Solvable Problems in the Quantum Computing Era

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

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

What if useful quantum computing doesn’t start with chemistry or cryptography—but with long-standing problems in physics? For years, a dominant narrative has been: 𝗪𝗲 𝗻𝗲𝗲𝗱 𝗳𝘂𝗹𝗹𝘆 𝗲𝗿𝗿𝗼𝗿-𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗲𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘄𝗲 𝗰𝗮𝗻 𝗱𝗼 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗮𝘁’𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗼𝗻𝗹𝘆 𝗽𝗮𝘁𝗵? 𝗪𝗵𝗮𝘁 𝗶𝗳 𝘄𝗲 𝗰𝗮𝗻 𝘀𝗼𝗹𝘃𝗲 𝗰𝗲𝗿𝘁𝗮𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗼𝗱𝗮𝘆—without waiting for millions of physical qubits and full-blown QEC? 𝗪𝗵𝗮𝘁 𝗶𝗳 𝗵𝗶𝗴𝗵-𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲, 𝗵𝗶𝗴𝗵-𝗳𝗶𝗱𝗲𝗹𝗶𝘁𝘆 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗼𝗿𝘀 with tens or hundreds of well-behaved qubits can already unlock new insights into non-equilibrium dynamics, frustrated spin systems, or lattice gauge theories? Because here’s the thing: Many-body physics, for example the study of disordered landscapes, doesn’t always require large-scale, error-corrected quantum hardware. 𝗕𝘂𝘁 𝗶𝘁 𝗱𝗼𝗲𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲 𝗾𝘂𝗯𝗶𝘁𝘀 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝘄𝗲𝗹𝗹 𝗲𝗻𝗼𝘂𝗴𝗵—and a thoughtful choice of problems where quantum hardware can genuinely outperform classical simulations. 𝗜𝗻 𝗮 𝘄𝗮𝘆, 𝗶𝘁’𝘀 𝗮 𝗿𝗲𝘁𝘂𝗿𝗻 𝘁𝗼 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴’𝘀 𝗿𝗼𝗼𝘁𝘀: Not chasing the biggest algorithms, but focusing on the hardest-to-simulate quantum systems where even modest hardware can offer breakthroughs. 𝗧𝗵𝗲𝘀𝗲 𝗽𝗵𝘆𝘀𝗶𝗰𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗱𝗲𝘁𝗼𝘂𝗿𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 “𝗸𝗶𝗹𝗹𝗲𝗿 𝗮𝗽𝗽.” 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲𝘀 𝗼𝗻 𝘁𝗵𝗲 𝘄𝗮𝘆 𝘁𝗵𝗲𝗿𝗲—𝘀𝘁𝗿𝗲𝘀𝘀-𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗼𝘂𝗿 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗮𝗻𝗱 𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗽𝗮𝘁𝗵 𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗶𝘀 𝗿𝗲𝗮𝗹. It might be about answering questions in condensed matter and high energy physics that we’ve never been able to ask this way before. 𝗔𝗻𝗱 𝘁𝗵𝗮𝘁’𝘀 𝗮 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝘀𝗶𝗴𝗻 𝘄𝗲’𝗿𝗲 𝗵𝗲𝗮𝗱𝗶𝗻𝗴 𝗶𝗻 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻. 📸 Image Credits: Yoshioka et al. (2024)

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.

🌟 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

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

⚛️ Sequential Quantum Computing 📑 We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome the limitations of each type of quantum computer by combining their complementary strengths. Current quantum devices, including analog quantum annealers and digital quantum processors, offer distinct advantages, yet face significant practical constraints when individually used. SQC addresses this by efficient inter-processor transfer of information through bias fields. Consequently, measurement outcomes from one quantum processor are encoded in the initial-state preparation of the subsequent quantum computer. We experimentally validate SQC by solving a combinatorial optimization problem with interactions up to three-body terms. A D-Wave quantum annealer utilizing 678 qubits approximately solves the problem, and an IBM’s 156-qubit digital quantum processor subsequently refines the obtained solutions. This is possible via the digital introduction of non-stoquastic counterdiabatic terms unavailable to the analog quantum annealer. The experiment shows a substantial reduction in computational resources and improvement in the quality of the solution compared to the standalone operations of the individual quantum processors. These results highlight SQC as a powerful and versatile approach for addressing complex combinatorial optimization problems, with potential applications in quantum simulation of many-body systems, quantum chemistry, among others. ℹ️ Romero et al - 2025

We’ve all known #Moderna for some years for their role in transforming mRNA science to rapidly develop life-saving vaccines and therapeutics. But what you might not know is that behind almost every mRNA innovation lies an incredibly hard problem: figuring out how each sequence folds. Each mRNA strand can twist and loop into an astronomical number of secondary structures. Only a handful of those make sense, given the physical laws governing molecular behavior. Predicting which ones are biologically plausible? That involves solving a complex combinatorial optimization problem, which turns out to be a sweet spot for quantum computing… exactly where pure classic approaches hit a wall. So the team began creating and testing quantum novel algorithms -like CVaR VQE- and benchmarking them against classical solvers to predict mRNA folding. And the results? The Quantum-enabled pipeline is already matching classic solvers and is expected to augment beyond what’s at reach of classic computers today. ‼️ You can read all details here: https://lnkd.in/ex5gxDCn. You will learn about: 🔹 A 𝐧𝐞𝐚𝐫-𝐭𝐞𝐫𝐦 𝐪𝐮𝐚𝐧𝐭𝐮𝐦-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐛𝐢𝐨𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 🔹 Massive 𝐥𝐚𝐫𝐠𝐞 𝐬𝐜𝐚𝐥𝐞: last year, we ran the largest variational quantum algorithm yet -80 qubits modeling 60-nucleotide mRNA strands (and targeting this year 156 qubits and 950-gate circuits) 🔹A 𝐜𝐥𝐞𝐯𝐞𝐫 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐛𝐨𝐨𝐬𝐭: adding a Conditional Value at Risk (CVaR) lightweight classical post-processing step, to reduce the sensitivity to noisy outliers. 🔹 𝐑𝐞𝐜𝐨𝐫𝐝-𝐦𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: the quantum-enhanced simulations are now reaching the same quality as top classical solvers and aiming at going beyond, proving what a powerful platform Quantum Computing is for Science. To me, this case study perfectly shows 2 vectors we are fully committed at IBM: 1. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦-𝐜𝐥𝐚𝐬𝐬𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: the future of computing is going to be full of these hybrid approaches aiming at combining the most efficient use of quantum and classical resources in a 𝐣𝐨𝐢𝐧𝐭 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐡𝐢𝐠𝐡-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 (𝐇𝐏𝐂) 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭. 2. 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: when you represent your problem in mathematical terms, abstracting from the domain, it is much easier to borrow ideas from other domains and boost innovation (probably you know that CVaR or Conditional Value at Risk comes from finance). IBM IBM Research IBM Quantum #innovationthatmatters #Science #FutureOfComputing

⚛️ Two quantum breakthroughs this week just moved us significantly closer to practical quantum computers that could solve real-world problems. Alice & Bob in Paris achieved something remarkable: their "Galvanic Cat" qubits can now resist errors for over an hour - that's millions of times longer than standard qubits that typically last only microseconds. This solves quantum computing's biggest challenge: keeping information stable long enough to perform meaningful calculations. Meanwhile, Caltech physicists assembled the largest qubit array ever built: 6,100 neutral atoms trapped by 12,000 laser "optical tweezers" with 99.98% accuracy. Think of it as building a quantum city where every atom is perfectly positioned and controlled. 🏗️ Here's why this matters for every industry: 💊 Pharmaceutical companies could simulate molecular interactions in hours instead of years, accelerating drug discovery 🔋 Materials scientists could design better batteries and solar panels by understanding quantum behavior 🧬 Medical researchers could unlock new treatments by modeling complex biological systems 🏦 Financial institutions could optimize portfolios and detect fraud with unprecedented precision These cat qubits could reduce quantum computer hardware requirements by up to 200 times compared to competing approaches - making quantum computers not just more powerful, but dramatically cheaper and more accessible. 💰 The actionable insight: Start preparing your teams now. Companies that understand quantum applications in their field will have a massive competitive advantage when these systems become commercially available in the next 5-7 years. What quantum applications could transform your industry? Share your thoughts below! 👇 https://lnkd.in/ea4p9Sby https://lnkd.in/e8Urf97w

Harvard University researchers have achieved fault-tolerant universal quantum computation using 448 neutral atoms, marking a critical milestone toward scalable quantum systems This isn't just incremental progress, it's the first demonstration of all key error-correction components in one setup, paving the way for practical quantum applications that could transform AI training, drug discovery, and complex simulations Why this matters: Error Correction Breakthrough: Quantum bits (qubits) are notoriously fragile due to environmental noise; this system operates below the error threshold, allowing real-time detection and correction without halting computations, essential for building larger, reliable quantum machines Scalability Achieved: By showing that adding more qubits reduces overall errors, the team has overcome a major barrier; previous systems struggled with error accumulation, limiting size and utility Impact on AI and Beyond: Quantum computers excel at parallel processing vast datasets; this could accelerate AI model training by orders of magnitude, solving optimization problems that classical supercomputers take years to crack Room for Growth: Using laser-controlled rubidium atoms, the architecture is hardware-agnostic and could integrate with existing tech, speeding up commercialization in fields like materials science and cryptography This positions quantum tech closer to real-world deployment, potentially disrupting industries reliant on high-compute tasks. Read more here: https://lnkd.in/dxM4pQYw #QuantumComputing #AIBreakthroughs #TechInnovation #FutureOfComputing #QuantumAI