Exploring Quantum Neural Networks and Data Encoding in Qohort 3

#Day_19 of 21 days quantum computing learning challenge #qucode Topic: Quantum machine learning Quantum Data Encoding: How to load classical data (like tabular data or images) into a quantum computer! Common strategies: Angle encoding, amplitude encoding, or binary encoding. For example, in angle encoding, input vector values are mapped to rotation angles on qubits using [ RX, RY, RZ ] gates. Quantum Neural Networks (QNNs) mirrors a classical neural network: #qucode #cohort_3 #Quantum_computing 🧠 **Classical NN vs Quantum NN Previous Day-19 post with a table generated with perplexity has some factual and grammatical errors.. so rewriting correct information here again.. In QNN: #Input: The classical data is encoded onto qubits. for example, by mapping each feature value to a rotation angle of a quantum gate. #Non_linearity: Unlike classical neural networks, QNN perform only linear (unitary) operations. Introducing nonlinearity achieved via quantum measurement or specialized architectures. #hidden_layer: trainable quantum gates include parameters (angles, phases) that can be tuned.quantum entanglement enables the network to create highly complex, non-local correlations between qubits which enables to process data in an exponentially large state space than classical NN #Output: output measurement probabilities or expectation values from quantum states.Reliable results by many repeated measurements of the quantum circuit are needed

Machine Learning Detects Measurement-induced Entanglement in Arrays of Qubits, Enabling Characterisation of Long-range Quantum Effects Researchers successfully detect long-range entanglement created by measuring qubits, using artificial intelligence to model the complex post-measurement states and revealing a connection to a fundamental shift in how accurately classical systems can predict quantum behaviour #quantum #quantumcomputing #technology https://lnkd.in/eUdqxvbC

Title: Day 19 – Quantum Machine Learning Basics Subtitle: Quantum Data Encoding & Quantum Neural Networks Sections inside the poster: Introduction QML combines quantum computing + machine learning. Goal: Exploit quantum parallelism to accelerate ML tasks. Quantum Data Encoding Amplitude Encoding → encodes classical vector into quantum state. Basis Encoding → maps data to qubits (0/1 states). Angle Encoding → uses rotation gates (Rx, Ry, Rz). Diagram: Show a qubit with rotation gates for angle encoding. Quantum Neural Networks (QNNs) Hybrid structure: classical optimizer + quantum circuit layers. Variational Quantum Circuits (VQCs): parameterized gates adjusted by optimization. Diagram: Classical Input → Data Encoding → Quantum Circuit (parameterized rotations + entanglement) → Measurement → Classical Output. Key Insight: ✅ Quantum feature spaces ✅ Potential for exponential speedups ✅ Early applications in chemistry, finance, drug discovery, image classification #QuantumMachineLearning #QML #QuantumFourierTransform #QFT #QuantumAI #QuantumComputing #FutureOfAI #QuantumAlgorithms #QuantumNeuralNetworks #QuantumML #QMLApplications #QuantumDeepLearning #NextGenAI

📌 Expert Session 3 - Quantum Computing Challenge (Qohort-3) QuCode Credits- Thirumalai M Quantum Machine Learning (QML): Where Physics Meets Intelligence Today’s session bridged two worlds: Machine Learning and Quantum Computing. 🔹 We began with a refresher on ML supervised, unsupervised and reinforcement learning. Mostly recalling how neural networks learn patterns through layers, activations and optimization works. 🔹 Then came the leap: Qubits, Superposition, Entanglement. Unlike classical bits, qubits exist in complex states, enabling richer data representation and correlations across features. 🔹 Enter Quantum Machine Learning (QML): a fusion where quantum principles empower learning algorithms. From encoding methods (basis, amplitude, angle, phase) to quantum neural networks (ansatz design, entangling circuits, optimization), the toolkit is rapidly expanding. 💡 Applications? From medical imaging & genomics to drug discovery and optimization problems, QML is poised to handle tasks beyond the reach of classical ML. But challenges remain :- noise, hardware limits, and error mitigation in today’s NISQ devices. Yet the horizon is clear with fault-tolerant quantum systems, QML could redefine problem solving at scale. 🔑 Takeaway: QML doesn’t replace ML, it complements it. It thrives in domains demanding exploration of vast state spaces and hidden correlations. The question is no longer if QML will matter, but when it will transform the way we learn from data. #QuantumModels #CircuitModel #AdiabaticQC #MeasurementBasedQC #QuantumAlgorithms #QuantumOptimization #Qubits #QuantumHamiltonian #QuantumResearch #QuantumFuture #QuantumComputing #QuCode #QuantumAdvantage #QuantumRevolution #NextGenComputing

Day 19 of 21-Day Challenge of Learning Quantum Computing by QuCode🚀 Day 19 Learnings: 🎯 Today’s Focus ✨ Quantum Data Encoding 🧠 Quantum Neural Networks 🔹Quantum data encoding is the process of transforming classical information into quantum states, such as qubits, for processing in quantum computers and algorithms. This transformation, also known as quantum embedding or a quantum feature map, allows for the use of quantum phenomena like superposition and entanglement to represent and process data more efficiently, potentially leading to exponential speedups in computation for applications in quantum machine learning and other fields. 🔹Quantum Neural Networks (QNNs) are machine learning models that combine concepts from quantum computing and classical neural networks, aiming to leverage quantum phenomena like superposition and entanglement for enhanced efficiency and capability. #Qucode #QuantumComputing #QuantumStates #LearningChallenge #Day19 #QuantumMachineLearning #Singlequbitstates #QuantumState #QuantumStateVisualization #QuantumMechanics #ContinuousLearning #21DayChallenge #Qiskit #QuantumCircuit #Quantumphenomenon #QuantumComputing #QuantumAI #QNN #Innovation #LearningJourney #quantum #FutureTech

Day 19 of my quantum computing journey with QuCode's Qohort-3 took us to the electrifying intersection of AI and quantum mechanics! Today, we explored how these two revolutionary fields are coming together: 🔹 Quantum Data Encoding: We dove into the techniques for translating classical data into the language of qubits. This is the essential first step to unleashing quantum's massive computational power on machine learning problems. 🔹 Quantum Neural Networks (QNNs): We examined how quantum circuits can augment and enhance traditional neural network architectures, opening up new frontiers for solving complex challenges in optimization and pattern recognition. The fusion of Quantum + AI is one of the most promising areas in technology. It's incredible to learn about the building blocks that will define a new era of intelligent computation. ⚛️🤖 #QuantumComputing #QuantumLearning #QuantumAI #QuantumNeuralNetworks #QuantumDataEncoding #QuCode #QuantumIndia #NationalQuantumMission

⚡Physics-Informed Neural Networks (PINN)⚡ PINNs are neural networks trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. The physics-informed neural network can predict the solution far from the experimental data points and thus performs much better than the naive network. This network indeed has some concepts of our prior physical principles. 🌎Blog by Ben Moseley: https://lnkd.in/egixy6DG #engineering #simulation #pinns

📅 Day 19 of 21 – Quantum Computing Challenge Today’s theme: Quantum Machine Learning (QML) Basics 🤖⚛️ Blending the worlds of quantum computing and artificial intelligence. 🧮 Key Takeaways: Quantum Data Encoding (Qiskit): Classical data must be mapped into quantum states. Techniques like angle encoding and amplitude encoding let qubits represent complex datasets. Quantum Neural Networks (Quantum Sense): Quantum circuits can act like neural networks, with parameterized gates serving as trainable weights. These models could one day outperform classical deep learning on certain tasks. Hybrid Models: Near-term QML often combines quantum circuits with classical optimizers, leveraging the best of both worlds.

Quantum AI has the potential to revolutionize industries by providing unprecedented computational power and efficiency. #QuantumAi #Innovation Brilliancy Deep Tech #BrilliancyQuantumBrands https://lnkd.in/gjxQK_tj

🚀Is AI about to get a QUANTUM boost? 🔯Or is quantum computing just hype? Dive into this fascinating explainer to uncover how quantum tech could supercharge AI, tackling mind-bending problems like never before! #AI #QuantumComputing #Innovation 👉Check it out: http://2.sas.com/6040AyFDe