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Quantum EngineeringYear 2: Advanced Quantum ScienceMonth 35Day 970

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Year 2·Month 35·Week 3

Day 970: Quantum Neural Networks

Day 970 of 2,016~20 min read

Learning Objectives

  • •**Design quantum neural network architectures** with appropriate layer structure
  • •**Distinguish encoding layers from variational layers** and their roles
  • •**Analyze QNN expressibility** and universality properties
  • •**Implement deep quantum circuits** with multiple encoding-variational blocks
  • •**Compare QNN architectures** to classical neural networks
  • •**Apply QNNs** to regression and classification tasks

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Year 2 Semester 2B Fault Tolerance HardwareMonth 35 Advanced Algorithms - Week 139 QML FoundationsSchedule OverviewLearning ObjectivesMorning Session Theory 3 hours1 What is a Quantum Neural NetworkDefinition and ContextQNN vs Classical Neural Network2 QNN Architecture Components21 Encoding Layers S_lmathbfx22 Variational Layers W_lboldsymboltheta23 Measurement Layer3 The Data Re-uploading ArchitectureMotivation Universal ApproximationMulti-Qubit Extension4 Expressibility of QNNsDefinitionFactors Affecting ExpressibilityExpressibility vs Trainability Trade-off5 QNN Output FunctionsFourier Series RepresentationFrequency Spectrum Constraints6 QNN Types and Variants61 Quantum Perceptron62 Quantum Convolutional Neural Network QCNN63 Quantum Graph Neural Network7 Connection to Classical MLUniversal Approximation ComparisonCapacity ScalingAfternoon Session Problem Solving 2 hoursWorked Example 1 Single-Qubit QNN OutputWorked Example 2 Counting Trainable ParametersWorked Example 3 QNN vs Classical NN Parameter EfficiencyPractice ProblemsProblem 1 Layer Design Direct ApplicationProblem 2 Frequency Analysis IntermediateProblem 3 QCNN Design ChallengingEvening Session Computational Lab 2 hoursLab Building Deep Quantum Neural NetworksExpected OutputSummaryKey FormulasKey TakeawaysConnection to Classical MLDaily ChecklistPreview Day 971
Day 969Day 970 of 2,016Day 971