Qiskit Global Summer School 2021
In this lab, you will learn how to construct quantum states and circuits, and run a simple quantum algorithm.
- Quantum States and Circuits
- 1-1: Bit Flip
- 1-2: Plus State
- 1-3: Minus State
- 1-4: Complex State
- 1-5: Bell State
- 1-6: GHZ-like State
- The Deutsch-Jozsa Algorithm
- 1-7: Classical Deutsch-Jozsa
- 1-8: Quantum Deutsch-Jozsa
In this lab, you will learn how to create and work with parameterized quantum circuits and quadratic programs, and how to solve optimization problems using the Quantum Approximate Optimization Algorithm.
- 2-1: MaxCut
- 2-2: MaxCut to Quadratic Program
- 2-3: Quantum Approximate Optimization Algorithm
- 2-4: Conditional Value at Risk
In this lab, you will learn how to implement quantum feature maps, quantum kernels and quantum support vector classification.
- 3-1: Quantum Feature Map
- 3-2: Quantum Kernel
- Exercise: Quantum Support Vector Classification
In this lab, you will learn how to train circuit-based variational models, using different training techniques and see restrictions the models have and how they might be overcome.
- Computing Expectation Values
- 4-1: By matrix multiplication
- 4-2: By simulation
- Training A New Loss Function
- 4-3: Define the Hamiltonian
- 4-4: Use the SPSA optimizer to find the minimum
- Natural Gradients
- Exploratory Exercise: Natural Gradients and Barren Plateaus
In this lab, you will use quantum process tomography to see how noise affects a typical parameterized quantum circuit used in machine learning.
- 5-1: Build the quantum circuit
- 5-2: Quantum Process Tomography with only shot noise
- 5-3: Build a T1/T2 noise model
- 5-4: Quantum Process Tomography with T1/T2 noise
- 5-5: Quantum Process Tomography with mitigated T1/T2 noise
- Exploratory Exercise: Quantum Process Tomography with varying CX gate durations