Real-World Applications of Quantum Circuits

Many of you will have seen the news about HSBC’s world-first application of quantum computing in algorithmic bond trading. Today, I’d like to highlight the technical paper that explains the research behind this milestone. In collaboration with IBM, our teams investigated how quantum feature maps can enhance statistical learning methods for predicting the likelihood that a trade is filled at a quoted price in the European corporate bond market. Using production-scale, real trading data, we ran quantum circuits on IBM quantum computers to generate transformed data representations. These were then used as inputs to established models including logistic regression, gradient boosting, random forest, and neural networks. The results: • Up to 34% improvement in predictive performance over classical baselines. • Demonstrated on real, production-scale trading data, not synthetic datasets. • Evidence that quantum-enhanced feature representations can capture complex market patterns beyond those typically learned by classical-only methods. This marks the first known application of quantum-enhanced statistical learning in algorithmic trading. For full technical details please see our published paper: 📄 Technical paper: https://lnkd.in/eKBqs3Y7 📰 Press release: https://lnkd.in/euMRbbJG Congratulations to Philip Intallura Ph.D , Joshua Freeland Freeland and all HSBC colleagues involved — and huge thanks to IBM for their partnership.

One-Atom Quantum Computer Simulates Molecular Reactions with Unprecedented Efficiency Introduction: A Quantum Breakthrough in Chemistry Simulation A research team has successfully used a one-atom quantum computer to simulate how real molecules evolve over time after absorbing light—something that has long challenged classical computing. Published in the Journal of the American Chemical Society, this study represents a milestone in quantum chemistry and demonstrates a method that’s reportedly a million times more efficient than conventional quantum simulation techniques. Key Innovations and Findings: 1. Simulating Molecular Change, Not Just Static Properties • Traditional quantum computers have so far only been used to calculate static molecular properties—like energy levels or bond strengths. • This new method allows for dynamic simulations: modeling how molecules respond to light, including electron excitation, atomic vibration, and bond reshuffling—processes critical to photosynthesis, solar cells, and photomedicine. 2. Trapped Ion Technology • The researchers used a trapped calcium ion, essentially a one-atom quantum processor, as their simulation platform. • By manipulating the ion’s quantum state, they recreated the time-evolution of molecular systems at femtosecond (quadrillionth of a second) resolution—matching the timescales of real photochemical reactions. 3. Radical Leap in Efficiency • The study claims a million-fold increase in resource efficiency compared to standard quantum simulation techniques. • This was achieved through a novel algorithmic approach that minimizes the quantum operations needed to model time-dependent processes. 4. Real-World Applications Simulated • The team successfully modeled specific molecular transformations triggered by light, a foundational step for future advances in: • Drug development • Solar energy design • Photodynamic cancer therapies • DNA damage mitigation research Why This Matters: A New Quantum Era in Chemistry • Understanding photochemical dynamics is central to both biological function and energy technologies, yet has been computationally intractable—until now. • This study shows that even ultra-small quantum systems can tackle complex, real-world problems, provided the algorithms are smart enough. • It suggests a future where chemical simulation becomes routine on small, highly optimized quantum devices, long before fault-tolerant universal quantum computers arrive. Conclusion: One Atom, Big Impact By simulating the fleeting, intricate dance of molecules under light, a single-ion quantum computer has demonstrated that quantum chemistry’s future may be smaller, faster, and more accessible than expected. This research not only overcomes a major bottleneck in simulation but also signals a powerful new direction for time-resolved quantum modeling. Keith King https://lnkd.in/gHPvUttw

Quantum-centric supercomputing is a new architecture where both a classical and quantum computer are used together to investigate a computation problem. Sample-based Quantum Diagonalization (SQD) has emerged as one of the leading algorithm for this architecture and it allows the simulation of the electronic structure. It has been used to look at electronic structure of iron sulfides (https://lnkd.in/eK8jW-Wp) and water and methane dimers (https://lnkd.in/epgUJeD8) and in this work (https://lnkd.in/eqh8J96M) our team working with Lockheed Martin have explored how SQD can be used to study molecular dissociation for both open-shell ground states and closed-shell excited states across different symmetry sectors. The study uses a CH2 molecular system, which is relevant for both interstellar and combustion chemistry. The circuits used are LUCJ ansatz and are executed on quantum hardware at a scale of 52 qubits and 3000 two-qubit gates. The results for the CH2 singlet state showed close alignment with Selected Configuration Interaction (SCI) calculations, with deviations of only a few mEh, while triplet state results also maintained reasonable accuracy within a few mEh at equilibrium. This work also marks the first SQD analysis of quantum phase transitions resulting from level crossings, expanding SQD’s applicability to new quantum phenomena. While there is still a lot of fundamental research to be done, given these results we can see a future in modeling larger radicals, transient species, and complex combustion reactions which will have Implications to the aerospace industry and beyond. If you want to get started with SQD check out https://lnkd.in/e6TuS5AZ.

Interesting approach alert! QUBO-based SVM tested on QPU (Neutral Atoms). A recent study, "QUBO-based SVM for credit card fraud detection on a real QPU," explores the application of a novel quantum approach to a critical cybersecurity challenge: credit card fraud detection. Here are some of the key findings: * QUBO-based SVM model: The study successfully implemented a Support Vector Machine (SVM) model whose training is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach could leverage the capabilities of quantum processors. * Performance: The results demonstrate that a version of the QUBO SVM model, particularly when used in a stacked ensemble configuration, achieves high performance with low error rates. The stacked configuration uses the QUBO SVM as a meta-model, trained on the outputs of other models. * Noise robustness: Surprisingly, the study observed that a certain amount of noise can lead to enhanced results. This is a new phenomenon in quantum machine learning, but it has been seen in other contexts. The models were robust to noise both in simulations and on the real QPU. * Scalability: Experiments were extended up to 24 atoms on the real QPU, and the study showed that performance increases as the size of the training set increases. This suggests that even better results are possible with larger QPUs. Practical implications: This research highlights the potential of quantum machine learning for real-world applications, using a hybrid approach where the training is performed on a QPU and the testing on classical hardware. This approach makes the model applicable on current NISQ devices. The model is also advantageous because it uses the QPU only for training, reducing costs and allowing the trained model to be reused. * Ideal for cybersecurity and regulatory issues: The study also observed that the model preserves data privacy because only the atomic coordinates and laser parameters reach the QPU, and the model test is done locally. Here the article: https://lnkd.in/d5Vfhq2G #quantumcomputing #machinelearning #cybersecurity #frauddetection #neutralatoms #QPU #NISQ #quantumml #fintech #datascience

Over the years, quantum computing has been judged mostly by its limitations — especially the gap between what today’s hardware can achieve and what classical algorithms can simulate. But the truth is more subtle and more exciting: the classical tools we rely on to simulate accurately quantum systems, like chemical compounds and materials, also have deep, well-known limitations. At Algorithmiq, we have been exploring how to turn this tension into something useful: a way to design and control information flow in artificial quantum materials, and to map out where classical methods begin to break while quantum methods provide reliable information. Why does this matter beyond physics? Because these simulations lies at the heart of the key industries driving the next decade: - catalytic processes for decarbonisation, - solid-state battery interfaces, - complex energy materials, - high-coherence quantum devices, - and next-generation computational chemistry. The challenge is that classical simulation becomes unreliable in precisely the regimes where these systems become most interesting — where disorder, interference, and entanglement govern their behaviour. We show that by pushing both quantum processors and classical algorithms into these hard regimes, we are beginning to see how quantum hardware can reveal properties impossible to discover with classical methods. Our initial evidence of quantum advantage for a useful use case is not just a scientific milestone — it is the early evidence of a technology crossing into real-world relevance. And challenges matter. They inspire people, create accountability, and accelerate progress. This is why I believe the Quantum Advantage Tracker, launched yesterday together with IBM Quantum, represents a turning point. It introduces the transparency, verification, and community benchmarking that every emerging technology needs to mature — and that investors rightly expect before deploying large-scale capital. We have published a detailed technical blog post explaining why information-flow modeling in artificial materials may become one of quantum computing’s most powerful use cases. 🔗 Link in the comments #QuantumComputing #QuantumAdvantage #InvestingInScience #DeepTech #MaterialsInnovation #Benchmarking #QDC2025 #QuantumMaterials #OpenScience

Can we find hidden tunnels using quantum computers? For our quantum computing final project, my team and I decided to find out. Detecting subsurface structures, such as tunnels, aquifers, or voids, is impossible using classical methods, as classical gravimeters are plagued by vibrations, tilt, and drift. That's where Akshat, Aakrisht, Sahana, Landon, and I's Physics 19N final project, GraviQ: Simulating Subsurface Mapping with a Qubit-Based Gravimeter, comes in. By simulating an "hourglass" configuration of two atom clouds, we can measure the vertical gravity gradient (Gzzs) while canceling out the environmental noise. We built our procedure in three steps: 1) We generated 2D density grids representing rock, ore, tunnels, and caves to create synthetic environments. 2) We used Qiskit, a quantum simulator to model a Ramsey interferometer. We mapped subsurface density to qubit phase shifts, simulating the behavior of a real quantum sensor (including decoherence and sampling noise). 3) We fed the resulting Gzz maps into a U-Net machine learning segmentation model. The tentative results are notable. Despite the simulated noise, our model achieved ~95% accuracy in detecting tunnel presence and a Dice score of up to 0.85 for localization. We believe if we can replicate this in real life, the applications are far-reaching in fields ranging from civil engineering and infrastructure, to mineral extraction, to even space exploration. Here are links to our code and slides: GitHub: https://lnkd.in/eRUYWvj6 Slides: https://lnkd.in/eeBv-F5h Huge thanks to my teammates Akshat Kannan, Aakrisht Mehra, Sahana, and Landon Moceri, and Professor Hari Manoharan for the guidance and discussions along the way. Happy to chat with anyone interested in or working on quantum sensing or related research!

In finance, Monte Carlo simulations help us to measure risks like VaR or price derivatives, but they’re often painfully slow because you need to generate millions of scenarios. Matsakos and Nield suggest something different: they build everything directly into a quantum circuit. Instead of precomputing probability distributions classically, they simulate the future evolution of equity, interest rate, and credit variables inside the quantum computer, including binomial trees for stock prices, models for rates, and credit migration or default models. All that is done within the circuit, and then quantum amplitude estimation is used to extract risk metrics without any offline preprocessing. This means you keep the quadratic speedup of quantum MC while also removing the bottleneck of classical distribution generation. If you want to explore the topic further, here is the paper: https://lnkd.in/dMHeAGnS #physics #markets #physicsinfinance #derivativespricing #quant #montecarlo #simulation #finance #quantitativefinance #financialengineering #modeling #quantum

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

Very proud to share that D-Wave is the first to demonstrate #quantum computational supremacy on a useful, real-world problem with relevance to materials discovery.   In a new peer-reviewed paper published in Science, research shows that D-Wave’s quantum computer performed the most complex magnetic materials simulation problem in the study in minutes and with a level of accuracy that would take nearly one million years using the Frontier supercomputer at Oak Ridge National Laboratory —and more than the world’s annual electricity consumption.   The broader quantum computing research and development community is collectively building an understanding of the types of computations for which quantum computing can overtake classical computing. This work is an important step toward sharpening that understanding with clear evidence of where our quantum computer was able to outperform classical methods. Learn more: https://lnkd.in/gKEGcsKP