Quantum Computing Solutions for Complex Problem Classes

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

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

⚛️ 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

#QuantumTuesday What if the key to unlocking quantum computing's full potential lies not in brute force but in elegant simplicity? As the GoTo Fractional Quantum Chief Intellectual Property Officer, I constantly explore the intersection of innovation, strategy, and disruptive technologies. Today, I’m thrilled to share insights from an extraordinary paper: "Tensor Quantum Programming" by A. Termanova et al. This work brilliantly merges tensor networks (TNs) and quantum computing, opening doors to solving some of the most complex computational problems of our time. Imagine tackling partial differential equations, quantum chemistry simulations, or machine learning models not with overwhelming computational resources but by leveraging tensor efficiency and the unique strengths of quantum circuits. This hybrid approach - classical for simplicity, quantum for complexity - redefines the rules of computation. Key takeaways from this breakthrough: 🔑 Efficiency Redefined: TNs are mapped to quantum circuits, creating a paradigm where high-dimensional problems scale linearly in complexity. Yes, you read that right - linear scalability in quantum circuits for problems that traditionally overwhelmed classical systems. 🔑 Applications Everywhere: - Simulating Hamiltonians for quantum systems. - Optimizing black-box functions with precision. - Revolutionizing quantum chemistry, from molecular dynamics to electron correlations. - Enhancing machine learning models by encoding TN architectures directly onto quantum platforms. 🔑 The Future Is Here: By bridging the gap between classical and quantum resources, Tensor Quantum Programming paves the way for solving real-world problems, from innovation-driven industries to fundamental research. This paper highlights an important truth: quantum computing isn't about doing more of the same; it’s about doing what was previously impossible. For those of us in the business of strategy and intellectual property, such breakthroughs represent not just scientific progress but entirely new frontiers for value creation. As an IP Alchemist, this inspires me to think about how we can protect and leverage these innovations to shape industries and fuel growth. How do we ensure that the architectures we build today are not just protected but optimized for tomorrow’s quantum future? What are your thoughts on the role of hybrid approaches like this in quantum computing? Let’s connect and dive into the possibilities. 🚀 #QuantumComputing #TensorNetworks #InnovationStrategy #IPManagement #DeepTechDisruption Terra Quantum AG Markus Pflitsch Artem Melnikov Aleksandr Berezutskii Roman Ellerbrock Michael Perelshtein

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

I've been tackling the "barren plateaus" problem in QML, where training stalls inside vast search spaces. My latest experiment in fraud detection revealed a fascinating, counterintuitive solution. I discovered that increasing my quantum circuit's entanglement didn't smooth the path to a solution, but it created a more complex and rugged loss landscape (using a dressed quantum circuit scheme). Taking advantage of the hyvis library, I visualized this effect (thanks to the colleagues of JoS QUANTUM for putting this together), as shown in the first image of the post. The landscape evolves from a simple valley to a rich, expressive terrain (but potentially more complex for an optimizer). But did this complexity hurt performance? Usually that should be the case, but the exact opposite happened. The image shows the model with the most complex landscape (8 CNOTs by layer) not only learned faster (lower loss) but also achieved the highest accuracy (AUC) on the validation set and later in the test set. There is no free lunch on this. We can't generalize from these examples. This added complexity, or "expressivity," is precisely what allowed the model to find a superior solution in this case and avoid getting stuck, but it is not the norm. My biggest conclusion here It seems that for QML, the key to real-world performance isn't avoiding complexity, but leveraging it. To be able to extract permanent benefits, we should follow approaches like what Dra. Eva Andres Nuñez is researching by finding the way to use the extra complexity of entanglement to be able to find the global minima and not get stuck in our quantum optimization procedures using the theory behind SNNs. Here details about the hyvis library in GitHub: https://lnkd.in/dzqcFvDE An insightful paper from Eva about mixing SNNs and quantum: https://lnkd.in/dXDiuCBH Same subject from Jiechen Chen: https://lnkd.in/d-Uyngef #quantumcomputing #machinelearning #ai #datascience #frauddetection #ml #qml

🌟 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

🚀 New research from Amazon Quantum Solutions Lab addressing hard combinatorial optimization problems using algorithms well-suited to quantum computers. In this blog, the team takes a look at a quantum-guided cluster algorithm (QGCA) to addresses a key limitation in traditional approaches of getting trapped in local minima when solving complex combinatorial problems. By utilizing low-energy correlations, they enable collective moves that remain effective even in highly constrained and frustrated settings, where standard methods struggle. The approach is relevant for scheduling, routing, portfolio optimization, and network design problems where constraint satisfaction is challenging. ✍ Nice work by Peter Eder, Aron Kerschbaumer, Christian Mendl, Jernej Rudi Finžgar, Helmut G. Katzgraber, Martin Schuetz, Raimel A. Medina, and Sarah Braun #QuantumComputing #Optimization #Research #AWS https://lnkd.in/gchXgHgs

🚨 Quantum Computing Breakthrough in Finance 🚨 HSBC just announced a world-first. By using IBM’s Heron quantum processor, the bank achieved a 34% improvement in predicting bond trading probabilities. This marks the first time a bank has applied quantum computing to real financial trading data at scale, moving beyond theory and into production-level application. Some are calling this a “Sputnik moment” for quantum. That is not a perfect analogy, given the geopolitical nature of Sputnik and the corporate implications of HSBC's use of quantum computing. But I am not surprised to see a big leap forward for quantum in the world of finance. In fact, when I wrote Quantum: Computing Nouveau back in 2018, I predicted this exact trajectory: that quantum would move from academic labs to financial markets and other industries where optimization, forecasting, and massive data challenges are prevalent. In my 2018 book, I outlined - Why finance would be among the earliest adopters of quantum, thanks to its reliance on complex risk management, forecasting, and trading models. - How quantum computing could deliver step-change improvements in processing power, solving problems classical computing struggles and corporate NP problems. In computer science, NP (nondeterministic polynomial-time) problems are problems where it’s easy to verify a solution once you have it, but extremely hard to calculate the solution in the first place. - The looming arms race for quantum advantage, not only among tech companies, but also in financial services, energy, logistics, and governments. HSBC’s milestone confirms that we’re crossing the threshold from theory to practice. Quantum computing isn’t just “new math”—it’s new computing, with profound implications for markets, cybersecurity, and global competition. 🔮 Back in 2018, I wrote that quantum computing is not just optional. It is a conditio sine qua non for the future of finance and data-driven industries. Today, we’re watching that future unfold. #Quantum #QuantumComputing #Future #Finance https://lnkd.in/gMNc2M9b