Applications of Computational Quantum Physics in Technology Innovation

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

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

For those tracking progress in Quantum… As my colleague Hartmut Neven has predicted, real-world applications possible only on quantum computers are much closer than people think – as near as five years, even though fully error corrected quantum computers may be further away.  Recently, my colleagues on our Quantum AI team at Google Research took another important step on that path with a new set of results we published last week in Nature that share a promising new approach to applications on today’s quantum computers. Our analog-digital quantum simulator using super-conducting qubits shows performance beyond the reach of classical simulations in cross-entropy benchmarking experiments. Simulations with the level of experimental fidelity in this simulator would require more than a million years on a Frontier supercomputer. The simulator brings together digital’s flexibility and control with the analog’s speed – and provides a path towards applications that cannot be accomplished on a classical computer. Along the way, my colleagues also made a scientific discovery – they observed the breakdown of a well-known prediction in non-equilibrium physics, the Kibble-Zurek mechanism - an important result in our understanding of magnetism, and also useful in various kinds of quantum simulations. Congratulations to Trond Andersen, Nikita Astrakhantsev, and the rest of the team on this exciting step – much more to come! You can read the Nature paper here: https://lnkd.in/gg2En5qe 

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

Excited to share another new #QuantumComputing result from Global Technology Applied Research at JPMorganChase.  We have justed posted a new arXiv preprint titled "On Speedups for Convex Optimization via Quantum Dynamics" (https://lnkd.in/e2sRz_my), which follows our recent work on “Fast Convex Optimization with Quantum Gradient Methods”(https://lnkd.in/eMtqXM-r). Convex optimization is a fundamental subroutine in #machinelearning, #engineering, and #datascience with many applications in #FinancialEngineering, and understanding the full potential for #quantum speedup is of great interest.   Complementing our previous research on quantum gradient methods, we now consider a natural optimization algorithm inspired by physics, namely, the simulation of a quantum particle subject to a potential defined by the objective function. Specifically, we study discrete simulations of the Quantum Hamiltonian Descent (QHD) framework (https://lnkd.in/e9xw_DDb) and establish the first rigorous query complexity bounds for this approach. Our findings reveal that, while the simulation of QHD probably does not improve upon classical algorithms for exact objective functions, it in fact offers a super-quadratic speedup over all known classical algorithms in the high-dimensional regime for noisy or stochastic convex optimization! These settings are common in machine learning, #reinforcementlearning, and #portfoliooptimization with empirically calibrated parameters. Our research highlights the potential for large quantum speedups on such problems.   Together with our previous work, this illustrates that gradient-based and dynamical methods for quantum convex optimization are complementary: with quantum gradient methods providing large speedups in the noiseless setting, and the dynamical approach providing large speedups in the noisy and stochastic setting.   Co-authors: Shouvanik Chakrabarti, Dylan Herman, Jacob Watkins, Enrico Fontana, Brandon Augustino, Junhyung Lyle Kim, and Marco Pistoia.

I just saw a quantum computer that uses light instead of electricity. And it made me wonder... ...could this be what unlocks the bioeconomy? During Dreamforce week, I attended a Techleap side event and met Dr.-Ing. Stefan Hengesbach, CEO of QuiX Quantum. He showed me their Quantum Photonic Processor. Here's why it caught my attention: The biggest bottleneck in bioeconomy isn't capital—it's computational complexity. When we design enzymes to break down agricultural waste, we're predicting how thousands of atoms interact. When we engineer microorganisms to produce materials, we're modeling billions of possible configurations. Classical computers test these sequentially. One possibility, then another, then another. Could quantum computing change that?? What makes QuiX interesting: Their processor uses photons as information carriers and operates at room temperature—far more practical than systems requiring extreme cooling. - Potential bioeconomy applications: - Protein folding for enzyme design - Molecular simulation for bio-based materials - Pathway optimization for bioprocessing Research teams are already solving protein folding problems that were computationally impossible before. But when does this move from research to applied biotech? The implications: Right now, bioeconomy startups spend months on trial and error optimization. What if we could simulate first and predict outcomes? What if we could model bio-material performance over years without waiting? I'm not saying quantum replaces lab work—biology is messy. But could it dramatically cut iteration time? The real question: Europe's building quantum infrastructure for these applications. QuiX supplies quantum computers to the German Aerospace Center. The technology is shifting from research to potential competitive advantage. For bioeconomy founders: should you be tracking this? Sometimes the best conversations happen at side events. What's your take—how soon before quantum computing impacts biotech startups? #QuantumComputing #Bioeconomy #BioTech #CircularEconomy #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.

The news out of China about their latest quantum machine achieving a task in minutes that would take the world’s most powerful supercomputers an estimated 2.6 billion years to complete is truly mind-bending. This is the technical and conceptual leap known as Quantum Computational Advantage (often incorrectly called 'quantum supremacy'). Why is this so significant? The Qubit Advantage: Classical computers operate on bits of 0s or 1s. Quantum machines use qubits, which leverage the quantum states of superposition and entanglement, allowing them to exist as 0, 1, and both simultaneously. This capability enables an exponential increase in processing power for specific, complex problems. Shattered Limits: The task solved (likely a highly complex Boson Sampling or Random Circuit Sampling problem, as seen with previous Chinese machines like Jiuzhang and Zuchongzhi-3) demonstrates that for certain computational challenges, the age of classical computation is already reaching its practical limit. Real-World Impact: This speed unlocks a future previously confined to science fiction: Drug Discovery: Simulating entire molecules for new medicines with atomic precision. Materials Science: Designing revolutionary new materials from the ground up. Cryptography: Potentially breaking current encryption standards, demanding the immediate development of Post-Quantum Cryptography (PQC) solutions. This isn't about running Microsoft Excel faster; it’s about solving problems that were previously classified as impossible. The quantum race is heating up, and it's no longer just a laboratory experiment. It’s a geopolitical and technological reality that will redefine industries and national security. What practical applications do you foresee making the biggest immediate impact from this kind of computational power? #QuantumComputing #TechBreakthrough #Innovation #FutureOfTech #ComputationalAdvantage #ChinaTech

Quantum Entanglement — a Theory No One Talked about ForEver — recently — Recent Developments in Quantum Entanglement Research (as of January 2026) Quantum entanglement, a cornerstone of quantum mechanics where particles remain interconnected regardless of distance, has seen significant advancements in 2025. These breakthroughs focus on practical applications like quantum computing, networking, communication, and sensing, overcoming challenges such as decoherence, scalability, and environmental constraints. Below is a summary of key research highlights from the past year, drawn from peer-reviewed studies, institutional announcements, and expert discussions. 1. Room-Temperature Quantum Entanglement for Signaling Researchers at Stanford University developed a nanoscale device that entangles photons and electrons at room temperature, eliminating the need for cryogenic cooling. Led by Jennifer Dionne and Feng Pan, the device uses a thin layer of molybdenum diselenide (MoSe₂) on nanopatterned silicon to generate "twisted light," enabling stable spin coupling between photons and electrons. This could lead to affordable quantum components for cryptography, AI, and high-speed data transmission. Similar discussions on X highlighted its potential for quantum networking, including integration with CMOS chips for long-distance entanglement distribution. 2. Discovery of a New Type of Quantum Entanglement A team from the Technion - Israel Institute of Technology identified a novel form of entanglement in the total angular momentum of photons within nanoscale structures. Published in Nature, the study by Amit Kam and Shai Tsesses shows photons entangling solely via angular momentum, expanding the quantum state space. This is the first new entanglement type in over two decades and could enable miniaturized quantum devices for communication and computing. 3. Entanglement of Atomic Nuclei for Scalable Quantum Computing At the University of New South Wales (UNSW), Andrea Morello's group achieved entanglement between phosphorus atomic nuclei in silicon chips, using electrons as intermediaries over 20-nanometer distances. This "geometric gate" approach makes nuclear spin qubits compatible with standard silicon fabrication, addressing noise and scalability issues. It paves the way for integrating reliable qubits into everyday electronics, potentially accelerating large-scale quantum computers. Related X posts noted broader quantum computing progress, including spectral gap estimation with 20 qubits.