HQS Quantum Simulations

𝗟𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝘂𝗽 𝘆𝗼𝘂𝗿 𝘀𝗰𝗿𝗲𝗲𝗻 𝗮𝗻𝗱 𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗯 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗲𝗹𝗲𝗰𝘁𝗿𝗼𝗻 𝘀𝗽𝗲𝗰𝘁𝗿𝗼𝘀𝗰𝗼𝗽𝘆 𝗶𝘀 𝘂𝘀𝗲𝗱 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗶𝗻𝗲𝘀. As for the quantum simulation use case NMR, we've already provided you with a lot of information. However, this is just one example of how you can benefit from quantum mechanics. Now, let's discuss the quantum simulation use case of electron spectroscopy. In this short video, Vladimir Rybkin (Team Leader of the Ab Initio Spectroscopy Team at HQS) explains how visible light in LEDs and PHOLEDs comes from electrons forming excited singlet and triplet states—and why the energy gaps that govern color and efficiency matter so much. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 Electron spectroscopy measures and explains these excited states and their gaps. Many emitter molecules are hard to model with standard methods. They can show strong electron correlation and important spin effects. Quantum simulation is designed to handle these challenges. It can predict gaps and spectra. It can guide material choice before synthesis and testing. It can speed up research and development. 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗼 𝗮𝘁 𝗛𝗤𝗦 We combine powerful classical solvers and quantum ready workflows. Our Active Space Finder helps pick the most important orbitals for ground and excited states. This makes spectroscopy focused simulations more robust and faster. Watch the video with Vladimir to see how electron spectroscopy meets quantum simulation in display-grade photomaterials—and let’s talk about your pipeline. #ElectronSpectroscopy #Spectroscopy #QuantumApplication #QuantumSimulation

Since we are now all hyped about about using quantum computers to solve NMR problems, it is probably a good time to talk about our preprint “Can a Quantum Computer Simulate Nuclear Magnetic Resonance Spectra Better than a Classical One?” https://lnkd.in/e9SNGDpN In contrast to the recent work of Google, we stick with standard low and high field NMR and test if there are cases for which a classical solver can not converge to the correct solution. This captures something we care deeply about at HQS: making careful, quantitative calls about when NMR problems genuinely require a quantum computer. Our approach starts from strength on the classical side. We built a powerful NMR solver with a black‑box implementation of a cluster approximation tailored to spin systems. The solver automatically forms spin clusters, exploits symmetries, exactly diagonalizes each cluster, and assembles the full spectrum. Accuracy is assessed against exact solutions where possible and through convergence towards the largest possible cluster solution. We choose (mostly) realistic parameter regimes across field strengths and line broadening. Although the regime of 20Mhz field strength and 0.1 Hz line width stretches things a little. Yet still, the result is an “out‑of‑the‑box” workflow that scales to large, chemically relevant systems while providing a trustworthy stop‑criterion rather than hope. What did we learn? For many practically relevant proton NMR settings, our solver reaches clear convergence at moderate cluster sizes and runtimes, with errors that reliably shrink as the maximum cluster size grows. We also show where and why clustering can struggle, for example in highly symmetric molecules where effective spin blocks can hide long‑range correlations. Those cases are exactly where a quantum computer might become the right tool, and our analysis makes that boundary explicit. Of course, no need to wait for the quantum computer. With our NMR solver you can get started and get fast, accurate spectra and hard numbers on convergence and cost. If the solver flags a tough regime—think extreme resolution, ultra‑low fields, or unusual symmetry—then we are happy to see how to go to quantum workflows in the future! We will of course continue our work on this issue. Our database of molecules is constantly getting bigger and we will keep checking for convergence of all of them. If you have any interesting molecule or parameter regime, do not hesitate to contact us!

Googles recent papers on NMR: Part 4/4. This is the fourth part on the recent paper dump by Google AI. When considering the practicality of measuring OTOCs in NMR, there are many possibilities of criticism. But in total I feel that the paper from Google AI do in fact represent a great advance for quantum computing. For the NMR and spectroscopy community, the pairing of a paper in which quantum advantage is shown and an application in NMR is truly noteworthy. The Nature result frames OTOCs as a verifiable, hardware‑efficient quantum task; the arXiv study illustrates how OTOCs might translate into spectroscopic observables that speak to real molecular structure. Taken together, they outline a pathway from abstract many‑body diagnostics to measurements that chemists and materials scientists care about, while leaving open important questions about scope, competitiveness with other methods and practical workflows in day to day NMR. As a company focused on rigorous, industry‑relevant NMR benchmarks, we welcome these developments. They raise the bar for how to connect quantum hardware to measurable spectroscopic quantities and create space for the community to stress‑test protocols, cross‑validate with established techniques, and map out where quantum methods can add value. This also fits very well to our expectations for quantum computing. We can see the OTOC Measurement as an abstract spin problem of academic interest which has achieved quantum advantage in 2025. And the quantity can be connected to NMR. I would say our expectation for 2025 has been achieved. Lets see what happens in 2026!

It is understandable that quantum hardware attracts a large share of funding. Building and scaling devices demands capital for fabrication, cryogenics, control electronics, and specialized facilities. But in the end, the value will be produced by applications. They determine which problems are worth solving, what accuracy is needed, and which performance targets actually move an industry needle. Here is a simple exercise: open the Quantum Algorithm Zoo and cross out entries that are only for cryptography, rely on unclear oracles, or offer merely quadratic speedups. The remaining set is smaller than many expect. That is not a pessimistic conclusion; it is a call to invest in application research so that real workflows, data models, and validation protocols catch up with the hardware. As we move toward error‑corrected machines, much more sophisticated algorithms come into play. The quantum linear equation solver family could be transformative, but it needs urgent work on practical input oracles and state preparation, on how to handle solutions represented in superposition, and on the fine print such as scaling with the condition number, sparsity, and precision. Progress here will turn theoretical speedups into deployable tools. Our own focus at HQS is a concrete example. Spin physics alone—NMR, MRI, ESR—can drive quantum computing demand for quite a while. Getting to quantum advantage in real industry applications, however, requires sustained development. We build strong classical baselines first and quantify where quantum resources would change outcomes. Our NMR solver, including a black‑box cluster approximation, lets us estimate when classical methods suffice and when a quantum path is justified, giving partners numbers rather than hype. If you are shaping budgets or roadmaps, consider pairing hardware investments with serious, long‑horizon application programs. And long horizon means: Applications with provable scaling advantage. Especially development for error corrected quantum computers needs to be clearly motivated by scaling advantages. But even more near term applications profit from sound foundations.

It is understandable that quantum hardware attracts a large share of funding. Building and scaling devices demands capital for fabrication, cryogenics, control electronics, and specialized facilities. But in the end, the value will be produced by applications. They determine which problems are worth solving, what accuracy is needed, and which performance targets actually move an industry needle. Here is a simple exercise: open the Quantum Algorithm Zoo and cross out entries that are only for cryptography, rely on unclear oracles, or offer merely quadratic speedups. The remaining set is smaller than many expect. That is not a pessimistic conclusion; it is a call to invest in application research so that real workflows, data models, and validation protocols catch up with the hardware. As we move toward error‑corrected machines, much more sophisticated algorithms come into play. The quantum linear equation solver family could be transformative, but it needs urgent work on practical input oracles and state preparation, on how to handle solutions represented in superposition, and on the fine print such as scaling with the condition number, sparsity, and precision. Progress here will turn theoretical speedups into deployable tools. Our own focus at HQS is a concrete example. Spin physics alone—NMR, MRI, ESR—can drive quantum computing demand for quite a while. Getting to quantum advantage in real industry applications, however, requires sustained development. We build strong classical baselines first and quantify where quantum resources would change outcomes. Our NMR solver, including a black‑box cluster approximation, lets us estimate when classical methods suffice and when a quantum path is justified, giving partners numbers rather than hype. If you are shaping budgets or roadmaps, consider pairing hardware investments with serious, long‑horizon application programs. And long horizon means: Applications with provable scaling advantage. Especially development for error corrected quantum computers needs to be clearly motivated by scaling advantages. But even more near term applications profit from sound foundations.

𝗕𝘂𝗶𝗹𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗶𝗿𝗰𝘂𝗶𝘁𝘀 𝘁𝗵𝗮𝘁 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗡𝗠𝗥 𝘀𝗽𝗲𝗰𝘁𝗿𝗮. We’re joining Qiskit Fall Fest 2025 at the Universität Stuttgart (Nov 3–5) — and on Nov 4 we’ll host an HQS hackathon challenge: predicting NMR spectroscopy on a quantum computer. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗱𝗼 Map a real spin system to a Hamiltonian Build and run time‑evolution circuits in Qiskit Extract correlation functions and reconstruct spectra Benchmark against a classical baseline 𝗪𝗵𝗼 𝗶𝘁’𝘀 𝗳𝗼𝗿 Intermediate/advanced participants with basic Qiskit + Python Chemists, physicists, and engineers curious about quantum-for-NMR 𝗪𝗵𝘆 𝗷𝗼𝗶𝗻 Hands‑on with real hardware access Practical skills you can reuse in research and industry part of the International Year of Quantum Science and Technology 𝗪𝗵𝗲𝗿𝗲/𝗪𝗵𝗲𝗻 📍 University of Stuttgart, Campus Vaihingen ⏰ Nov 3–5; HQS challenge on Nov 4 Register and details: https://lnkd.in/ed4jPxRk #QiskitFallFest2025 #FallFest

𝗕𝘂𝗶𝗹𝗱 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗶𝗿𝗰𝘂𝗶𝘁𝘀 𝘁𝗵𝗮𝘁 𝗽𝗿𝗲𝗱𝗶𝗰𝘁 𝗡𝗠𝗥 𝘀𝗽𝗲𝗰𝘁𝗿𝗮. We’re joining Qiskit Fall Fest 2025 at the Universität Stuttgart (Nov 3–5) — and on Nov 4 we’ll host an HQS hackathon challenge: predicting NMR spectroscopy on a quantum computer. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂’𝗹𝗹 𝗱𝗼 Map a real spin system to a Hamiltonian Build and run time‑evolution circuits in Qiskit Extract correlation functions and reconstruct spectra Benchmark against a classical baseline 𝗪𝗵𝗼 𝗶𝘁’𝘀 𝗳𝗼𝗿 Intermediate/advanced participants with basic Qiskit + Python Chemists, physicists, and engineers curious about quantum-for-NMR 𝗪𝗵𝘆 𝗷𝗼𝗶𝗻 Hands‑on with real hardware access Practical skills you can reuse in research and industry part of the International Year of Quantum Science and Technology 𝗪𝗵𝗲𝗿𝗲/𝗪𝗵𝗲𝗻 📍 University of Stuttgart, Campus Vaihingen ⏰ Nov 3–5; HQS challenge on Nov 4 Register and details: https://lnkd.in/ed4jPxRk #QiskitFallFest2025 #FallFest

𝗗𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗡𝗠𝗥 𝘀𝗽𝗲𝗰𝘁𝗿𝗮 𝗼𝗻 𝗾𝘂𝗮𝗻𝘁𝘂𝗺 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗛𝗤𝗦 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 In this video, we walk you through the complete workflow to obtain an NMR spectrum on a quantum computer. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: 🔸 Start from data: Retrieve the NMR Hamiltonian from our database. 🔸 Simplify the physics: Transform into the rotating frame to streamline spin dynamics. 🔸 Choose your hardware: Configure the target quantum device with qoqo. 🔸 Build the program: Generate the quantum circuit ready for execution. 🔸 Get results: Perform time evolution to obtain the NMR spectrum. 𝗤𝘂𝗶𝗰𝗸 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗿𝘀: 🔸 Rotating frame: By moving from the laboratory frame to a rotating frame, we simplify the evolution of spin dynamics and make simulations more efficient. 🔸 qoqo: qoqo is HQS Quantum Simulations’ toolkit for representing quantum circuits. The name stands for “Quantum Operation Quantum Operation,” using reduplication. Try it yourself: Get a free license for HQS Spectrum Tools. Register on our cloud to receive your license and start experimenting with NMR spectrum generation. Learn more about the NMR use case with HQS software: https://lnkd.in/eMF9m7RV #NMR #QuantumComputing #QuantumApplication #QuantumUseCase

Meeting old friends in new places I have been working with and in the field of decoherence theory since before starting my PhD. The Bloch-Redfield approach to describing open quantum systems in a system-bath framework was always one of my favourite models linking decoherence with microscopic origins. Although it is crucial to understand the behavior of superconducting circuits in many experiments, for practical applications semi-heuristic Lindblad noise models are often used. I have been reading more about NMR and spectroscopy methods recently and it was a nice discovery for me that Bloch-Redfield is used for practical applications in relaxometry. Of course the formulation tends to be slightly different than I am used to in condensed matter theory but the core is always a second order perturbation theory in the coupling between a system and environment where the environmental degrees of freedom are traced out as neatly described in this paper: https://lnkd.in/eHi75kw2 by Song et al. #NMR #Relaxometry #BlochRedfield