Quantum Computing Applications in Problem Solving

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

Thought you knew which #quantumcomputers were best for #quantum optimization? The latest results from Q-CTRL have reset expectations for what is possible on today's gate-model machines. Q-CTRL today announced newly published results that demonstrate a boost of more than 4X in the size of an optimization problem that can be accurately solved, and show for the first time that a utility-scale IBM quantum computer can outperform competitive annealer and trapped ion technologies. Full, correct solutions at 120+ qubit scale for classically nontrivial optimizations! Quantum optimization is one of the most promising quantum computing applications with the potential to deliver major enhancements to critical problems in transport, logistics, machine learning, and financial fraud detection. McKinsey suggests that quantum applications in logistics alone are worth over $200-500B/y by 2035 – if the quantum sector can successfully solve them. Previous third-party benchmark quantum optimization experiments have indicated that, despite their promise, gate-based quantum computers have struggled to live up to their potential because of hardware errors. In previous tests of optimization algorithms, the outputs of the gate-based quantum computers were little different than random outputs or provided modest benefits under limited circumstances. As a result, an alternative architecture known as a quantum annealer was believed – and shown in experiments – to be the preferred choice for exploring industrially relevant optimization problems. Today’s quantum computers were thought to be far away from being able to solve quantum optimization problems that matter to industry. Q-CTRL’s recent results upend this broadly accepted industry narrative by addressing the error challenge. Our methods combine innovations in the problem’s hardware execution with the company’s performance-management infrastructure software run on IBM’s utility-scale quantum computers. This combination delivered improved performance previously limited by errors with no changes to the hardware. Direct tests showed that using Q-CTRL’s novel technology, a quantum optimization problem run on a 127-qubit IBM quantum computer was up to 1,500 times more likely than an annealer to return the correct result, and over 9 times more likely to achieve the correct result than previously published work using trapped ions These results enable quantum optimization algorithms to more consistently find the correct solution to a range of challenging optimization problems at larger scales than ever before. Check out the technical manuscript! https://lnkd.in/gRYAFsRt

Excited to announce a new #QuantumComputing result from JPMorganChase's Global Technology Applied Research, titled “Fast Convex Optimization with Quantum Gradient Descent,” which has just appeared on arXiv! Convex #optimization is a fundamental subroutine in #MachineLearning, engineering, and #DataScience, with many applications in financial engineering. We develop new #QuantumAlgorithms in the “derivative-free” setting where the algorithm only uses the function value and not its gradient. We show that #quantum algorithms without gradient access can match the convergence of classical gradient-descent methods, which do assume gradient access! In the derivative-free setting, this translates to an exponential speedup in terms of the dimension.   Our results also have applications outside the black-box setting. By leveraging a connection between semi-definite programming and eigenvalue optimization, we develop algorithms that exhibit the best known quantum or classical runtimes for semi-definite programming, linear programming, and zero-sum games, which are the three most well-studied classes of structured convex optimization problems. These classes model many practical problems of interest, including portfolio optimization and least-squares regression problems. Coauthors: Brandon Augustino, Dylan Herman, Enrico Fontana, Junhyung Lyle Kim, Jacob Watkins, Shouvanik Chakrabarti, and Marco Pistoia. Link to the article: https://lnkd.in/eMtqXM-r

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 new paper, now published in Nature Computational Science, introduces "Quantum Approximate Multi-Objective Optimization," a breakthrough from researchers at IBM, Los Alamos National Laboratory, and Zuse Institute Berlin. This work represents one of the most promising proposals for near-term demonstrations of quantum advantage in combinatorial optimization, with enormous relevance across industry and science: https://lnkd.in/ew7Pe2K5 Multi-objective optimization is a branch of mathematical optimization that deals with problems involving multiple often conflicting goals—e.g., constructing financial portfolios that minimize risk while maximizing returns. These problems can be extremely challenging for classical methods as the number of objective functions increases, even in cases where the single-objective version of the problem is easily solvable. The study demonstrates how quantum computers can approximate the optimal Pareto front, i.e., the set of all optimal trade-offs between conflicting objectives, showing better scaling than classical algorithms. Sampling good solutions from vast solution spaces is a task at which quantum computers excel, and the researchers take full advantage of that in their work. This marks an important step toward practical quantum advantage in optimization, and shows the value of exploring quantum capabilities beyond conventional problem classes. The paper is the latest outcome from our quantum optimization technical working group, and I encourage you to have a look.

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.

⚛️ Quantum Computing – Strategic Recommendations for the Industry 📜 This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior. ℹ️ Erdman et al - 2026

As reported by World Economic Forum, #quantumcomputing is emerging as a transformative solution for #energy forecasting and optimization, addressing the growing complexities of renewable energy integration and evolving consumption patterns. Traditional computing struggles to manage the variability of #solar and #wind energy, coupled with the unpredictability of rising electrification from #electricvehicles and smart appliances. These challenges require advanced computational capabilities to balance supply and demand effectively. Quantum computing leverages qubits, which process vast datasets simultaneously, enabling highly accurate energy forecasting. By incorporating weather patterns, historical usage data, and grid conditions, quantum algorithms enhance predictions, allowing energy providers to better anticipate fluctuations in renewable generation and align energy distribution with demand. This reduces inefficiencies, minimizes energy waste, and ensures a stable power supply. Beyond forecasting, quantum computing optimizes power grid operations by identifying potential bottlenecks, improving load balancing, and enabling real-time grid management. This results in a more resilient and adaptive energy infrastructure. Additionally, quantum computing enhances energy storage efficiency and demand-response strategies by determining the best times to charge and discharge energy, ensuring alignment with grid conditions. Practical applications are already demonstrating the benefits of quantum computing, from optimizing renewable integration to improving electric vehicle charging schedules. As the #technology advances, it will play an increasingly critical role in shaping the future of energy management. By offering real-time optimization, increased efficiency, and more sustainable energy solutions, quantum computing is set to revolutionize the #global #energy sector, ensuring a cleaner, more resilient, and reliable energy #ecosystem.

Google's quantum computer achieved a measurable advantage over classical computers for molecular analysis. Their Quantum Echoes algorithm represents progress toward practical quantum computing applications in chemistry and materials science. The research details: ↳ Published in Nature with peer review ↳ 13,000x performance improvement on specific calculations ↳ Tested on molecules with 15 and 28 atoms ↳ Results verified against established Nuclear Magnetic Resonance data The algorithm functions as a "molecular ruler" that can measure atomic distances and interactions. It uses quantum interference effects to amplify measurement signals, providing sensitivity that classical computers struggle to achieve efficiently. Current applications being explored include: ↳ Drug development for understanding molecular binding ↳ Materials research for battery and polymer characterization   ↳ Chemical analysis for determining molecular structures ↳ Nuclear Magnetic Resonance enhancement for laboratory use Google worked with UC Berkeley to validate the approach. The quantum computer analyzed molecular structures and provided information that traditional methods either missed or required significantly more computational time to obtain. The research addresses a practical problem in computational chemistry where molecular modeling requires substantial computing resources. Quantum computers may offer efficiency advantages for these specific types of calculations. This work follows Google's established quantum computing research program, building on their previous demonstrations of quantum error correction and computational complexity advantages. Which scientific fields do you think will adopt quantum-enhanced analysis methods first? ♻️ Share this to inspire someone. ➕ Follow me to stay in touch.