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📢 Exciting News! 📢 Today at 11:40 CET, I will be speaking at the Data Science Summit ML Edition conference about Quantum Machine Learning (QML) for binary classification and its potential use in credit scoring. In my talk, I will discuss how quantum computers could revolutionize machine learning by consuming significantly less energy than classical GPU-powered computers. I'll explain the vast computational space of the quantum state vector, which grows exponentially with the number of qubits, and explore how a quantum computer could be the future of machine learning. We'll dive into the key differences between QML and classical ML, and explore practical ways to experiment with QML on current quantum machines. My goal is to bring this fascinating topic closer to all attendees. Don't miss out on this opportunity to learn about the cutting-edge advancements in Quantum Machine Learning! Looking forward to seeing you there! #QuantumMachineLearning #ML #CreditScoring #QuantumComputing #MachineLearning

I'm happy to share our new paper on arxiv: "Harnessing quantum back-action for time-series processing" with Giacomo Franceschetto Antonio Acín Maciej Lewenstein Pere Mujal Torreblanca TL;DR We show that inherently quantum effect, i.e. quantum backaction, can serve as a useful resource in time-series processing with quantum reservoir computing. In quantum computing, every measurement we make on a system creates a “back-action” effect, impacting the system itself. Imagine trying to study something fragile under a microscope: some measurements reveal a lot but can also disrupt the subject. This concept applies in quantum systems, where traditional (or "projective") measurements give rich details but can destabilize the system itself. Our latest work explores a lesser-known approach—weak measurements. Think of weak measurements as gentler, balancing the information we gather with minimal disruption. While projective measurements are widely used in quantum computing, weak measurements are an untapped resource with exciting potential. By integrating weak measurements into Quantum Reservoir Computing (QRC)—a type of quantum machine learning—our team saw notable improvements in both efficiency and performance on challenging tasks, like predicting time-series data. QRC allows quantum systems to “remember” past information to improve future predictions, but traditional measurements often disrupt this memory. Weak measurements, however, allow the system to stay more stable, unlocking better predictive power. Key Takeaways: 1. Less is More: Weak measurements enable us to collect information without disrupting the system’s memory. 2. Optimized Performance: By adjusting the strength of these measurements, we found a good balance, enhancing task performance in quantum machine learning. This research brings us a step closer to powerful, more efficient quantum machine learning models. Exciting times ahead for the intersection of quantum computing and AI :) https://lnkd.in/dhzSsnKD Enjoy! #QuantumComputing #MachineLearning #AI #QuantumReservoirComputing #QuantumTech #quantummechanics #quantumtechnologies

Before diving deeper into Quantum Machine Learning (QML), let's take a moment to clarify the concepts of quantum superposition, parallelism, and entanglement—key properties that fundamentally distinguish quantum computation from classical computation.

📢 Exciting new blog post alert! Check out our latest post on "FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models" where we delve into the integration of large language models (LLMs) into Neural Architecture Search (NAS). We explore how FL-NAS, our novel LLM-based NAS framework, simultaneously considers model accuracy, fairness, and hardware deployment efficiency. Our experiments show that FL-NAS outperforms state-of-the-art DNN models across various design considerations. Read the full post here: https://bit.ly/3SDjDyn

✨ #Quantum Computing is a multidisciplinary field at the intersection of computer science, physics, and mathematics that seeks to use the information processing power of quantum mechanics to solve otherwise difficult computational problems 💻. 🎖 Announcing the XPRIZE #Quantum Applications, a 3-year, $5 million global competition to apply quantum computing to accelerate 📈 quantum applications that address global challenges 🌐. 🏅 The Google Quantum AI team is powering the XPRIZE #Quantum Applications as title sponsor, made possible in partnership with Google.org and supporting partner #GESDA. 🔎 #Google #Quantum #AI team's focus is to unlock the full potential of quantum computing by developing a large-scale computer capable of complex, error-corrected computations 💻. #google #quantum #computing #ai #xprize #gesda #innovation 👉 Follow me Levent Coskun to stay up to date on #robotics #AI #hightech and #innovation. To get notifications about my posts please activate the notification bell 🔔 in my profile.

I just completed the 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐏𝐞𝐧𝐧𝐲𝐋𝐚𝐧𝐞 by PennyLane! PennyLane by Xanadu is an open-source framework for quantum machine learning, quantum chemistry, and quantum computing. 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐐𝐌𝐋) explores the intersection of ideas from quantum computing and machine learning, and aims to leverage quantum computing to potentially accelerate tasks like training machine learning models and develop new algorithms beyond classical computing capabilities. The challenge involved constructing a quantum circuit for a quantum machine learning model using 𝐭𝐫𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐛𝐥𝐨𝐜𝐤𝐬 and 𝐞𝐧𝐜𝐨𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬. 𝐓𝐫𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐁𝐥𝐨𝐜𝐤𝐬 are similar to layers in classical neural networks and consist of parameterized quantum operations that adjust during training, similar to how weights are tuned in classical models. 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬 convert classical data into quantum states for processing. This experience provided a brief glimpse into the evolving field of QML, and I'm eager to dive deeper!

In Episode 9, we discuss how the property of superposition can help us with our counterfeit bag detector in one single query. https://lnkd.in/gwq7HgSf #data #analytics #ai #ml #datascience #designedanalytics #quantumcomputing

🚀 Excited to present our technical paper at IEEE Quantum Week 2024 in Montréal! 🚀 Join me on Wednesday, for my presentation on "A Hybrid Quantum-Classical Physics-Informed Neural Network Architecture for Solving Quantum Optimal Control Problems" in "Quantum Machine Learning and Neural Network Architectures" session. Our work demonstrates how innovative neural network architectures can bridge the gap between quantum and classical systems to tackle complex control challenges. Looking forward to insightful discussions! See you there! 👋 #IEEEQuantumWeek2024 #QuantumMachineLearning #NeuralNetworks #QuantumComputing

I am happy to share that I have received the Womanium Global Quantum + AI Project certificate after completing a 6-week industry project on Quantum Machine Learning for Conspicuity Detection in Production. The project aims to optimize production by identifying improvement measures through conspicuity detection using process data analysis. It explores the potential of hybrid quantum computing to accelerate this process by implementing and benchmarking hybrid quantum algorithms against classical methods such as machine learning and statistical approaches. Proud to have been part of this experience and grateful for the opportunity offered by Womanium to grow in this cutting-edge field! #QuantumComputing #QuantumAlgorithms #QuantumMachineLearning #QuantumOptimization #AIResearch WOMANIUM

Quantum Computing: Revolutionizing Machine Learning Algorithms by Ignite Talks via iGNiTe ([Global] Quantum Computing) URL: https://lnkd.in/g49hfyBj Dennis Nenno shares the potential applications of quantum computing in machine learning, particularly in speeding up algorithms like principal component analysis. He highlights the challenges in using quantum computers, such as storing and processing large amounts of data, and the need for quantum RAM to efficiently store and retrieve data. Nenno also mentions the potential synergy between understanding quantum computing and AI, as well as his prediction that quantum machine learning will be developed before quantum encryption is crack. #QuantumComputing #MachineLearning #ExponentialSpeedup #RecommendationSystems #QuantumSupremacy #TechnologyAdvancement #FutureInnovation #DigitalTransformations #DataAnalysis #TechRevolution https://ift.tt/kSG9oOa