Quantum Computing for Rapid Anomaly Detection

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

> Sharing Resource < Interesting paper this morning: "Opportunities and Challenges for Data Quality in the Era of Quantum Computing" by Sven Groppe, Valter Uotila and Jinghua Groppe Abstract: In an era where data underpins decision-making across science, politics, and economics, ensuring high data quality is of paramount importance. Conventional computing algorithms for enhancing data quality, including anomaly detection, demand substantial computational resources, lengthy processing times, and extensive training datasets. This work aims to explore the potential advantages of quantum computing for enhancing data quality, with a particular focus on detection. We begin by examining quantum techniques that could replace key subroutines in conventional anomaly detection frameworks to mitigate their computational intensity. We then provide practical demonstrations of quantum-based anomaly detection methods, highlighting their capabilities. We present a technical implementation for detecting volatility regime changes in stock market data using quantum reservoir computing, which is a special type of quantum machine learning model. The experimental results indicate that quantum-based embeddings are a competitive alternative to classical ones in this particular example. Finally, we identify unresolved challenges and limitations in applying quantum computing to data quality tasks. Our findings open up new avenues for innovative research and commercial applications that aim to advance data quality through quantum technologies. #quantumcomputing #quantummachinelearning #dataquality #research #papers Link: https://lnkd.in/eFhUfDX7

🚀 Quantum is the future! Just Published! 🚨 As cyber threats become more intelligent and unpredictable, traditional defense models struggle to keep up, especially at scale. I'm thrilled to share our latest research, where we take a quantum leap in network security. 📄 Introducing: Quantum Neural Network-Enhanced Zero Trust Architecture 🔐 What it solves: While quantum computing offers game-changing potential in threat detection and policy enforcement, it’s often held back by computational load and scalability issues. Our QNN-ZTA framework tackles these head-on. 🔍 Key innovations include: ✅ A hybrid quantum-classical security architecture for scalable performance ✅ Quantum-enhanced anomaly scoring for precision threat detection ✅ Dynamic micro-segmentation that isolates high-risk network zones in real time 🧠 By leveraging quantum principles like superposition and entanglement, we achieved: 🔸 87% improvement in threat mitigation efficiency 🔸 Sharper detection accuracy 🔸 Dramatic reduction in false positives This work lays the foundation for a scalable, adaptive, and future-ready cybersecurity model—powered by quantum intelligence. Abstract: Modern networks face mounting challenges from complex cyber threats, struggling to balance scalability and detection accuracy. While quantum computing holds promise for enhanced cybersecurity, it’s hindered by encoding inefficiencies and high processing costs. To overcome these barriers, we introduce QNN-ZTA—a Quantum Neural Network-Enhanced Zero Trust Architecture that merges quantum principles with ZTA and intrusion detection systems. QNN-ZTA uses superposition, entanglement, and variational optimization to deliver real-time anomaly detection and dynamic risk-based policy enforcement. Key innovations include a hybrid quantum-classical architecture for scalable performance and quantum-powered micro-segmentation to contain threats. Our evaluation shows up to 87% improvement in threat mitigation, validating QNN-ZTA as a robust, adaptive, and quantum-optimized model for next-gen cybersecurity. 📘 If you're interested in cybersecurity, quantum computing, or Zero Trust models, I’d love your thoughts and feedback. #Cybersecurity #QuantumComputing #ZeroTrust #QNN #NetworkSecurity #AI #Research #Innovation https://lnkd.in/gH4UTu9z

Quantum AI Revolution in BFSI: The Ultimate Shield Against Fraud & Financial Risk Quantum AI vs Cybercrime: Are Banks Ready for the Real Fight? Every minute, millions of financial transactions take place. Cybercriminals aren’t just keeping up—they’re outpacing us. They’re using AI, automation…, and even quantum-powered attacks. Traditional fraud detection? It’s showing cracks. As someone who’s led tech innovation in BFSI for decades, I believe we’re at a critical turning point where only those who integrate Quantum AI will stay ahead. Here’s why: a)     Legacy models can’t keep up. Too many false positives, missed threats, and slow responses. b)     Quantum-powered fraud is evolving faster than most banks' security infrastructure. c)     Speed vs. Security? With Quantum AI, we can have both real-time authentication and ultra-secure transactions. Quantum AI = a radical upgrade in fraud defence. We’re talking about systems that:        i.           Predict fraud before it happens      ii.           Slash false declines without compromising protection    iii.           Simulate thousands of risk scenarios in real-time    iv.           Make compliance automated and real-time Top financial institutions are already investing billions in this future. Quantum computing will soon make today’s encryption obsolete. This isn’t sci-fi. This is strategic survival. 3 Action Steps for Tech and Finance Leaders: 1. Invest in Quantum AI talent—this is no longer niche tech. 2. Adopt quantum-resistant encryption before your systems get breached. 3. Partner with innovators—startups are pushing the pace, and partnerships are powerful. Let’s shift the conversation from fear to future-readiness. Is your organisation ready for Quantum AI in fraud detection and compliance? How are you preparing? Let’s exchange ideas in the comments—or connect to explore transformation strategies together. #QuantumAI #AIinBanking #CyberSecurity #FraudPrevention #DigitalTransformation #AIinBFSI #FutureOfFinance #QuantumComputing #RegTech #CIOLeadership

Quantum Computing: The Next Leap to a Scam-Free Financial Future Picture this: a world where digital scammers and fraudsters no longer have the upper hand, and financial crime detection becomes nearly instantaneous. Today, financial professionals and regulators race against increasingly complex scams and money laundering operations, often falling behind due to the limits of conventional computing. Quantum computing is changing that narrative. Its power lies in processing massive datasets, revealing suspicious patterns in real-time, and running sophisticated simulations to detect fraud the moment it occurs. For example, Italian bank Intesa Sanpaolo partnered with IBM to pilot quantum machine learning for fraud detection. Early results showed quantum algorithms could pinpoint fraudulent transactions with far quicker speed and greater accuracy than legacy systems, dramatically reducing both false alarms and missed scams. How do we make this vision a reality? Financial institutions must first establish cross sector partnerships with technology providers and regulators to develop and pilot quantum fraud detection systems at scale. Simultaneously, organisations need to migrate legacy cryptographic infrastructure to post quantum standards before quantum computers become powerful enough to break existing encryption, a transition the BIS estimates should be underway by 2030. Finally, regulators must establish clear timelines and compliance frameworks that incentivise the adoption of quantum ready systems, creating a coordinated push across the financial ecosystem rather than fragmented, uneven progress. Quantum computing holds the promise of making financial scams a relic of the past. Institutions that act now, adapting systems and skills, will lead this revolution and safeguard global finance for generations to come. Call to Action: Are you preparing your organisation for the quantum leap? Join the conversation on quantum readiness, partner for pilots, and push for policy incentives. Together, we can make a scam-free future more than just a dream. #QuantumComputing #FinancialSecurity #SAfeCities

My honest, unfiltered thoughts about applications of #QuantumComputing on some of the use cases of AI/ML are: 1. Efficiency boost: More efficient for critical use cases like surface defect detection, which is a computer vision application of ML, reduced trainable parameters 2. Scalability:  Opened scope for solving larger problems   3. Enhanced reliability: Delivered higher accuracy even with poor datasets My team at BosonQ Psi (BQP) has built #QML solvers as part of #BQPhy, and demonstrated their usage with our newest partner, materialsIN, for the first time. Our modern world relies heavily on concrete infrastructure, but even the strongest foundations can succumb to the relentless forces. To prevent structural failure, early detection of cracks and defects is paramount. BosonQ Psi (BQP) and materialsIN have developed this advanced approach using hybrid quantum-classical machine learning. This significantly improves early crack detection, identifying cracks in concrete, even under challenging conditions. By leveraging the power of quantum algorithms, we've overcome the limitations of traditional methods. Our quantum-powered crack detection offers: i. Enhanced accuracy: Pinpointing even the smallest defects. ii. Efficiency: Handling large-scale projects with ease. iii. Better detection: Identifying rare defects, even with limited data. Want to learn more about how quantum can safeguard our infrastructure? Read more: https://lnkd.in/dndpdq4Y   #quantumcomputing #QML #optimization #engineering #construction