Applications for Early-Stage Quantum Systems

I’m excited to share this new work from our IBM Quantum team in collaboration with Oak Ridge National Laboratory. This is a major demonstration of what we mean by realizing useful Quantum-centric supercomputing. Building on the chemistry work developed with RIKEN (https://lnkd.in/eK8jW-Wp) last year, and the previous Krylov demonstration with University of Tokyo (https://lnkd.in/eae_8zGc), the IBM Quantum and ORNL teams developed a quantum algorithm for ground states with convergence guarantees similar to phase estimation, while retaining the error mitigation aspect of sample-based methods. Putting together sample-based approaches and Krylov methods, we call this sample-based Krylov quantum diagonalization (SKQD). The algorithm can be used to compute ground state energies of quantum systems for many lattice models relevant in materials science and high-energy physics. SKQD is demonstrated experimentally on 85 qubits and 6,000 two-qubit gates on IBM quantum processors, against the ground state of the Anderson impurity model, obtaining high accuracies for problem sizes beyond the reach of exact diagonalization. This marks one of the largest implementations of quantum diagonalization to date, and points at how quantum computing, combined with classical computation in quantum-centric supercomputing environments, will enable us to push beyond classical methods for interesting applications. These new results also show again how algorithmic discovery is essential, especially for quantum-centric supercomputing architectures. Classical algorithms for materials science have made an impressive progress in the last decades. However, by thinking of quantum-classical workflows where quantum can deliver a value that cannot be matched by classical, we will move closer to demonstrating quantum advantage. Congratulations again to the team on this achievement. Check out the paper here: https://lnkd.in/epwCrG5R.

Google’s 69-Qubit Quantum Simulator Outperforms Supercomputers in Key Calculations Researchers from Google and the PSI Center for Scientific Computing have developed a 69-qubit quantum simulator that can outperform the fastest classical supercomputers in studying complex quantum systems. This breakthrough brings unprecedented accuracy in modeling quantum processes, unlocking new possibilities in materials science, magnetism, and thermodynamics. Key Features of Google’s Quantum Simulator • Combines Digital & Analog Quantum Computing: The simulator supports both universal quantum gates (digital mode) and high-fidelity analog evolution, providing superior performance in cross-entropy benchmarking experiments. • Beyond Classical Computational Limits: This hybrid approach enables calculations that classical supercomputers cannot efficiently simulate, especially in quantum material and energy research. • Specialized for Quantum Simulations: Unlike general-purpose quantum computers, this simulator is optimized for modeling quantum interactions, making it a powerful tool for scientific discovery. Digital vs. Analog Quantum Computing • Digital Quantum Computing: • Uses quantum gates to manipulate qubits, similar to logic gates in classical computing. • Best suited for algorithms, machine learning, and cryptography applications. • Analog Quantum Computing: • Models physical quantum systems directly, simulating real-world interactions with fewer computational steps. • Ideal for studying material science, condensed matter physics, and quantum thermodynamics. Why This Matters • Accelerating Scientific Research: The simulator could help discover new materials, improve energy storage, and refine magnetism-based technologies. • Advancing Quantum Supremacy: By achieving results beyond classical computation, this simulator cements Google’s lead in quantum research. • Potential for Quantum AI Integration: Combining digital and analog approaches may enhance machine learning models and optimize large-scale computations. What’s Next? • Expanding Qubit Count: Google may scale up its hybrid quantum simulations, pushing closer to full-scale quantum supremacy. • Exploring More Applications: Future research could apply these simulations to biophysics, drug discovery, and nuclear physics. • Potential Industry Collaborations: Google’s breakthrough may lead to partnerships in materials engineering and quantum-enhanced AI systems. This 69-qubit quantum simulator represents a major leap in computational power, proving that quantum systems can now surpass supercomputers in specialized scientific tasks, bringing us closer to practical quantum applications.

Is Quantum Machine Learning (QML) Closer Than We Think? Select areas within quantum computing are beginning to shift from long-term aspiration to practical impact. One of the most promising developments is Quantum Machine Learning, where early pilots are uncovering advantages that classical systems are unable to match. 🔷 The Quantum Advantage: Quantum computers operate on qubits, which can represent multiple states simultaneously. This enables them to process complex, interdependent variables at a scale and speed that classical machines cannot. While current hardware still faces limitations, consistent progress in simulation and optimization is confirming the technology’s potential. 🔷 Why QML Matters: QML combines quantum circuits with classical models to unlock performance improvements in targeted, data-intensive domains. Early-stage experimentation is already showing promise: • Accelerated training for complex models • More effective handling of high-dimensional and sparse datasets • Greater accuracy with smaller sample sizes 🔷 The Timeline Is Shortening: Quantum systems are inherently probabilistic, aligning well with generative AI and modeling under uncertainty. Just as classical computing advanced despite hardware imperfections, current-generation quantum systems are producing measurable results in narrow but high-value use cases. As these outcomes become more consistent, enterprise adoption will follow. 🔷 What Enterprises Can Do Today: Quantum hardware does not need to be perfect for companies to begin exploring value. Practical entry points include: • Simulating rare or complex risk scenarios in finance and operations • Using quantum inspired sampling for better forecasting and sensitivity analysis • Generating synthetic datasets in regulated or data scarce environments • Targeting challenges where classical AI struggles, such as subtle anomalies or low signal environments • Exploring use cases in fraud detection, claims forecasting, patient risk stratification, drug efficacy modeling, and portfolio optimization 🔷 Final Thought: Quantum Machine Learning is no longer confined to research. It is becoming a tool with real strategic potential. Organizations that begin investing in awareness, experimentation, and talent today will be better positioned to lead as the ecosystem matures. #QuantumMachineLearning #QuantumComputing #AI

#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

⚛️ 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

Researchers at INRS have developed a synthetic photonic lattice capable of generating and manipulating quantum states of light, paving the way for promising advancements in applications ranging from quantum computing to secure quantum communication protocols. A study co-directed by Professor Roberto Morandotti of Institut national de la recherche scientifique (INRS) in collaboration with teams from Germany, Italy, and Japan paves the way for innovative solutions that could enable the development of a system to process quantum information with both simplicity and power. Their work, just published in the journal Nature Photonics, presents a method for manipulating the photonic states of light in a never-before-seen way, offering greater control over the evolution of photon propagation. This control makes it possible to improve the detection and number of photon coincidences, as well as the efficiency of the system. Central to the research team’s experiments is the concept of quantum walks. “The development of the field of quantum computing, which began some twenty years ago, has benefited greatly from the notion of quantum walks, which are known to increase the speed and complexity of computer algorithms,” explains Professor Roberto Morandotti, whose laboratory is based at the INRS Énergie Matériaux Télécommunications Research Centre. Recently, the scientific community developed another concept: synthetic photonic networks. “This work enables us to use the concept of synthetic photonics dimensions to explore many quantum phenomena at the fundamental level, and to apply them to quantum technologies,” explains Stefania Sciara, a post-doc on Roberto Morandotti’s team and co-author of the study. The potential of this type of lattice was already known, for example, to simulate effects such as parity-time symmetry, superfluidity of light, and topological structures, but using conventional technology. “But despite their potential,” she adds, “a synthetic photonic lattice capable of handling quantum states had never been demonstrated.” This is precisely what Roberto Morandotti and his team have done. They have discovered a temporal synthetic photonic lattice capable of generating and manipulating quantum states of light (photons), using the concept of quantum walks in simple fiber systems. #quantum #communications #light #lattice #fibreoptics #photonics #breakthrough https://lnkd.in/gbtC_zRj

Is this the first real-world use case for quantum computers? True randomness is hard to come by. And in a world where cryptography and fairness rely on it, “close enough” just doesn’t cut it. A new paper in Nature claims to present a demonstrated, certified application of quantum computing, not in theory or simulation, but in the real world. Led by Quantinuum, JPMorganChase, Argonne National Laboratory, Oak Ridge National Laboratory, and The University of Texas at Austin, the team successfully ran a certified randomness expansion protocol on Quantinuum’s 56-qubit H2 quantum computer, and validated the results using over 1.1 exaflops of classical computing power. TL;DR is certified randomness--the kind of true, verifiable unpredictability that’s essential to cryptography and security--was generated by a quantum computer and validated by the world’s fastest supercomputers. Here’s why that matters: True randomness is anything but trivial. Classical systems can simulate randomness, but they’re still deterministic at the core. And for high-stakes environments such as finance, national security, or fairness in elections, you don’t want pseudo-anything. You want cold, hard entropy that no adversary can predict or reproduce. Quantum mechanics is probabilistic by nature. But just generating randomness with a quantum system isn’t enough; you need to certify that it’s truly random and not spoofed. That’s where this experiment comes in. Using a method called random circuit sampling, the team: ⚇ sent quantum circuits to Quantinuum’s 56-qubit H2 processor, ⚇ had it return outputs fast enough to make classical simulation infeasible, ⚇ verified the randomness mathematically using the Frontier supercomputer ⚇ while the quantum device accessed remotely, proving a future where secure, certifiable entropy doesn’t require trusting the hardware in front of you The result? Over 71,000 certifiably random bits generated in a way that proves they couldn’t have come from a classical machine. And it’s commercially viable. Certified randomness may sound niche—but it’s highly relevant to modern cryptography. This could be the start of the earliest true “quantum advantage” that actually matters in practice. And later this year, Quantinuum plans to make it a product. It’s a shift— from demos to deployment from supremacy claims to measurable utility from the theoretical to the trustworthy read more from Matt Swayne at The Quantum Insider here --> https://lnkd.in/gdkGMVRb peer-reviewed paper --> https://lnkd.in/g96FK7ip #QuantumComputing #CertifiedRandomness #Cryptography