GeSn Use Cases in Quantum Computing

IBM Quantum’s USE CASES: A GLIMPSE INTO THE FUTURE OF INDUSTRY 🧬🏦 ⚛️ A couple of weeks ago, during the Quantum World Congress 2025, Jay Gambetta, head of IBM Quantum, highlighted some of the industry use cases they are exploring in collaboration with leading organizations. Let’s see how they have been applying quantum algorithms to industry-relevant problems: 🏦 HSBC – Enhancing trade execution strategies for corporate bonds • Context: Corporate bond markets are relatively illiquid, so execution strategies (when, where, and how to trade) matter for minimizing costs. • Quantum role: Quantum machine learning can identify execution patterns and optimize order placement under uncertainty. • Value: Improves liquidity management and reduces transaction costs. 🏦 Vanguard – Portfolio optimization for fixed income assets • Context: Asset managers must balance return and risk across thousands of bonds and debt instruments. • Quantum role: Quantum optimization algorithms can search through complex solution spaces (mean-variance, risk-return tradeoffs) potentially more efficiently than classical solvers. • Value: More efficient capital allocation, better hedging, and portfolio diversification strategies. ✈️ Boeing – Exploring material degradation caused by radicals • Context: Aircraft materials degrade over time due to exposure to oxygen, radiation, and chemical radicals. • Quantum role: Quantum computers can simulate the Hamiltonian dynamics of radical reactions at a molecular level, something classical computers struggle with due to exponential complexity. • Value: More accurate modeling of material aging could lead to safer, longer-lasting components and reduced maintenance costs. 🧬 Moderna – Predicting mRNA secondary structures with quantum optimization techniques • Context: mRNA vaccines depend on folding into stable secondary structures that influence translation efficiency and stability. • Quantum role: Quantum optimization algorithms (e.g., QAOA, VQE-inspired heuristics) can explore folding possibilities more efficiently than brute-force classical approaches. • Value: Enables better vaccine design and faster response to new pathogens. 🚚  E.ON – Optimizing vehicle-to-grid fleet charge scheduling • Context: EV fleets can act as grid storage, but optimizing charging/discharging schedules with constraints (energy prices, demand curves, grid stability) is a massive optimization challenge. • Quantum role: Hybrid quantum-classical optimization and quantum machine learning (QML) can help handle nonlinear scheduling problems. • Value: Reduces costs, stabilizes grids, and integrates renewables more effectively. Each of these use cases targets a real bottleneck where classical methods fall short, showing how quantum computing is beginning to connect computational capabilities with industry needs. Which of these applications do you think will deliver impact first?

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

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

“We make cars. What could quantum possibly do for us?” a representative from a major car company asked me this week. “And besides,” they added, “we already use AI — so we’re probably covered.” Fair question. And no, quantum won’t make trucks teleport (ever). But it will reshape how cars are designed, produced, powered, and maintained — often together with #AI. In fact, companies like Volkswagen Group, Mercedes-Benz AG, and Porsche AG are already exploring quantum use cases today: ⚡ Battery breakthroughs - car manufacturers are working with companies developing quantum hardware to simulate lithium-sulfur battery materials using #QuantumComputing. The idea is to improve charge capacity, energy density, and battery life for electric vehicles. ⚡ ⚡ Production optimization - another use case is to apply quantum to simulate welding and other processes, identifying potential defects before they happen on the factory floor. And this is just the beginning. Let’s unpack how quantum will act as a force multiplier for AI — especially in industrial sectors like automotive, logistics, and mobility: 🔹 Faster training of AI models Training large models for autonomous driving or fleet management takes serious compute. Quantum computing could speed up complex math operations in deep learning — shaving training time from months to days. 🔹 Smarter supply chain optimization Quantum algorithms like QAOA could help AI find faster, better solutions to complex problems like routing, scheduling, and resource allocation — critical in global automotive supply chains. 🔹 Next-gen R&D simulations AI + quantum chemistry = a leap in simulating materials, structures, and battery components, before building anything physical. That means faster, smarter innovation. 🔹 Safer autonomy through better NLP Vehicle perception systems rely on understanding nuance and context. Quantum-enhanced NLP may help AI interpret rare edge cases more accurately — a big win for autonomous driving safety. 🔹 Richer data analytics Quantum machine learning could unlock insights from massive, high-dimensional datasets — from predictive maintenance to customer behavior modeling. Bottom line? Quantum won’t replace AI. But it will unlock a new scale of possibility. We’re moving from “maybe someday” to “what can we pilot now?” And those who start early — even with hybrid quantum-classical approaches — will build real strategic advantage. Curious what you think: 👉 Where do you see quantum enhancing AI in your industry? Let’s exchange ideas, in comments below!

The Future of MRI: What Happens When Quantum Computing Meets Medical Imaging? Google’s launch of its first quantum computer chip opens up a completely new frontier for MRI technology. Imagine combining quantum mechanics with advanced imaging—what we could achieve is nothing short of revolutionary. Let’s explore how quantum computing could reshape MRI as we know it, pushing boundaries in resolution, speed, and accessibility. Quantum-Enhanced MRI: A Concept Picture an MRI sequence designed with quantum principles like entanglement and superposition at its core: Entangled Spin States: Instead of traditional RF pulses, quantum algorithms would entangle nuclear spins in tissue, creating a shared quantum state. This massively amplifies signal sensitivity, especially for detecting rare biomarkers or low-concentration metabolites. Superposition for Encoding: Quantum superposition could encode spatial information (X, Y, Z) simultaneously, slashing scan times by reducing the need for multiple gradient applications. Spin Squeezing: By manipulating quantum uncertainty, we could reduce noise in one dimension while enhancing signal precision in another—perfect for ultra-high-resolution imaging. Quantum Feedback Loops: Real-time quantum computation could dynamically optimize the magnetic field, compensating for patient motion or scanner imperfections on the fly. Possible Scenarios for the Future of MRI Ultra-High-Resolution Imaging: Quantum computing could refine MRI to image at the cellular or molecular level, potentially visualizing structures like individual proteins or mapping brain networks in unprecedented detail. Use Case: Detecting diseases like Alzheimer’s years before symptoms appear. Faster, Real-Time Scans: With quantum-enhanced processing, MRIs could achieve real-time imaging. Motion artifacts would become irrelevant, and scanning entire organs could take seconds instead of minutes. Use Case: Emergency cardiac imaging or dynamic tracking of blood flow. Improved Sensitivity for Early Detection: Quantum sensors could enable detection of weak magnetic resonance signals, helping diagnose early-stage cancers or rare diseases. Non-proton imaging (e.g., sodium or phosphorus) might even become routine. Use Case: Identifying cancers or metabolic changes long before they’re visible in conventional scans. Portable, Affordable MRI Systems: Quantum computing could lead to more compact hardware designs and cheaper magnets, enabling portable systems for underserved areas. Use Case: Scalable solutions for remote or low-resource settings. Hybrid Imaging: Quantum computing could make it easier to integrate MRI with other modalities like PET or spectroscopy, creating multi-functional devices capable of both structural and metabolic imaging. Use Case: Simultaneously visualizing tumor structure and activity in cancer research. #QuantumComputing #MRI #MedicalImaging #HealthcareInnovation #FutureTech 4o

🚀 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐒𝐔𝐏𝐏𝐎𝐑𝐓 𝐕𝐄��𝐓𝐎𝐑 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 (𝐐𝐒𝐕𝐌): 𝐁𝐑𝐈𝐃𝐆𝐈𝐍𝐆 𝐂𝐋𝐀𝐒𝐒𝐈𝐂𝐀𝐋 𝐌𝐋 𝐀𝐍𝐃 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐂𝐎𝐌𝐏𝐔𝐓𝐈𝐍𝐆 The "Kernel Trick" changed classical Machine Learning by allowing us to classify data in higher-dimensional spaces. But as datasets grow in complexity, classical kernels hit a computational wall. This is where 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐕𝐞𝐜𝐭𝐨𝐫 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 (𝐐𝐒𝐕𝐌) take over, leveraging the vast Hilbert space of quantum mechanics to find patterns that classical silicon simply cannot see. 𝟏. 𝐓𝐇𝐄 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐊𝐄𝐑𝐍𝐄𝐋 𝐂𝐎𝐍𝐂𝐄𝐏𝐓 In a classical SVM, we use a kernel function to measure similarity between data points. In a QSVM, we replace this with a 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐊𝐞𝐫𝐧𝐞𝐥. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠: Classical data is mapped into a quantum state $| \psi(x) \rangle$ using feature maps like 𝐙𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐌𝐚𝐩 or 𝐙𝐙𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐌𝐚𝐩. 𝐇𝐢𝐠𝐡-𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥 𝐇𝐢𝐥𝐛𝐞𝐫𝐭 𝐒𝐩𝐚𝐜𝐞: By encoding data into qubits, we project it into an exponentially large feature space, making it easier to find a hyperplane that separates complex, non-linear data. 𝟐. 𝐀 𝐇𝐘𝐁𝐑𝐈𝐃 𝐀𝐑𝐂𝐇𝐈𝐓𝐄𝐂𝐓𝐔𝐑𝐄 QSVM is a prime example of a 𝐇𝐲𝐛𝐫𝐢𝐝 𝐐𝐮𝐚𝐧𝐭𝐮𝐦-𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞: 𝐈𝐧𝐩𝐮𝐭: Classical features are prepared. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧: A quantum circuit calculates the inner product (similarity) between quantum states to build the 𝐊𝐞𝐫𝐧𝐞𝐥 𝐌𝐚𝐭𝐫𝐢𝐱. 𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: The resulting matrix is fed back to a classical SVM classifier to solve the final optimization problem and define the decision boundary. 𝟑. 𝐖𝐇𝐘 𝐈𝐓 𝐌𝐀𝐓𝐓𝐄𝐑𝐒: 𝐓𝐇𝐄 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐀𝐃𝐕𝐀𝐍𝐓𝐀𝐆𝐄 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬: QSVM can capture intricate correlations in data that are mathematically too "heavy" for classical RBF or Polynomial kernels. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: For specific high-dimensional datasets, quantum kernel estimation offers a path toward superior classification accuracy. 𝐍𝐈𝐒𝐐 𝐑𝐞𝐚𝐝𝐲: Because the heavy optimization stays classical, QSVM is highly effective on today's noisy, intermediate-scale quantum devices. 𝟒. 𝐑𝐄𝐀𝐋-𝐖𝐎𝐑𝐋𝐃 𝐔𝐒𝐄 𝐂𝐀𝐒𝐄𝐒 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: Classifying molecular similarity and bioactivity in massive chemical libraries. 𝐅𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Identifying subtle patterns in transaction data to catch sophisticated fraud. 𝐆𝐞𝐧𝐨𝐦𝐢𝐜𝐬: Managing the high-dimensional feature sets found in DNA sequencing and protein folding. 💡 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐂 𝐓𝐀𝐊𝐄𝐀𝐖𝐀𝐘 QSVM isn't about replacing the SVMs we know and love; it's about 𝐞𝐧𝐡𝐚𝐧𝐜𝐢𝐧𝐠 them. By using quantum mechanics as a specialized "feature extractor," we can tackle non-linear classification problems that were previously out of reach. As hardware stability improves, the gap between classical and quantum kernels will only continue to widen.

How Quantum Computing Will Unlock Trillions in Financial Value ⚛️📈 Quantum computing & finance: this isn’t science fiction — it’s strategy. In 2024, Goldman Sachs revealed that quantum models could cut computation times from days to seconds. BlackRock, JPMorgan, HSBC — they’re not watching from the sidelines. They’re already testing quantum use cases. Why? Because traditional systems can’t handle the sheer complexity of some core financial problems — even with supercomputers. Here are 3 real-world applications — with clear examples and hard numbers VCs will appreciate: 1️⃣ Portfolio optimization (NP-complete problems) 🎯 Imagine choosing 40 assets from 5,000, under constraints like liquidity, volatility, ESG scores, risk exposure… ⚠️ Classic algorithms need to test billions of combinations — a computational nightmare. ✅ Quantum algorithms (like QAOA) dramatically reduce the complexity, helping generate better portfolios in less time. 💰 For large asset managers, improving performance by just 0.5% means millions in added returns annually. 2️⃣ Pricing complex options 💡 Structured products with multiple underlyings require multi-factor models. Monte Carlo simulations can take hours even on advanced infrastructure. 🚀 Quantum models simulate probability distributions natively, enabling pricing in seconds. 📉 That time edge = better arbitrage = real financial alpha. 3️⃣ Fraud & anomaly detection at scale 🔍 A bank like Citi processes over 30 billion transactions per year. Finding subtle or cross-channel anomalies in real time? Nearly impossible. 🧠 Quantum systems can analyze massive datasets simultaneously, mapping complex correlations that classical systems can’t. ➡️ Think of flagging coordinated fraud across accounts, markets, and behavior patterns — before it even happens. 🎯 For investors: • The quantum finance market could surpass €850M by 2030 (source: McKinsey). • Early adopters are building deep tech moats — from IP to specialized data pipelines. • The ecosystem is still forming — there’s huge upside in early-stage SaaS, quantum APIs, hybrid modeling tools, and hardware interfaces. 💬 Quantum hardware is still maturing. But the financial use cases are already validated — and the upside is real. 📌 Just like AI in 2015: those who invest before the explosion will shape the next generation of financial infrastructure. #QuantumComputing #Finance #VC #Fintech #DeepTech #StartupInvesting #NextGenFinance #Innovation #PortfolioOptimization #FraudDetection #OptionPricing

Six months ago, the IDC Worldwide Quantum Computing Forecast made a specific bet: the next phase of quantum computing wouldn't be driven by better hardware alone. It would come from combining increasingly capable quantum systems with AI, HPC, and domain expertise to solve problems beyond the practical reach of classical computing. Last week offered a compelling example of exactly that. IBM, Oak Ridge National Laboratory, and Cleveland Clinic used a hybrid quantum-classical workflow to model the chemistry of molten FLiBe salt, a leading candidate material for future fusion reactors. Rather than replacing classical computing, IBM's increasingly capable quantum hardware was applied to the portion of the problem where it provides the greatest computational advantage, while classical systems handled the remaining calculations. That's exactly the heterogeneous computing model we expect to define enterprise quantum adoption. What's equally important is where this work happened. The research is part of the U.S. Department of Energy's Genesis Mission, bringing together quantum computing, HPC, AI, and domain expertise across the national laboratory ecosystem. Read alongside recent initiatives like QuantumEAGLe, it reinforces a broader trend: government investment is evolving beyond advancing quantum hardware. It's increasingly focused on building the collaborative ecosystem needed to translate scientific breakthroughs into real-world applications. This is also why simulation continues to stand out in our enterprise research. Alongside optimization and quantum AI, simulation remains one of the leading quantum use cases organizations are exploring. Fusion materials research represents one of the most demanding examples imaginable, but the underlying challenge extends well beyond energy. Industries including pharmaceuticals, chemicals, advanced manufacturing, and materials science all face computational problems where heterogeneous computing could eventually deliver meaningful advantages. The remaining challenge isn't demonstrating that quantum can contribute to scientific discovery. It's making these capabilities accessible outside national laboratories. Today's breakthrough required quantum scientists, computational chemists, HPC researchers, and highly specialized workflows. The next phase of the market will depend on advances in both quantum hardware and the surrounding software ecosystem, development platforms, and workflow orchestration that allow domain experts to leverage quantum computing without becoming quantum specialists. I explore what this means for enterprise quantum adoption, heterogeneous computing, and the evolution of the quantum software ecosystem in our latest IDC Link: https://lnkd.in/gFQV95FF Ashish Nadkarni Jeff Janukowicz Jerry M. Chow Jay Gambetta Mike Houston Steven Malkiewicz #Quantum #QuantumComputing #HPC #AI #FusionEnergy #DOE #IDC

Quantum AI is interesting — but where could it actually be useful? A lot of discussion around Quantum AI stays very abstract. But a better question is: Where could Quantum AI create practical value first? - Not everywhere. - Not across entire AI systems. - Most likely in specific parts of the workflow. Here are a few areas that seem especially interesting: 1. Optimization Many AI and engineering workflows depend on optimization. Examples: - model parameter search - resource allocation - scheduling - portfolio or logistics problems If quantum methods help with certain optimization subroutines, that could create very targeted value. 2. Sampling and probabilistic modeling Some AI workflows rely heavily on sampling, probability distributions, and complex search spaces. This is one of the areas where quantum systems are often discussed as potentially useful. 3. Scientific discovery This is the use case I personally find most compelling. AI is already helping with: - molecule discovery - material design - experiment acceleration Quantum computing may eventually complement that by helping model quantum systems more naturally. 4. Quantum hardware control One of the most practical uses today is AI helping quantum systems themselves. For example: - calibration - control - noise reduction - error mitigation So in many cases, the first impact of “Quantum AI” may be: AI is improving quantum systems before quantum improves AI. Final thought: The future of Quantum AI may not come from one big breakthrough. It may come from small, specific advantages inside larger workflows. Curious to hear your view? Which use case feels most realistic to you? - Optimization - Sampling / probabilistic modeling - Scientific discovery - Quantum hardware control Comment 1 / 2 / 3 / 4 #QuantumComputing #AI #QuantumAI #DeepTech #Innovation

🚀 The European Securities and Markets Authority (ESMA) has released a detailed analysis about Quantum computing in financial markets. They identified 4 application areas in which quantum could significantly improve market efficiency, risk management and operational resilience. Major institutions are already running proofs of concept to identify where quantum advantage justifies the investment, and ESMA provide an interesting breakdown of public and private investments in which 🇨🇳China is leading the money game with public spending, while 🇺🇸 US startups gets the major share of venture capital attention. Here the 4 high-potential application cases: 1️⃣ Optimization (most promising in near-term) Many financial problems are combinatorial and could be treated with quantum algorithms that use superposition to explore vast solution spaces, and entanglement to model interdependencies. These are highly compatible with quantum annealing and variational algorithms already runnable on current NISQ hardware: - Mean-variance portfolio optimization, risk minimization and hedging  Index tracking - Trade settlement optimization for clearing houses (maximizing settled transactions without breaching credit limits)  - Predicting financial contagion in interconnected networks and identifying FX arbitrage opportunities 2️⃣ Stochastic Modeling Quantum Monte Carlo Integration theoretically needs quadratically fewer sample paths than classical Monte Carlo for the same accuracy. While data-encoding overheads remain a challenge, QMCI is a compelling pathway as hardware matures: - Pricing of derivatives - Risk measurement: VaR, sensitivity analysis of options 3️⃣ Machine Learning Quantum Machine Learning includes algorithms that speed up classical techniques via quantum linear algebra and quantum-native methods suited to near-term devices. Experimental implementations already match classical accuracy while offering better interpretability: - Credit assessment and fraud detection - Quantum Principal Component Analysis for dimensionality reduction in large datasets  - Quantum reinforcement learning and other supervised tasks 4️⃣ Quantum-enabled distributed ledger technologies Quantum computing could enhance blockchain platforms through:  - Proof-of-quantum-work consensus that slashes energy consumption  - Quantum entanglement for instant tamper detection and faster distributed consensus  - Improved scalability, security and interoperability for tokenisation and cross-institution KYC ⚡️ESMA also stresses the urgency of migrating to post-quantum cryptography as a sufficiently powerful quantum computer could break current public-key cryptography threatening financial transaction security. It's an interesting reading about the potential applications of this fast growing technology. Here is they full report, where you can find more datapoints. #quantum #esma #optimization #ibm