Practical Applications of Quantum Physics Models

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

Lockheed and IBM Use Quantum Computing to Solve Chemistry Puzzle Once Thought Impossible Introduction: Cracking a Chemical Code with Quantum Power In a breakthrough for quantum chemistry, Lockheed Martin and IBM have successfully used quantum computing to model the complex electronic structure of an “open-shell” molecule—a challenge that has defied classical computing for years. This marks the first application of the sample-based quantum diagonalization (SQD) method to such systems and signals a significant advance in the practical application of quantum computing for scientific research. Key Highlights from the Collaboration • The Molecule: Methylene (CH₂): • Methylene is an open-shell molecule, meaning it has unpaired electrons that lead to complex quantum behavior. • These molecules are notoriously difficult to simulate accurately because electron correlations create exponentially growing complexity for classical algorithms. • The Innovation: Sample-Based Quantum Diagonalization (SQD): • The team used IBM’s quantum processor to implement SQD for the first time in an open-shell system. • SQD is a hybrid algorithm that leverages quantum sampling to solve eigenvalue problems in quantum chemistry, reducing computational burdens. • Why Classical Methods Fall Short: • Traditional high-performance computing (HPC) platforms struggle with electron correlation in multi-electron systems. • Approximation techniques become prohibitively expensive as system size increases, especially for reactive or radical species like methylene. • Quantum Advantage in Practice: • Quantum processors can represent electron configurations using entangled qubits, offering more scalable solutions. • By simulating the electronic structure directly, quantum methods could help scientists design new materials, catalysts, and pharmaceuticals faster and more efficiently. Why It Matters: Pushing Past the Limits of Classical Chemistry • Industrial and Scientific Impact: • Simulating open-shell systems is vital for battery design, combustion processes, and metalloprotein modeling. • The success of SQD opens the door to accurate modeling of previously inaccessible molecules, potentially accelerating innovations in energy, health, and aerospace. • Defense and Aerospace Relevance: • Lockheed Martin’s involvement reflects strategic interest in applying quantum computing to defense-grade materials and mission-critical chemistry. • Quantum Chemistry as a Flagship Use Case: • This achievement underscores how quantum computing is beginning to deliver real results in scientific domains where classical methods hit their ceiling. • As quantum hardware improves, the number of solvable molecular systems will expand exponentially. Quantum computing just helped humanity take a critical step into the chemical unknown, proving its value not just in theory—but in practice. Keith King https://lnkd.in/gHPvUttw

🌟 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝘄𝗶𝘁𝗵 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 🌟 Excited to share a groundbreaking study that explores the potential of quantum computing in transforming the pharmaceutical industry! 🚀💊 🧪 𝗙𝗼𝗰𝘂𝘀: Precise determination of Gibbs free energy profiles for prodrug activation. Accurate simulation of covalent bond interactions. This pioneering work goes beyond conventional proof-of-concept studies by addressing real-world drug design challenges. By constructing a versatile quantum computing pipeline, the researchers have taken significant steps towards integrating quantum computation into practical drug discovery workflows. 🧬🔗 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 💥 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗧𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝗧𝗮𝗻𝗴𝗶𝗯𝗹𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Unlike previous studies that were primarily theoretical, this research implements a hybrid quantum computing pipeline to solve practical problems in drug design. This marks a significant shift towards real-world applicability of quantum computing in pharmaceuticals, making it a valuable tool for researchers and industry professionals. 💥 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗩𝗲𝗿𝗶𝘁𝗮𝗯𝗹𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗶𝗻 𝗗𝗿𝘂𝗴 𝗗𝗲𝘀𝗶𝗴𝗻: The study sets a new benchmark by applying quantum computing to actual drug design scenarios. This involves precise calculations and simulations that are critical in the drug discovery process, showcasing the capability of quantum computing to handle complex biochemical problems that traditional methods struggle with. 💥 𝗘𝗺𝗽𝗵𝗮𝘀𝗶𝘇𝗶𝗻𝗴 𝗖𝗼𝘃𝗮𝗹𝗲𝗻𝘁 𝗕𝗼𝗻𝗱𝗶𝗻𝗴 𝗜𝘀𝘀𝘂𝗲𝘀 𝗶𝗻 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝗶𝗲𝘀: The research specifically targets covalent bond interactions, a crucial aspect in drug development. By focusing on the precise determination of Gibbs free energy profiles for prodrug activation and accurate simulation of covalent bond interactions, the study addresses critical tasks that are central to designing effective drugs. This focus on covalent bonding issues underscores the practical significance of the study. The results demonstrate the immense potential of quantum computing in creating scalable solutions for the pharmaceutical industry. This is a remarkable step forward in the quest to revolutionize drug discovery and design! 🌐💡 Citation: Li, W., Yin, Z., Li, X. et al. A hybrid quantum computing pipeline for real world drug discovery. Sci Rep 14, 16942 (2024). https://lnkd.in/d3mkrAPs #QuantumComputing #DrugDiscovery #Pharmaceuticals #Innovation #Technology #Science #Research

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

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.

Today in Science Magazine, work from our IBM team, in collaboration with The University of Manchester, University of Oxford, ETH Zürich, EPFL and the University of Regensburg, shows the creation and simulation of a new molecule with an electronic structure that has never existed before — a half‑Möbius topology: https://lnkd.in/eFU5s9qR. The molecule was assembled using scanning probe microscopy at temperatures just above absolute zero — building it one atom at a time using STM, atom manipulation, and AFM. The electronic orbitals of this half‑Möbius molecule twist by 90 degrees with every loop around the ring, completing a full turn only after four revolutions. Why is this also important for quantum computing? This work demonstrates, for the first time, that quantum computing calculations can provide decisive scientific guidance and powerful characterization capabilities to support the discovery of new complex chemical molecules. In close collaboration with leading experimental laboratories, quantum simulations can now contribute directly to interpreting experimental observations and to guiding the design and understanding of novel molecular systems. The calculations performed in this project go well beyond the regime accessible to brute-force classical simulations, although we do not exclude the possibility that approximate classical methods could also provide valuable insights. Nevertheless, the discovery process itself benefited from quantum simulation, and we chose to employ quantum computing because it offers a natural and scalable framework for tackling problems of this kind. In particular, by comparing Dyson orbitals measured with scanning tunneling microscopy (STM) with images reconstructed from electronic structure calculations performed on a quantum computer using the SqDRIFT algorithm, we were able, for the first time, to contribute directly to the discovery and characterization of a new molecule exhibiting entirely novel electronic structure properties. paper: https://lnkd.in/esg9sHqV

At the intersection of AI and the real world -- can we solve some of science's greatest problems? Ever wondered how quantum computing could revolutionize our understanding of complex systems? Well, this article dives into how quantum computers can significantly speed up simulations compared to classical computers, especially when dealing with coupled classical oscillators (think of them as swinging pendulums that affect each other's movement -- and which can be used to solve all kinds of physics problems). In simple terms, while traditional computers might take longer to calculate how these pendulums interact as their number increases, a quantum computer can do this much faster—achieving what's called an "exponential speedup." This means that as the problem grows bigger (more pendulums), the advantage of using quantum computers grows dramatically. The researchers, including experts from Google Quantum AI and several universities, demonstrate a theoretical framework where quantum algorithms outperform their classical counterparts. They provide a method that could be practically implemented on quantum devices in the near future, paving the way for more efficient simulations in physics, engineering, and beyond. This breakthrough not only highlights the potential of quantum computing to solve specific technical challenges but also gets us closer to realizing practical applications that were once thought to be decades away. It's an exciting peek into a future where quantum computing could become a key tool in scientific research and innovation. https://lnkd.in/gFmW8bEX

Everybody’s asking about the 𝗸𝗶𝗹𝗹𝗲𝗿 𝗮𝗽𝗽 𝗳𝗼𝗿 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀. But when a team actually uses one to explore 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 in a way we couldn't before, the 𝘀𝗶𝗹𝗲𝗻𝗰𝗲 from the broader community is deafening. Really? I’ve talked about using quantum computers for exploring physics before. I get it - 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲, 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝘃𝗲 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗩𝗖𝘀 𝗮𝗻𝗱 𝗺𝗮𝗿𝗸𝗲𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗮𝗯𝗼𝘂𝘁. 𝗕𝘂𝘁 𝗜 𝗳𝗶𝗻𝗱 𝗶𝘁 𝗮𝗯𝘀𝗼𝗹𝘂𝘁𝗲𝗹𝘆 𝗮𝗺𝗮𝘇𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘄𝗲'𝗿𝗲 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝘁𝗵𝗮𝘁 𝗮𝗹𝗹𝗼𝘄 𝘂𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝗿 𝘄𝗼𝗿𝗹𝗱 𝗼𝗻𝗲 𝗹𝗮𝘆𝗲𝗿 𝗱𝗲𝗲𝗽𝗲𝗿. A new paper from Google 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗔𝗜 & 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘀, is a perfect case in point. The team tackled a monster of a problem in condensed matter physics: 𝗵𝗼𝘄 𝘁𝗼 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗱𝗶𝘀𝗼𝗿𝗱𝗲𝗿. Classically, this is a brute-force nightmare: You have to simulate thousands or even millions of different disorder configurations one by one, which can take an exponential amount of time. 𝗜𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗻𝗴 𝗼𝗻𝗲 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝗮 𝘁𝗶𝗺𝗲, 𝗚𝗼𝗼𝗴𝗹𝗲 𝘂𝘀𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 𝟴𝟭-𝗾𝘂𝗯𝗶𝘁 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗼𝗿 𝘁𝗼 𝗽𝗿𝗲𝗽𝗮𝗿𝗲 𝗮 𝘀𝘁𝗮𝘁𝗲 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮𝗹𝗹 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲 𝗱𝗶𝘀𝗼𝗿𝗱𝗲𝗿 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀. Then they gave it a tiny kick of energy in one spot, and watched what happened. The result? The energy stayed put. It refused to spread. This is a phenomenon called 𝗗𝗶𝘀𝗼𝗿𝗱𝗲𝗿-𝗙𝗿𝗲𝗲 𝗟𝗼𝗰𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 (𝗗𝗙𝗟). Even though the system's evolution and the initial state were perfectly uniform and disorder-free, the underlying superposition over different "backgrounds" caused the system to localize. 𝗜𝘁’𝘀 𝗮 𝘀𝘁𝘂𝗻𝗻𝗶𝗻𝗴 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗺𝗲𝗰𝗵𝗮𝗻𝗶𝗰𝘀 𝗮𝘁 𝘄𝗼𝗿𝗸 𝗼𝗻 𝗮 𝘀𝗰𝗮𝗹𝗲 𝘁𝗵𝗮𝘁’𝘀 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝘆 𝗱𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁 𝗳𝗼𝗿 𝗰𝗹𝗮𝘀𝘀𝗶𝗰𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀 𝘁𝗼 𝗵𝗮𝗻𝗱𝗹𝗲, 𝗲𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗹𝘆 𝗶𝗻 𝟮𝗗. But this isn't just a cool physics experiment. This work carves out a concrete path to quantum advantage. The team proposed an 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺 based on this technique that offers a 𝗽𝗼𝗹𝘆𝗻𝗼𝗺𝗶𝗮𝗹 𝘀𝗽𝗲𝗲𝗱𝘂𝗽 𝗳𝗼𝗿 𝘀𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗱𝗶𝘀𝗼𝗿𝗱𝗲𝗿𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. So yes, let's keep working toward fault-tolerant machines that can break RSA and optimize your portfolio. But let's not ignore the incredible science happening right now. 📸 Credits: Google 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗔𝗜 & 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗼𝗿𝘀 (arXiv:2410.06557) Pedram Roushan

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.

Exciting yet under-the-radar paper (arXiv:2506.10191) from Google Quantum AI on higher-order OTOCs (out-of-time-order correlators) -- a big leap toward practical (scientific) quantum advantage! 🚀 Using their Willow chip with ~100 qubits, they’ve shown remarkable result, yet it’s surprising this hasn’t sparked more buzz -- perhaps because OTOCs are tricky to explain to a wider audience. 🤔   Key Takeaways: 🕒 Quantum Speed: Willow chip solves quantum Hamiltonian properties in ~2.1 hours, using ~40 kWh of energy. 💻 Classical Lag: Best classical method (tensor networks) on Frontier supercomputer estimated to take 3.2 years, 550GWh energy—practically infeasible! 🧪 Real-World Impact: Enables learning properties of quantum materials, with applications in chemistry and quantum control. 10,000x reduction in needed energy for simulation.   This showcases power of NISQ-era quantum devices for quantum simulation. Shall we call it scientific quantum advantage? 📢 #QuantumComputing #QuantumAdvantage #GoogleQuantumAI