Quantum Patterns in Real-World Applications

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.

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

Exponential quantum advantage in processing massive classical data by John Preskill https://lnkd.in/eUTvGHaX Abstract Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP = BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

Closing the gap between quantum theory and sensing reality Quantum sensing is often framed as a race for better hardware: longer coherence times, cleaner materials, improved qubit designs. All of this matters. But it is not the full story. In our latest work, published in Nature Magazine, the team at Terra Quantum AG demonstrates that algorithmic innovation alone can unlock major gains in quantum magnetometry. By redesigning phase-estimation protocols for superconducting qubits, we show how to expand the dynamical range by orders of magnitude while improving precision, without relying on entanglement or new hardware. The core insight is simple: Quantum advantage does not depend on coherence alone, but on how efficiently phase information is transformed into knowledge. Smarter algorithms extract more information from the same physical system, even under realistic noise conditions. This work brings quantum sensing closer to practical deployment. It shows that progress toward Heisenberg-limit performance can be achieved today, through software–hardware co-design, rather than waiting for ideal devices tomorrow. Quantum technologies will not scale through hardware alone. Algorithms are where quantum physics becomes real-world impact. Read the full paper here 👉 https://lnkd.in/dFpxKc-T and below 👇 #QuantumIsNow #QuantumSensing #QuantumAlgorithms #DeepTech #QuantumMetrology #SuperconductingQubits

Scientists carefully moved 48 single atoms into a perfect circle, and the ripples you see inside are not water. They are real quantum waves. This experiment is called a quantum corral. Using a scanning tunneling microscope, researchers picked up atoms one by one and placed them on a metal surface. Each atom was positioned with extreme care, forming a tiny ring that is far smaller than anything we can see with normal light. When electrons move across the surface inside this ring, they behave like waves. The circle of atoms acts like a wall, trapping those waves inside. The trapped waves reflect back and forth, creating ripple patterns in the center. These ripples are standing waves made of electrons, not water or light. The image looks simple, but it shows something deep about quantum physics. At this tiny scale, particles like electrons do not act only like solid objects. They spread out like waves and create patterns. The circle of atoms makes these patterns visible by limiting where the electrons can move. This kind of work helps scientists understand how electrons behave in materials. It also plays a role in nanotechnology, where engineers design devices at the atomic level. By controlling atoms one by one, researchers can test ideas about quantum behavior in a direct way. Seeing 48 atoms arranged by hand is already amazing. Seeing quantum waves inside that circle makes it even more powerful. It proves that quantum effects are not just equations on paper. They can be shaped, controlled, and even photographed, showing us how strange and beautiful the tiny world really is.

Two days ago, we were proud to see the Nature Magazine publish our article on Certified Quantum Randomness, a task we demonstrated on the Quantinuum H2 trapped-ion #quantum computer, unattainable on any classical supercomputer. Unlike the randomness sources accessible on today's classical computers, the output of our #quantumcomputing-based protocol can be certified to be random under certain computational-hardness assumptions, with no trust required in the hardware generating the randomness. We are humbled by the enthusiastic response we received from the scientific community and industry. To better explain of the usefulness of Certified Quantum Randomness in the industry, we wrote a companion perspective paper, entitled "Applications of Certified Randomness," now available as an arXiv preprint at the following URL: https://lnkd.in/eCX7vDXP In this perspective, we explore real-world applications for which the use of certified randomness protocols may lead to improved security and fairness. We identify promising applications in areas including #cryptography, differential #privacy, financial markets, and #blockchain. Through this initial exploration, we hope to shed light on potential applications of certified randomness.

⚛️ 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

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

Quantum computing is pushing the boundaries of chemical simulations to unprecedented accuracy! In a groundbreaking study recently published in The Journal of Chemical Theory and Computation, researchers from IBM Quantum® and Lockheed Martin demonstrated a significant milestone in quantum chemistry, the application of sample-based quantum diagonalization (SQD) techniques to accurately model "open-shell" molecules. Why is this critical? Open-shell molecules like CH₂ (methylene) have unpaired electrons, resulting in complex electronic structures that classical computational methods struggle to simulate accurately. Methylene is particularly intriguing because its high reactivity and magnetic properties significantly influence combustion processes, atmospheric chemistry, and even interstellar phenomena. By harnessing quantum computing, researchers successfully calculated CH₂’s singlet-triplet energy gap—a notoriously difficult challenge for classical approaches. This advancement paves the way for accurately predicting chemical reactivity and designing novel materials crucial for aerospace, catalysis, and sensor technologies. Quantum computing is becoming a transformative tool in real-world chemical research. Explore the full details of this landmark study below #QuantumComputing #QuantumChemistry #IBMQuantum #LockheedMartin #OpenShellMolecules #AerospaceInnovation #MaterialsScience #ChemicalSimulation

https://lnkd.in/eAJQMGV3 This represents one of the first practical applications of quantum algorithms to computational biology. We're not just theorizing—we've shown that quantum interior point methods can successfully analyze cellular metabolism. Key Highlights: -Demonstrated quantum algorithms solving real biological problems -Designed for near-term quantum devices -Successfully optimized cellular metabolism pathways (glycolysis + TCA cycle) -Establishes foundations for quantum computational biology Our approach can be applied: -Community-scale microbiome simulations -Biofuel optimization in synthetic biology -Drug target discovery in metabolic pathways -Model refinement for precision systems biology The dawn of quantum computational biology is here. #QuantumComputing #ComputationalBiology #SystemsBiology #QuantumAlgorithms #Metabolism #SyntheticBiology #DrugDiscovery #Microbiome #PrecisionMedicine