About

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
- 3.1. Quantum Computation 0

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

Ruter

Ruter

Python

3. Quantum Computation
3.1. Quantum Computation 0
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