Tutorials

List of jupyter notebooks in: GitHub.

Basic Usage

• Tequila Basic Usage
  • Create and Simulate Quantum Circuits
  • Create and Simulate Objectives/Expectationvalues
  • Optimization example

• Source (GitHub)

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• Frequently Asked Questions:
  • It is recommended to take a look at the BasicUsage notebook before looking at this
  • Which optimization methods can I use?
  • Which simulators/Quantum-Backends can I use?
  • Can I avoid re-translation/compilation on my objectives/circuits?
  • How can I run on a real quantum computer?
  • How can I emulate a real quantum computer?
  • Can I compile Objectives into different backends?
  • How do I transform Measurements into Hamiltonians?
  • Can I do basic operations on wavefunctions and operators without quantum backends?
  • Can I import an Hamiltonian from OpenFermion?
  • Can I compile into a regular function instead of one which takes dictionaries?
  • How do numerical gradients work?
  • Can I use the numerical gradient protocols from SciPy?

• Source (GitHub)

Chemistry

• Chemistry basics with Tequila
  • Initialize Molecules
  • Using different Qubit Encodings of OpenFermion
  • Setting active spaces
  • Computing classical methods with Psi4
  • Hello World “H2” optimization with LiH in an active space
  • Unitary Coupled-Cluster Style Construction
  • Noisy optimization of an active space molecule with tapered qubit embeding

• Source (GitHub)

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• Excited state calculation

• Source (GitHub)

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• Basis Set Free VQE calculations with Tequila

• Source (GitHub)

Optimization

• Optimization
  • Overview
  • Local Optimizers
  • Numerical and Customized Gradients
  • Bayesian optimization
  • This concludes our tutorial. Hope you had fun! Happy optimizing!

• Source (GitHub)

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• Tequila Tutorial:
  • Handling Optimizers, Initializing parametrized circuits
• FAQ
  • 1: How can I extract the parameters from a given circuit?
  • 2: How can I do measurement based simulation?
  • 3: Which Simulator was used and how can I choose the simulator?
  • 4: Can I use numerical evaluation of gradients
  • 5: Can I use Hessian based optimizations, and can I evaluate Hessians numerically?
• Exercises
  • Exercise 1
  • Exercise 2
  • Exercise 3
• Solutions
  • Exercise 1 & 2
  • Exercise 3

• Source (GitHub)

Miscellaneous

• Optimize Measurements by grouping into commuting cliques
  • How to Use:
  • Behind the Scenes

• Source (GitHub)

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• Training Distributions with KM Divergence

• Source (GitHub)

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• Exploring the application of quantum circuits in convolutional neural networks
  • Background
  • Code and Running the Program
  • Plotting the Results
  • Evaluating the Model

• Source (GitHub)

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• Reduced Density Matrices in Tequila
  • The 1- and 2-RDM
  • Consistency checks
  • References
  • Possible applications

• Source (GitHub)

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• Tequila Tutorial:
  • Single-qubit classifier with data re-uploading

• Source (GitHub)

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• Single-qubit classifier with data re-uploading tutorial
  • Introduction
  • Model
  • Target states
  • Single-qubit classifier circuit
  • Cost function
  • Training
  • Test
  • Improvements and customization

• Source (GitHub)

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• Tequila tutorial: raw state preparation algorithm
  • Quantum gate construction
  • Compute the fidelity
  • Construct a parametrized quantum circuit
  • State preparation algorithm

• Source (GitHub)

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• State preparation algorithm tutorial
  • Quantum gate construction
  • Compute the fidelity
  • Construct a parametrized quantum circuit
  • State preparation algorithm

• Source (GitHub)

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• Noise in Tequila
  • This concludes our brief tutorial on Noise. Stay tuned (and up to date) for more exciting noise features in the future!

• Source (GitHub)