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  • About

Art

  • 1. Acrylic Pouring - tegneskole.no versjon

Applied Mathematics

  • Computational Fluid Dynamics

Data Science

  • Data Science

Computer Science

  • Software Engineering

Quantum Mechanics

  • 2. Quantum Mechanics

Quantum Computation

  • 3. Quantum Computation

Quantum Computer Hardware

  • 4. The Hardware of Quantum Computers
  • 5. Quantum materials
  • 6. Introduction to Ket notation
  • 7. Multi-Qubit States & Operations
  • 8. Advantages and dissadvantages of Ket notation
  • 9. A fact about maximally entangled states
  • 10. Experimental and theoretical measurements
  • 11. Spin Qubits
  • 12. Superconducting qubits
  • 13. V
  • 14. Circuit QED
  • 15. Assembling a quantum processor
  • 16. NV Center Qubits
  • 17. Quantum Annealing
  • 18. Topological Qubits
  • 19. Introduction to Topological Qubits
  • 20. Majorana fermions and where to find them
  • 21. Majorana bound states in superconductors
  • 22. Majorana experiments

Quantum Machine Learning

  • 23. Quantum Machine Learning Intro
  • 24. Classical vs. Quantum
  • 25. Quantum Advantage
  • 26. Challenges
  • 27. What are the elements of a quantum circuit?
  • 28. Classical and Quantum Probability Theory
  • 29. Quantum Machine Learning is a rocket emerging
  • 30. Classical probability distributions
  • 31. The Geometry of Probability Distribution
  • 32. Stochastic Matrix
  • 33. Quantum states
  • 34. Qubits revisited
  • 35. Superposition revisited
  • 36. Bloch Sphere revisited
  • 37. Interference
  • 38. More qubits and entanglement
  • 39. Multiple Qubits revisited
  • 40. Further reading
  • 41. Measurements revisited
  • 42. Bra-Ket Notation
  • 43. Dot product
  • 44. Ket-Bra
  • 45. More on the bra-ket notation
  • 46. More on Measurements
  • 47. Collapse of the Wave Function
  • 48. The Born Rule
  • 49. Measuring multiqubit systems
  • 50. Mixed States
  • 51. Density Matrix
  • 52. Evolution in Closed Systems
  • 53. Unitary evolution
  • 54. More on the Unitary evolution
  • 55. Open Quantum Systems
  • 56. Interaction with the environment: open systems
  • 57. Classical Ising Model
  • 58. Hamiltonian
  • 59. The Ising model
  • 60. The transverse-field Ising model
  • 61. Commuting Hamiltonian
  • 62. Gate-Model Quantum Computing
  • 63. Quantum Approximate Optimization Algorithm.
  • 64. Solovay-Kitaev theorem
  • 65. Quantum Circuits
  • 66. Hadamard gate
  • 67. The CNOT gate
  • 68. Defining circuits
  • 69. Compilation
  • 70. References
  • 71. Adiabatic Quantum Computing
  • 72. Adiabatic Theorem
  • 73. Unitary evolution and the Hamiltonian
  • 74. The adiabatic theorem and adiabatic quantum computing
  • 75. Quantum Annealing
  • 76. Chimera Graph
  • 77. Quantum annealing
  • 78. References
  • 79. Implementations
  • 80. Superconducting Architectures
  • 81. Dissadvantages
  • 82. Trapped ions
  • 83. Photonic Systems
  • 84. Quantum Approximate Optimization Algorithm
  • 85. Quantum approximate optimization algorithm
  • 86. Analysis of the results
  • 87. Encoding Classical Information
  • 88. Loss Functions and Regularization
  • 89. Ensemble Learning
  • 90. Ensemble methods
  • 91. Qboost
  • 92. More QBoost
  • 93. Solving by QAOA
  • 94. References
  • 95. Clustering by Quantum Optimization
  • 96. Mapping clustering to discrete optimization
  • 97. Solving the max-cut problem by QAOA
  • 98. Solving the max-cut problem by annealing
  • 99. References
  • 100. Kernel Methods
  • 101. An Inference
  • 102. Thinking backward: learning methods based on what the hardware can do
  • 103. A natural kernel on a shallow circuit
  • 104. References
  • 105. An Inference Circuit
  • 106. (Tror ikke skal være her men i (11))
  • 107. Probalistic Graphical Model
  • 108. GFX!!!??
  • 109. Probabilistic graphical models
  • 110. Optimization and Sampling PGMs
  • 111. Se igjen i lyx (Husk implementering og plots)
  • 112. Se bildet grafen på lyx
  • 113. Boltzmann machines
  • 114. References
  • 115. SE EKSAMEN!!!
  • 116. Quantum Fourier Transform
  • 117. Introduction
  • 118. Quantum Fourier Transform
  • 119. Even more Quantum Phase Estimation
  • 120. Quantum phase estimation
  • 121. References
  • 122. Overview of the Harrow-Hassidim-Lloyd Algorithm
  • 123. Introduction
  • 124. Setting up the problem
  • 125. Quantum Matrix Inversion
  • 126. Quantum phase estimation
  • 127. Using Quantum Linear Algebra for Learning
  • 128. Conditional rotation of ancilla
  • 129. Uncomputing the eigenvalue register
  • 130. Rejection sampling on the ancilla register and a swap test
  • 131. Quantum-Assisted Gaussian Processes
  • 132. References
  • 133. Integrating quantum kernels into scikit-learn
  • 134. Preliminaries
  • 135. Data preparation
  • 136. Training
  • 137. Creating a variational classifier with PennyLane
  • 138. The variational circuit
  • 139. Importing libraries
  • 140. Implementing the circuit
  • 141. Loading data
  • 142. Visualising the decision boundary
  • 143. More Training

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