Quantum vs Classical Computation 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.

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

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.

A quantum computer recently solved a problem in just four minutes that would take even the most advanced classical supercomputer billions of years to complete. This breakthrough was achieved using a 76-qubit photon-based quantum computer prototype called Jiuzhang. Unlike traditional computers, which rely on electrical circuits, this quantum computer uses an intricate system of lasers, mirrors, prisms, and photon detectors to process information. It performs calculations using a technique known as Gaussian boson sampling, which detects and counts photons. With the ability to count 76 photons, this system far surpasses the five-photon limit of conventional supercomputers. Beyond being a scientific milestone, this technique has real-world potential. It could help solve highly complex problems in quantum chemistry, advanced mathematics, and even contribute to developing a large-scale quantum internet. For example, quantum computers could help scientists design new medicines by simulating how molecules interact at the quantum level—something that classical computers struggle to do efficiently. This could lead to faster discoveries of life-saving drugs and treatments. While both quantum and classical computers are used to solve problems, they function very differently. Quantum computers take advantage of the unique properties of quantum mechanics—such as superposition and entanglement—to perform calculations at incredible speeds. This makes them especially powerful for solving problems that would be nearly impossible for traditional computers, bringing exciting new possibilities for scientific and technological advancements. As the Gaelic saying goes, “Tús maith leath na hoibre”—“A good start is half the work.” Quantum computing is still in its early stages, but its potential to reshape science, medicine, and technology is already clear.

> Sharing Resource < Ok, that's huge: "Exponential quantum advantage in processing massive classical data" by Haimeng Zhao, Alexander Zlokapa, Hartmut Neven, Ryan Babbush, John Preskill, Jarrod R. McClean, Hsin-Yuan (Robert) Huang 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. Link: https://lnkd.in/gmA-ntVU #quantummachinelearning #quantumcomputing #research #paper #bigdata #logicalqubits

If you've been doubting whether quantum computers will ever do anything useful beyond breaking encryption, this one's for you. A quantum computer with fewer than 60 logical qubits can run AI on massive real-world datasets using ten thousand to a million times less memory than any classical machine. Movie review sentiment analysis. Cell type classification from RNA sequencing. Real AI tasks, real data. This is not a storage trick. The quantum computer runs the full ML pipeline. An algorithm called quantum oracle sketching streams data through the processor one sample at a time. Each sample applies a small quantum rotation, then gets discarded. The accumulated rotations build a compressed quantum model of the entire dataset in a handful of qubits. Quantum algorithms then run classification and dimensionality reduction directly on that model. A readout protocol extracts the results. Data in, model built, inference done, predictions out. All on a tiny quantum chip. A classical machine matching this provably needs exponentially more memory, and that proof is unconditional. It relies only on quantum superposition being real. It holds even if you give classical machines unlimited time. Think about what this means for the age of AI. The world generates more data every day than it can store. Every sensor, every device, every interaction. Classical AI has to choose: store less and learn worse, or build bigger data centers and burn more energy. A quantum ML pipeline that learns from streaming data without storing it sidesteps that tradeoff entirely. But to be clear: This is a theoretical proof validated through numerical simulations. It has not been demonstrated on actual quantum hardware. Yet, fewer than 60 logical qubits is in the range that near-term error-corrected machines are targeting. We are finally getting the use-case evidence this field needed. 📸 Credits: Haimeng Zhao, Caltech Alexander Zlokapa Hsin-Yuan (Robert) Huang John Preskill Ryan Babbush Jarrod McClean Hartmut Neven Paper on arXiv:2604.07639 Deep dive on this live on X (@drmichaela_e). Newsletter version at 5pm CET today, link on my website.

Quantum computers aren't just faster. They compute in a fundamentally different way that breaks the rules of classical computing. THE SECRET: QUBITS Classical computers use bits: 0 or 1. One state at a time. Quantum computers use qubits: 0, 1, or BOTH simultaneously through superposition. THE SPEED DIFFERENCE: Classical computer with 3 bits: Can represent 1 combination at a time (000, 001, 010, etc.) Must check each combination sequentially Quantum computer with 3 qubits: Can represent ALL 8 combinations simultaneously Processes them all at once THE EXPONENTIAL ADVANTAGE: • 10 qubits = 1,024 states simultaneously • 50 qubits = 1 quadrillion states simultaneously • 300 qubits = more states than atoms in the universe REAL-WORLD IMPACT: Problem: Factor a 2,048-bit number (crucial for breaking encryption) Classical supercomputer: Billions of years Quantum computer: Hours or days THE CATCH: Qubits are incredibly fragile. They must be kept at near absolute zero (-273°C). Any heat, vibration, or electromagnetic interference destroys the quantum state. WHY IT MATTERS: Drug discovery, climate modeling, Al training, cryptography —all could be revolutionized by quantum computing. We're not just making computers faster. We're fundamentally changing what computation means.

Quantum Just Crossed the Line — This Is the Moment Everything Changes For the first time in history, quantum computing is not just theory! if you think quantum computing is still “research,” think again, it just outperformed the world’s fastest supercomputers by orders of magnitude, in a real, verifiable task. This breakthrough comes from Google’s Willow quantum chip, and it’s not theoretical anymore — it’s applied quantum advantage. Here’s what just happened: -> 13,000× faster than the fastest supercomputers   Not a simulation. Not a toy problem. Classical machines simply couldn’t compete. -> Real chemistry, not benchmarks   Using the Quantum Echoes algorithm, Google mapped complex molecular structures with   unprecedented precision — something classical compute struggles with at scale. -> Verified and repeatable results   This is the critical milestone. The output can be checked, validated, and reproduced —   moving quantum from “interesting” to “useful.” -> This isn’t a lab demo   It’s the first clear signal that quantum computing can solve real-world problems better   than classical systems. Why this matters immediately: -> Drug discovery   Massively faster molecular simulation means quicker, more accurate pharmaceutical design. -> Materials science   Advanced batteries, next-gen solar, and novel materials become computable, not guesswork. -> Previously impossible chemistry   Entire problem classes that were unreachable by classical computers are now on the table. Key takeaway: This is the moment quantum computing stops being a future promise and starts becoming infrastructure. Just like GPUs unlocked AI, quantum is about to unlock an entirely new layer of science, energy, and healthcare. We’re not watching progress anymore — we’re watching a phase change. P.S. For simple, explainers about the chip industry, check out our blog The Semiconductor World. Link in comments. #QuantumComputing #Google #TechBreakthrough #DeepTech #Semiconductors #FutureOfCompute #Innovation #QuantumAdvantage

In my recent posts, I have covered various topics in computing, from the limits of GPUs to alternative approaches like neuromorphic and photonic computing. Today, I want to dive into an area where classical computing simply cannot do the job - not because it’s slow or energy-hungry, but because it’s fundamentally incapable: modeling the world at its most basic level, down to atoms and molecules. This is where quantum computing becomes relevant. To truly solve complex problems like curing diseases or designing batteries that work under specific conditions, we need to model biology, chemistry, and physics at a molecular level, atomic level, and electron level, down to the spin states of the electrons. This is only possible via quantum computing. For example, take aspirin (acetylsalicylic acid), a relatively simple molecule with 15 atoms and 94 electrons. Each electron has two spin states, totaling 188 spin states. To model all possible configurations of these states on a classical computer, you would need 2^188 states, which translates to roughly 6.2 × 10^57 bytes of memory [Stack Exchange, ThoughtCo.] Quantum computers solve this elegantly - representing each state by a unit of information called a qubit. Modeling aspirin would require only 188 qubits - entirely feasible in the quantum realm. This is why quantum computing is essential for advancing our understanding of the physical and biological world. The field is still early, but progress is steady. Quantum-inspired software is already being used, and companies such as SandboxAQ are building real applications. Many more startups are entering the space. I shared a deeper explanation and a list of quantum computing startups in my latest newsletter for those who want to explore further: https://lnkd.in/gK3MXSBj More to come as this series continues.