Hands-on session 5

Session Recording

Lab book

Jupyter notebook

• Quantum models as Fourier series
  • Background
  • Goal of this demonstration
  • Part I: Fitting Fourier series with serial Pauli-rotation encoding
    • Define a target function
    • Define the serial quantum model
    • Fit the model to the target
  • Part II: Fitting Fourier series with parallel Pauli-rotation encoding
    • Define the parallel quantum model
  • Send it after class: Training the model
  • Part III: Sampling Fourier coefficients
    • Define your quantum model
    • Send it after class
  • Continuous-variable model
    • Send it after class
• Data-reuploading classifier
  • Background
  • Transforming quantum states using unitary operations
  • Data loading using unitaries
  • Model parameters with data re-uploading
  • The cost function and “nonlinear collapse”
  • Multiple qubits, entanglement and Deep Neural Networks
  • “Talk is cheap. Show me the code.” - Linus Torvalds
    • Simple classifier with data reloading and fidelity loss
    • Utility functions for testing and creating batches
    • Train a quantum classifier on the circle dataset
    • Results
  • Send it after class
  • References