Quantum Machine Learning Basics: Qiskit, Quantum Neural Networks, Hybrid Models

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

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 Learning Quantum Computing QuCode 📌 Today’s Focus 🔹 Quantum Data Encoding 🔹 Quantum Neural Networks 💡 Turning classical data into quantum states unlocks new ways to process information. Combine that with the power of neural networks, and you get a glimpse of how quantum machine learning could transform the future. #QuantumComputing #QuantumMachineLearning #QuantumAI #FutureTech #Innovation #LearningJourney

📌 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

Quantum Computing-19- #Qucode Quantum Machine Learning (QML) Basics Quantum Machine Learning (QML) brings together the power of quantum computing and machine learning, opening doors to solving problems classical systems struggle with. 🔹 Quantum Data Encoding The first challenge is representing classical data as quantum states. Techniques like amplitude encoding, angle encoding, and basis encoding map data into qubits. Efficient encoding is crucial since quantum advantage depends on how well data is embedded into the quantum space. 🔹 Quantum Neural Networks (QNNs) QNNs extend classical neural network ideas into the quantum realm. They use parameterized quantum circuits (PQCs), where tunable gate parameters are optimized like weights in neural networks. QNNs exploit superposition and entanglement to capture richer data relationships, potentially learning patterns inaccessible to classical models. ✨ QML is still in its early stages, but it holds promise for areas like drug discovery, optimization, and finance, where complex high-dimensional data is common. 🔮 The fusion of quantum power with machine intelligence could be a defining shift in the next era of computing.

#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

#WiMi Has Developed a Scalable Quantum Neural Network (SQNN) Technology Based on Multi-quantum-device Collaborative Computing https://lnkd.in/dC3SaZDM #CIOFirst #HologramAugmentedReality #Multiquantumdevice #News #quantumcomputing #quantumpredictor #ScalableQuantumNeuralNetwork #WIMIHologramCloud

AI Characterization Advances Tackle Exponential Scaling in Large-Scale Quantum Systems Artificial intelligence now offers a powerful means to characterise increasingly complex scientific systems, effectively sidestepping the traditional limitations imposed by their vast scale and complexity #quantum #quantumcomputing #technology https://lnkd.in/eWPTn_mJ

Day 19: Entering the Quantum Machine Learning Frontier 🤖 Day 19 of the QuCode’s 21 Days Quantum Computing Challenge - Cohort 3 was an exciting introduction to the cutting-edge field of Quantum Machine Learning (QML). I explored how quantum computers can be used to process data and learned about the fundamental concepts of quantum data encoding and quantum neural networks. I used the following resources for my self-study: * https://lnkd.in/gSDH92Pr * https://lnkd.in/g7Jur7EC * https://lnkd.in/gq9wSJQR * https://lnkd.in/g6szFqN7 My key takeaways from today's study: 1. Quantum Neural Networks (QNNs): A Hybrid Approach Quantum Neural Networks are a prime example of hybrid quantum-classical computing. A QNN consists of three main parts: * Data Encoding: This is the process of converting classical data into a quantum state that the quantum computer can understand. Techniques like angle embedding map data points to the rotation angles of qubits. * Parameterized Quantum Circuit: This is the core of the QNN, composed of trainable rotation gates and entanglement blocks. These are the quantum equivalent of weights and layers in a classical neural network. * Measurement: The quantum state is measured to extract a classical value, which can be interpreted as a prediction or a probability. 2. The Importance of Data Encoding How we encode classical data onto a quantum state is a critical step in QML. The choice of encoding method, also known as a feature map, can determine the performance of the algorithm. I learned that Qiskit's qml library offers several built-in feature maps to help capture the characteristics of the data in a way that is beneficial for a quantum computation. 3. Tackling Today's Challenges QML is still in its infancy, and I learned about the challenges facing it. A major issue is the "barren plateau" problem, where a QNN's cost function becomes too flat for classical optimizers to effectively train the model. #QuantumComputing #QuCode #21DaysChallenge #LearningJourney #QuantumMachineLearning #QML #QNN #QuantumNeuralNetworks #HybridComputing

Learning Reflections on Quantum Machine Learning (QML) Basics Attended expert session on Quantum Machine Learning (QML) introduced an exciting blend of quantum computing principles with machine learning techniques. Key learnings included: How qubits and superposition enable richer data representations compared to classical bits. The role of quantum feature maps in embedding classical data into high-dimensional Hilbert spaces. Introduction to variational quantum classifiers (VQCs) as a hybrid approach combining quantum circuits with classical optimization. Potential applications of QML in pattern recognition, anomaly detection, and optimization problems. Hands-on practice with QuCode, which helped visualize how quantum states can encode and process data in ways that classical ML cannot easily replicate. 💡 Major takeaway: QML is not about replacing classical machine learning—it’s about unlocking new computational possibilities where classical methods struggle. I’m inspired to keep exploring how QML could drive breakthroughs in AI, cryptography, and complex data-driven domains. 🚀 #QuantumComputing #QuantumMachineLearning #QML #AI #Research