,500 possible cycles, IQM Quantum Computers and DB developed and tested a hybrid quantum-classical algorithm designed for enterprise-scale optimization problems. There are two reasons why I like this use-case: 1) It is a recurring use-case: trains are running on a daily schedule with changing boundary conditions. Meaning the optimization task is not going to go away. This is different from, for example, molecular simulations, where you run the simulation once until you have the desired outcome. 2) It benefits the broader society: running critical infrastructure like a national train system in an efficient way impacts a large group of people. It is important to showcase that a new technology like quantum computing is not only relevant for a small group of experts but will have wider impact on society. Thanks Manfred Rieck, Martin Leib, Jiri Guth Jarkovsky and teams for this nice collaboration! Press release: https://iqm.tech/press-releases/iqm-and-deutsche-bahn-demonstrate-quantum-algorithm-for-railway-scheduling-on-real-operational-data/ Scientific paper: https://arxiv.org/pdf/2606.11383 White paper: https://iqm.tech/wp-content/uploads/2026/07/IQM-DB-RailwayOptimization-Whitepaper.pdf Inés De Vega Dimitrios P. Sylwia Barthel de Weydenthal Craig Ciesla Soren Hein Juha Vartiainen Tomi Riipinen Juha Hassel Jan Kuerschner Blair Robertson Mark Falcon Pasi Kivinen

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