Applications of Quantum Computing Beyond IT

“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!

Can we find hidden tunnels using quantum computers? For our quantum computing final project, my team and I decided to find out. Detecting subsurface structures, such as tunnels, aquifers, or voids, is impossible using classical methods, as classical gravimeters are plagued by vibrations, tilt, and drift. That's where Akshat, Aakrisht, Sahana, Landon, and I's Physics 19N final project, GraviQ: Simulating Subsurface Mapping with a Qubit-Based Gravimeter, comes in. By simulating an "hourglass" configuration of two atom clouds, we can measure the vertical gravity gradient (Gzzs) while canceling out the environmental noise. We built our procedure in three steps: 1) We generated 2D density grids representing rock, ore, tunnels, and caves to create synthetic environments. 2) We used Qiskit, a quantum simulator to model a Ramsey interferometer. We mapped subsurface density to qubit phase shifts, simulating the behavior of a real quantum sensor (including decoherence and sampling noise). 3) We fed the resulting Gzz maps into a U-Net machine learning segmentation model. The tentative results are notable. Despite the simulated noise, our model achieved ~95% accuracy in detecting tunnel presence and a Dice score of up to 0.85 for localization. We believe if we can replicate this in real life, the applications are far-reaching in fields ranging from civil engineering and infrastructure, to mineral extraction, to even space exploration. Here are links to our code and slides: GitHub: https://lnkd.in/eRUYWvj6 Slides: https://lnkd.in/eeBv-F5h Huge thanks to my teammates Akshat Kannan, Aakrisht Mehra, Sahana, and Landon Moceri, and Professor Hari Manoharan for the guidance and discussions along the way. Happy to chat with anyone interested in or working on quantum sensing or related research!

One-Atom Quantum Computer Simulates Molecular Reactions with Unprecedented Efficiency Introduction: A Quantum Breakthrough in Chemistry Simulation A research team has successfully used a one-atom quantum computer to simulate how real molecules evolve over time after absorbing light—something that has long challenged classical computing. Published in the Journal of the American Chemical Society, this study represents a milestone in quantum chemistry and demonstrates a method that’s reportedly a million times more efficient than conventional quantum simulation techniques. Key Innovations and Findings: 1. Simulating Molecular Change, Not Just Static Properties • Traditional quantum computers have so far only been used to calculate static molecular properties—like energy levels or bond strengths. • This new method allows for dynamic simulations: modeling how molecules respond to light, including electron excitation, atomic vibration, and bond reshuffling—processes critical to photosynthesis, solar cells, and photomedicine. 2. Trapped Ion Technology • The researchers used a trapped calcium ion, essentially a one-atom quantum processor, as their simulation platform. • By manipulating the ion’s quantum state, they recreated the time-evolution of molecular systems at femtosecond (quadrillionth of a second) resolution—matching the timescales of real photochemical reactions. 3. Radical Leap in Efficiency • The study claims a million-fold increase in resource efficiency compared to standard quantum simulation techniques. • This was achieved through a novel algorithmic approach that minimizes the quantum operations needed to model time-dependent processes. 4. Real-World Applications Simulated • The team successfully modeled specific molecular transformations triggered by light, a foundational step for future advances in: • Drug development • Solar energy design • Photodynamic cancer therapies • DNA damage mitigation research Why This Matters: A New Quantum Era in Chemistry • Understanding photochemical dynamics is central to both biological function and energy technologies, yet has been computationally intractable—until now. • This study shows that even ultra-small quantum systems can tackle complex, real-world problems, provided the algorithms are smart enough. • It suggests a future where chemical simulation becomes routine on small, highly optimized quantum devices, long before fault-tolerant universal quantum computers arrive. Conclusion: One Atom, Big Impact By simulating the fleeting, intricate dance of molecules under light, a single-ion quantum computer has demonstrated that quantum chemistry’s future may be smaller, faster, and more accessible than expected. This research not only overcomes a major bottleneck in simulation but also signals a powerful new direction for time-resolved quantum modeling. Keith King https://lnkd.in/gHPvUttw

Imagine a technology that could radically transform how we compute, solve complex problems, and address global challenges. This is the promise of quantum computing. A striking example of its potential is transforming the fertilizer production industry, which significantly impacts global electricity consumption and greenhouse gas emissions, accounting for about 1% of the world's electricity use. Quantum computing, based on quantum mechanics principles, introduces systems capable of existing in multiple states simultaneously, dramatically speeding up complex computations. This revolutionary technology can redefine AI, cybersecurity, and research and development while tackling critical global issues like climate change. The emergence of quantum computing necessitates new programming languages, development tools, and data processing techniques. Quantum computing is crucial in designing energy storages for renewable energy systems supporting initiatives like the International Solar Alliance. By improving the efficiency of these systems, quantum computing aligns with global clean energy goals, aiding in the transition to sustainable energy sources. The impact of quantum computing on AI is profound. It promises new, interdisciplinary innovations, redefining problem-solving and technological development. Its ability to simulate complex systems, from molecular structures to environmental systems, is fascinating, enabling AI to predict the behaviour of molecules to the dynamics of ecosystems. In security, quantum computing presents both challenges and opportunities. It could render current cryptography systems obsolete, prompting concerns in digital security. Simultaneously, it's spurring the development of quantum-resistant algorithms, a key focus for entities prioritizing security, including national governments. In R&D, particularly in simulating complex physical and chemical processes quantum can be a game changer. This can significantly reduce the time and costs associated with innovation, leading to rapid advancements in pharmaceuticals, materials engineering, and environmental science. We must prioritize education and training in quantum computing principles and applications as we navigate this quantum leap. This is essential to ensure equitable access to quantum technology and avoid deepening global inequalities or Quantum colonization. As governments worldwide recognize the transformative potential of quantum technologies, they are formulating policies to guide their ethical development and use. These initiatives, aiming to foster research, promote industry collaboration, and build necessary quantum infrastructure, ensure that quantum advancements are secure, responsible, and beneficial for society. #BigIdeas2024 Note: I generated the Image using DALL-E

Most enterprises treat quantum computing as a nerdy R&D curiosity. A mistake. Critical business problems, which are fundamentally constrained by classical computing today, are likely to be solved by 2030. With a hybrid combination of high performance computing and quantum approaches. Three sectors stand out: Pharma, Life & Material Sciences: Drug discovery is essentially a molecular simulation challenge. Classical systems approximate. Quantum systems are designed around quantum mechanics itself. Thus, it is not just about faster research, but the ability to model molecular interactions with higher fidelity. For protein folding, compound optimization, personalized therapeutics. Reaching quantum advantage first in pharma won’t merely accelerate pipelines — it will redefine them. Financial Services: Banks, insurers, stock exchanges operate enormous optimization, transaction or probability engines. E.g., for risk simulations, or fraud detections. Many of these problems scale exponentially in complexity. Quantum algorithms are particularly promising where classical Monte Carlo simulations hit practical limits. And, quantum computing is becoming a cybersecurity challenge. Post-quantum cryptography migration will likely be one of the largest infrastructure transitions the financial sector has seen for decades. Complex Logistics & Supply Chains: Airlines, shipping companies, manufacturers, energy grids, and global retailers all face combinatorial optimization problems. These systems already operate at scales where small efficiency gains create major business impact. Enterprises operating in these segments should get „quantum-ready“ now: • Identify quantum-relevant business problems • Work with quantum partners who advocate an open approach • Build internal quantum literacy • Develop hybrid workflows • Prepare your security stack for the post-quantum era. Additionally we need quantum computing companies delivering at production scale. IQM Quantum Computers calls this Production Quantum. Which is the delivery of a production-ready full stack solution rather than just a scientific solution for a specific problem. This is the same pattern we saw with #AI. The competitive gap formed before the technology fully matured. #Quantum readiness is becoming a strategic capability and critical timing question. For an increasing number of enterprises. Not only for R&D departments.

Six months ago, the IDC Worldwide Quantum Computing Forecast made a specific bet: the next phase of quantum computing wouldn't be driven by better hardware alone. It would come from combining increasingly capable quantum systems with AI, HPC, and domain expertise to solve problems beyond the practical reach of classical computing. Last week offered a compelling example of exactly that. IBM, Oak Ridge National Laboratory, and Cleveland Clinic used a hybrid quantum-classical workflow to model the chemistry of molten FLiBe salt, a leading candidate material for future fusion reactors. Rather than replacing classical computing, IBM's increasingly capable quantum hardware was applied to the portion of the problem where it provides the greatest computational advantage, while classical systems handled the remaining calculations. That's exactly the heterogeneous computing model we expect to define enterprise quantum adoption. What's equally important is where this work happened. The research is part of the U.S. Department of Energy's Genesis Mission, bringing together quantum computing, HPC, AI, and domain expertise across the national laboratory ecosystem. Read alongside recent initiatives like QuantumEAGLe, it reinforces a broader trend: government investment is evolving beyond advancing quantum hardware. It's increasingly focused on building the collaborative ecosystem needed to translate scientific breakthroughs into real-world applications. This is also why simulation continues to stand out in our enterprise research. Alongside optimization and quantum AI, simulation remains one of the leading quantum use cases organizations are exploring. Fusion materials research represents one of the most demanding examples imaginable, but the underlying challenge extends well beyond energy. Industries including pharmaceuticals, chemicals, advanced manufacturing, and materials science all face computational problems where heterogeneous computing could eventually deliver meaningful advantages. The remaining challenge isn't demonstrating that quantum can contribute to scientific discovery. It's making these capabilities accessible outside national laboratories. Today's breakthrough required quantum scientists, computational chemists, HPC researchers, and highly specialized workflows. The next phase of the market will depend on advances in both quantum hardware and the surrounding software ecosystem, development platforms, and workflow orchestration that allow domain experts to leverage quantum computing without becoming quantum specialists. I explore what this means for enterprise quantum adoption, heterogeneous computing, and the evolution of the quantum software ecosystem in our latest IDC Link: https://lnkd.in/gFQV95FF Ashish Nadkarni Jeff Janukowicz Jerry M. Chow Jay Gambetta Mike Houston Steven Malkiewicz #Quantum #QuantumComputing #HPC #AI #FusionEnergy #DOE #IDC

The Schrödinger Equation Gets Practical: Quantum Algorithm Speeds Up Real-World Simulations Quantum computing has taken a major leap forward with a new algorithm designed to simulate coupled harmonic oscillators, systems that model everything from molecular vibrations to bridges and neural networks. By reformulating the dynamics of these oscillators into the Schrödinger equation and applying Hamiltonian simulation methods, researchers have shown that complex physical systems can be simulated exponentially faster on a quantum computer than with traditional algorithms. This breakthrough demonstrates not only a practical use of the Schrödinger equation but also the deep connection between quantum dynamics and classical mechanics. The study introduces two powerful quantum algorithms that reduce the required resources to only about log(N) qubits for N oscillators, compared to the massive computational demands of classical methods. This exponential speedup could transform fields such as engineering, chemistry, neuroscience, and material science, where coupled oscillators serve as the backbone of real-world modeling. By bridging theory and application, this research underscores how quantum computing is redefining problem-solving in physics and beyond. With proven exponential advantages and the ability to simulate systems once thought computationally impossible, this quantum algorithm marks a milestone in quantum simulation, Hamiltonian dynamics, and real-world physics applications. The findings point toward a future where quantum computers can accelerate scientific discovery, optimize engineering designs, and even open new frontiers in AI and computational neuroscience. #QuantumComputing #SchrodingerEquation #HamiltonianSimulation #QuantumAlgorithm #CoupledOscillators #QuantumPhysics #ComputationalScience #Neuroscience #Chemistry #Engineering

A quantum computer recently solved a problem in just four minutes that would take even the most advanced classical supercomputer billions of years to complete. This breakthrough was achieved using a 76-qubit photon-based quantum computer prototype called Jiuzhang. Unlike traditional computers, which rely on electrical circuits, this quantum computer uses an intricate system of lasers, mirrors, prisms, and photon detectors to process information. It performs calculations using a technique known as Gaussian boson sampling, which detects and counts photons. With the ability to count 76 photons, this system far surpasses the five-photon limit of conventional supercomputers. Beyond being a scientific milestone, this technique has real-world potential. It could help solve highly complex problems in quantum chemistry, advanced mathematics, and even contribute to developing a large-scale quantum internet. For example, quantum computers could help scientists design new medicines by simulating how molecules interact at the quantum level—something that classical computers struggle to do efficiently. This could lead to faster discoveries of life-saving drugs and treatments. While both quantum and classical computers are used to solve problems, they function very differently. Quantum computers take advantage of the unique properties of quantum mechanics—such as superposition and entanglement—to perform calculations at incredible speeds. This makes them especially powerful for solving problems that would be nearly impossible for traditional computers, bringing exciting new possibilities for scientific and technological advancements. As the Gaelic saying goes, “Tús maith leath na hoibre”—“A good start is half the work.” Quantum computing is still in its early stages, but its potential to reshape science, medicine, and technology is already clear.

Microsoft ’s Majorana 2 program provides one of the strongest current examples of “Quantum–AI Convergence”. Through its Discovery Agentic AI platform, Microsoft used AI not simply as an application layer, but as an #engineering co-designer to accelerate materials discovery, optimize quantum device #architectures, improve fabrication processes, and enhance validation workflows. However, this is only one example of the broader Quantum-Convergence paradigm, where #quantum computing increasingly intersects with #AI, #HPC, advanced #semiconductors, #photonics, #robotics. The convergence is transforming AI from a user of computational infrastructure into an active participant in designing, calibrating, orchestrating, and securing next-generation quantum systems. NVIDIA – Building the orchestration layer for hybrid quantum-classical computing through accelerated computing, quantum simulation, quantum error-correction support, and AI-driven workload management. IBM – Integrating quantum hardware, AI, cloud computing, and enterprise software into unified computational ecosystems with a strong focus on fault-tolerant quantum computing. Google – Applying machine learning to quantum hardware calibration, error mitigation, quantum algorithm development, and advanced scientific computing. Quantinuum – Combining trapped-ion quantum computing, AI-enhanced control systems, cybersecurity, chemistry, and optimization applications. SandboxAQ – Developing Large Quantitative Models (LQMs) that integrate AI with quantum science, chemistry, materials discovery, and post-quantum cybersecurity. Xanadu – Advancing photonic quantum computing, quantum machine learning, differentiable quantum circuits, and AI-enabled photonic optimization. IonQ – Strong focus on AI optimization, quantum networking, distributed quantum computing, and hybrid quantum-classical architectures designed for future scalable quantum ecosystems. D-Wave Quantum – focused on quantum optimization systems, frequently integrated with AI workflows for logistics, manufacturing, scheduling, and industrial optimization. Rigetti Computing – Developing superconducting quantum processors tightly coupled with classical computing resources to support AI, optimization, and scientific simulation workloads. PsiQuantum – Pursuing large-scale fault-tolerant quantum computing through photonic architectures, leveraging silicon-photonics manufacturing techniques that may enable industrial-scale quantum systems. Collectively, these organizations are advancing the foundations for a novel computational ecosystem.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: 𝘀𝗼𝗺𝗲 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗺𝗮𝗿𝗸𝗲𝘁𝘀 𝗺𝗮𝘆 𝘀𝗰𝗮𝗹𝗲 𝗯𝘆 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗲𝘀. QuantumDiamonds is a good example. The company uses quantum sensors to map electrical currents inside advanced chips and help engineers locate faults hidden in complex packages and buried interconnects. The principle is quite simple: electrical currents create tiny magnetic fields. Defects in diamond can measure those fields, while software reconstructs where current is actually flowing inside the device. This is useful because semiconductor inspection is already a large industrial market. The customers exist. The budgets exist. And the problem is becoming harder as chiplets, advanced packaging and 3D integration increase the complexity of modern hardware. QuantumDiamonds has now secured €91 million to scale the technology, including support for a first-of-a-kind production facility in Munich. For European quantum, the broader signal is interesting. Industrialisation does not always require creating an entirely new market. Quantum technologies can also improve critical processes inside industries that are already large, sophisticated and ready to pay for better tools. Semiconductor inspection may be one such route for quantum sensing. #QuantumSensing #Semiconductors #DeepTech #QuantumTechnology #EuropeanTech