Quantum Technology in Battery and Logistics Solutions

“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!

New preprint❗How we improved the runtime of V2G by 10000x using quantum/classical machine learning! Inspired by the approach of #AlphaGo, we and IBM Quantum developed a novel machine learning based approach to solving the problem of #realtime battery discharging scheduling to meet pre-defined energy amount delivery (e.g. we must deliver 5 MW of electricity to the grid at 12:30pm, how do we discharge connected car batteries to do that). In this work, we proved the problem scales exponentially classically, apply quantum kernel methods as well as classical SVM. This paper is the first of a series on the topic, the next will dig into details of the quantum approach. Paper link here 👉 https://lnkd.in/d2EagfBi Many thanks to the brilliant collaborators on this project! Gabriele Agliardi Giorgio Cortiana Anton Dekusar Kumar Ghosh Naeimeh Mohseni Víctor Valls Kavitha Yogaraj Sergiy Zhuk Jay Gambetta Dr. Christian Essling E.ON Digital Technology #QuantumComputing #UnitCommitment #EVCharging

10 million containers. Thousands of trucks. Hundreds of cranes. One impossible scheduling problem. Welcome to the Port of Los Angeles—the largest container port in the US and a critical node in global supply chains. The bottleneck: Every day, Pier 300 (one of the port's largest terminals) faces a computational nightmare: - Which truck goes to which crane? - When do arrivals shift due to delays? - How do you balance load across equipment? - What happens when conditions change every few minutes? Classical scheduling systems couldn't keep up: ⏱️ Long truck wait times (sometimes 2+ hours) 🏗️ Inefficient crane utilization 📉 Reduced throughput during peak periods 💰 Millions in lost productivity Then they deployed quantum optimization. Working with quantum computers, Pier 300 built a system that: 🔬 Simulates 100,000+ cargo-handling scenarios 🎯 Optimizes truck-to-crane assignments in real-time 🔄 Updates every few minutes across two daily shifts ⚡ Runs with 99.999% availability The results: ✅ ~40% reduction in crane usage → Lower labor and equipment costs ✅ ~60% increase in container deliveries per crane → Massive productivity gain ✅ 10 minutes reduced per truck visit → Up to 2 hours in some cases ✅ Tens of millions in annual savings → Plus increased terminal asset value Why this matters: This isn't theory. This is a working terminal processing millions of containers with measurable, bottom-line impact. The shift: From "schedule and hope" to "optimize continuously." Classical algorithms could generate a schedule. Quantum systems generate the optimal schedule—and update it dynamically as reality changes. The insight for supply chain leaders: Port operations are some of the most complex scheduling challenges on the planet. If quantum optimization can handle this, what could it do for your: 📦 Warehouse operations? 🚚 Fleet routing? 📊 Inventory allocation? 🏭 Production scheduling? The computational barrier just fell. The logistics advantage is here. Question: What's the biggest bottleneck in your logistics operations that classical optimization can't crack? #QuantumComputing #Truckl #SupplyChain #Transportation #Innovation

Our R&D team at Stellium Inc. has recently been diving deep into concepts like quantum machine learning and quantum PCA, with the goal of identifying the best levers out there to address supply chain challenges with emerging tech. After our most recent midmonth Innov8 workshop, I’m no longer surprised by the fact that the market size for quantum computing is projected to grow at a CAGR of 18+% during the forecast period 2025-2032. The modern supply chain, as we all know, forms a sophisticated network of interconnected elements, where decision-making amid complexity often involves significant uncertainty. Effective management hinges on processing vast streams of real-time data to minimize costs and fulfill customer demands. As these global systems expand, classical computing approaches are reaching their limits in processing speed and handling intricate modeling. Enter Quantum Computing: 🎱 Quantum solutions are exceptionally positioned to tackle the most demanding challenges in logistics, including route optimization, operational efficiency, and emissions reduction. This capability stems from foundational quantum mechanics principles such as Superposition, Interference and Entanglement, that are redefining computational processes. For supply chain executives, this really boils down to resolving complex problems more rapidly than classical algorithms, including those on supercomputers. The aim is to develop responsive analytics through dramatically reduced computation times. Large scale supply chain optimization problems are no longer going to need hrs or days but rather seconds. Industry researchers and a few enterprises are already applying techniques such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing. These methods reformulate combinatorial challenges, like the traveling salesman problem in transportation logistics into quantum frameworks, identifying optimal solutions by reaching the ‘minimum energy state’. We are now seeing progress beyond conceptual stages to practical Proofs of Concept (PoCs): • BMW Group applied recursive QAOA to address partitioning issues in supply chain resource allocation. • Volkswagen demonstrated real-time optimal routing through urban traffic variations. • Coca-Cola Bottlers Japan Inc. utilized quantum computing to refine their logistics for a network exceeding 700,000 vending machines. Quantum-powered logistics and supply chain innovations are poised for substantial growth in the years ahead. Forward-thinking organizations recognize the impending transformation and are proactively preparing to become quantum-ready. At Stellium Inc., we are in our early R&D stage when it comes to exploring quantum use cases and strategic partnerships. I am bullish about the impact it’s going to have on supply chain and recognize the need to invest in it right now. DM if you’re interested to discuss more over coffee at Dubai this coming week or at SAP Connect early October in Vegas.

Quantum Batteries Show Real-World Charging Advantage Over Classical Designs Introduction Researchers have taken a significant step toward practical quantum batteries—energy storage devices that use quantum mechanical effects rather than traditional electrochemical processes. A collaborative team from the Southern University of Science and Technology in China and Spain’s CSIC demonstrated a superconducting quantum battery that charges faster than a comparable classical system under the same energy constraints. Key Breakthrough Demonstrating quantum charging advantage The team built a battery based on superconducting qubits, the same type of hardware used in many quantum computers. Experiments showed that the quantum battery charged faster than a classical equivalent while using the same total energy input. This performance improvement arises from collective quantum interactions between qubits. How quantum batteries work Quantum batteries store energy in qubits that can exist in superposition between energy states. When qubits interact with each other, they can form entangled collective dynamics that enable faster energy transfer. These quantum correlations allow the battery to deliver higher charging power than isolated classical units. Engineering a practical design Earlier theoretical models relied on “all-to-all” interactions that are difficult to implement experimentally. The researchers instead used a scalable architecture based on local, nearest-neighbor interactions between qubits. This design works with existing superconducting quantum processors and standard microwave control techniques. Why superconducting platforms matter Superconducting qubits allow engineers to precisely control interactions between artificial atoms. The platform enables unusual atom-to-atom coupling required for collective quantum charging. The experiment represents one of the largest hardware-compatible multi-cell quantum battery demonstrations to date. Potential future applications Quantum batteries could power quantum processors, sensors, and simulators that require controlled energy delivery. They may store work produced by quantum heat engines, enabling closed-loop quantum energy systems. Hybrid systems could even convert stored quantum energy into mechanical motion through phonons. Conclusion: Why This Matters Quantum computing, sensing, and communication technologies require new approaches to energy management at the quantum scale. This research demonstrates that quantum batteries can deliver measurable performance advantages using experimentally feasible hardware. While practical energy capacity remains far from real-world needs, the work establishes a scalable architecture that could eventually become part of integrated quantum technology infrastructures. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw