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Tequila

Tequila is an abstraction framework for (variational) quantum algorithms.
It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state of the art simulators as well as on real quantum devices.

Getting Started

Get started with our collection of

Further sources:

Installation

Recommended Python version is 3.9 (3.10).
Tequila supports linux, osx and windows. However, not all optional dependencies are supported on windows.

Install from PyPi

Do not install like this: (Minecraft lovers excluded) <strike>pip install tequila</strike>

You can install tequila from PyPi as:

pip install tequila-basic

this will install tequila with all essential dependencies. We recommend to install some fast quantum backends, like qulacs or qibo, as well. Those can be installed before or after you install tequila.

# install basic tequila
pip install tequila-basic
# install qulacs and/or other backends and use it within tequila
pip install qulacs

Install from github

You can install tequila directly with pip over:

pip install git+https://github.com/tequilahub/tequila.git

Install from devel branch (most recent updates):

pip install git+https://github.com/tequilahub/tequila.git@devel

Tequila Hello World

# optimize a one qubit example

# define a variable
a = tq.Variable("a")
# define a simple circuit
U = tq.gates.Ry(angle=a*pi, target=0)
# define an Hamiltonian
H = tq.paulis.X(0)
# define an expectation value
E = tq.ExpectationValue(H=H, U=U)
# optimize the expectation value
result = tq.minimize(method="bfgs", objective=E**2)
# check out the optimized wavefunction
wfn = tq.simulate(U, variables=result.angles)
print("optimized wavefunction = ", wfn)
# plot information about the optimization
result.history.plot("energies")
result.history.plot("angles")
result.history.plot("gradients")

Chemistry Hello World (Madness backend)

see below for installation of dependencies

import tequila as tq

# initialize molecule (also works over .xyz files --> see next example)
geomstring="Li 0.0 0.0 0.0\nH 0.0 0.0 1.6"
mol = tq.Molecule(geometry=geomstring)

# get the qubit hamiltonian
H = mol.make_hamiltonian()

# get the ansatz (circuit)
U = mol.make_ansatz(name="SPA") # or e.g. UpCCGSD

# define the expectation value
E = tq.ExpectationValue(H=H, U=U)

# minimize the expectation value
result = tq.minimize(E)

# optional:compute classical reference energies
# needs pyscf as well
cisd = mol.compute_energy("cisd")
fci = mol.compute_energy("fci")

print("VQE : {:+2.8}f".format(result.energy))
print("CISD: {:+2.8}f".format(cisd))
print("FCI : {:+2.8}f".format(fci))

Chemistry Hello World (Psi4 or PySCF backend)

see below for installation of dependencies

# define a molecule within an active space
active_orbitals=[1,2,5]
molecule = tq.quantumchemistry.Molecule(geometry="lih.xyz", basis_set='6-31g', active_orbitals=active, transformation="bravyi-kitaev")

# get the qubit hamiltonian
H = molecule.make_hamiltonian()

# create an k-UpCCGSD circuit of order k
U = molecule.make_ansatz(name="UpCCGSD")

# define the expectationvalue
E = tq.ExpectationValue(H=H, U=U)

# compute reference energies
fci = molecule.compute_energy("fci")
cisd = molecule.compute_energy("detci", options={"detci__ex_level": 2})

# optimize
result = tq.minimize(objective=E, method="BFGS", initial_values=0.0)

print("VQE : {:+2.8}f".format(result.energy))
print("FCI : {:+2.8}f".format(fci))

Do you want to create your own methods? Check out the tutorials!

Some Research projects using Tequila

J.S. Kottmann, A. Anand, A. Aspuru-Guzik.
A Feasible Approach for Automatically Differentiable Unitary Coupled-Cluster on Quantum Computers.
Chemical Science, 2021, doi.org/10.1039/D0SC06627C.
arxiv:2011.05938
General techniques are implemented in the chemistry modules of tequila.
See the tutorials for examples.

J.S. Kottmann, P. Schleich, T. Tamayo-Mendoza, A. Aspuru-Guzik.
Reducing Qubit Requirements while Maintaining Numerical Precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach.
J.Phys.Chem.Lett., 2021, doi.org/10.1021/acs.jpclett.0c03410.
arxiv:2008.02819
example code
tutorial on the madness interface

A. Cervera-Lierta, J.S. Kottmann, A. Aspuru-Guzik.
The Meta-Variational Quantum Eigensolver.
arxiv:2009.13545
example code

J.S. Kottmann, M. Krenn, T.H. Kyaw, S. Alperin-Lea, A. Aspuru-Guzik.
Quantum Computer-Aided design of Quantum Optics Hardware.
arxiv:2006.03075
example code
slides

A. Anand, M. Degroote, A. Aspuru-Guzik.
Natural Evolutionary Strategies for Variational Quantum Computation.
arxiv:2012.00101

J. S. Kottmann, A. Aspuru-Guzik,
Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure using a Separable Pair Approximation,
arxiv:2105.03836
example code

K. Choudhary,
Quantum Computation for Predicting Electron and Phonon Properties of Solids
arxiv:2102.11452

P. Schleich, J.S. Kottmann, A. Aspuru-Guzik,
Improving the Accuracy of the Variational Quantum Eigensolver for Molecular Systems by the Explicitly-Correlated Perturbative [2]-R12-Correction
arxiv:2110.06812
tutorial

M. Weber, A. Anand, A. Cervera-Lierta, J. S. Kottmann, T.-H. Kyaw, B. Li, A. Aspuru-Guzik, C. Zhang and Z. Zhao,
Toward Reliability in the NISQ Era: Robust Interval Guarantee for Quantum Measurements on Approximate States
arxiv:2110.09793
tutorial

M. S. Rudolph, S. Sim, A. Raza, M. Stechly, J. R. McClean, E. R. Anschuetz, L. Serrano, A. Perdomo-Ortiz
ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms
arxiv:2111.04695

P. Schleich
Regularization of Quantum Chemistryon Quantum Computers by means of Explicit Correlation
Master thesis

T.-H. Kyaw, T. Menke, S. Sim, A. Anand, N. P. D. Sawaya, W. D. Oliver, G. G. Guerreschi, A. Aspuru-Guzik
Quantum computer-aided design: digital quantum simulation of quantum processors
arxiv:2006.03070

Z. P. Bansingh, T.-C. Yen, P. D. Johnson, A. F. Izmaylov
Fidelity overhead for non-local measurements in variational quantum algorithms
arxiv:2205.07113

H. Lim, H.-N. Jeon, J.-K. Rhee, B. Oh, K. T. No
Quantum computational study of chloride ion attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes
arxiv:2112.15314

A, Meijer- van de Griend, J. K. Nurminen
QuantMark: A Benchmarking API for VQE Algorithms
DOI:10.1109/TQE.2022.3159327
QuantMark Codebase

A. Anand, J.S. Kottmann, A. Aspuru-Guzik
Quantum compression with classically simulatable circuits
code
arxiv:2207.02961

J.S. Kottmann
Molecular Circuit Design: A Graph-Based Approach
arxiv:2207.12421
example code

T.-H. Kyaw, M. B. Soley, B. Allen, P. Bergold, C. Sun, V. S. Batista, A. Aspuru-Guzik
Variational quantum iterative power algorithms for global optimization
arxiv:2208.10470
code

R.A. Lang, A. Ganeshram, A. Izmaylov
Growth reduction of similarity transformed electronic Hamiltonians in qubit space
arxiv:2210.03875

K. Gratsea, C. Sun, P.D. Johnson
When to Reject a Ground State Preparation Algorithm
arxiv:2212.09492

R.P. Pothukuchi, L. Lufkin, Y.J. Shen, A. Simon, R. Thorstenson, B.E. Trevisan, M. Tu, M. Yang, B. Foxman, V. S. Pothukuchi, G. Epping, B. J. Jongkees, T.-H. Kyaw, J. R. Busemeyer, J. D Cohen, A. Bhattacharjee
Quantum Cognitive Modeling: New Applications and Systems Research Directions
arxiv:2309.00597

T.-H. Kyaw, M. B. Soley, B. Allen, P. Bergold, C. Sun, V.S. Batista and A. Aspuru-Guzik
Boosting quantum amplitude exponentially in variational quantum algorithms
10.1088/2058-9565/acf4ba

A.G. Cadavid, I. Montalban, A. Dalal, E. Solano, N.N. Hegade
Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
arxiv:2308.15475

A. Anand, K. Brown
Hamiltonians, groups, graphs and ansätze
arxiv:2312.17146

P.W.K. Jensen, E.R. Kjellgren, P. Reinholdt, K.M. Ziems, S. Coriani, J. Kongsted, S. Sauer
Quantum Equation of Motion with Orbital Optimization for Computing Molecular Properties in Near-Term Quantum Computing
arxiv:2312.12386

Let us know, if you want your research project and/or tutorial to be included in this list!

Dependencies

Support for specific backends (quantum simulators, optimizers, quantum chemistry) can be activated by intalling them in your environment.

Quantum Backends

Currently supported

Tequila detects backends automatically if they are installed on your systems. All of them are available over standard pip installation like for example pip install qulacs. For best performance it is recommended to have qulacs installed.

QuantumChemistry:

Currently supported

Psi4.

In a conda environment this can be installed with

conda install psi4 -c conda-forge

Here is a small tutorial that illustrates the usage.

Madness

In a conda environment this can be installed with

conda install madtequila -c kottmann

This installs a modified version of madness ready to use with tequila.
Alternatively it can be compiled from the sources provided in this fork (follow readme instructions there).
Here is a small tutorial that illustrates the usage. For fast performance it is recommended to not use the conda package.

PySCF

Install with

pip install pyscf

Works similar as Psi4. Classical methods are also integrated in the madness interface allowing to use them in a basis-set-free representation.

Documentation

You can build the documentation by navigating to docs and entering make html. Open the documentation with a browser over like firefox docs/build/html/index.html Note that you will need some additional python packages like sphinx and mr2 that are not explicitly listed in the requirements.txt

You can also visit our prebuild online documentation that will correspond to the github master branch

How to contribute

If you find any bugs or inconveniences in tequila please don't be shy and let us know. You can do so either by raising an issue here on github or contact us directly.

If you already found a solution you can contribute to tequila over a pull-request. Here is how that works:

  1. Make a fork of tequila to your own github account.
  2. Checkout the devel branch and make sure it is up to date with the main github repository.
  3. Create and checkout a new branch from devel via git branch pr-my-branch-name followed by git checkout pr-my-branch-name. By typing git branch afterwards you can check which branch is currently checked out on your computer.
  4. Introduce changes to the code and commit them with git.
  5. Push the changes to your github account
  6. Log into github and create a pull request to the main github repository. The pull-request should be directed to the devel branch (but we can also change that afterwards).

If you plan to introduce major changes to the base library it can be beneficial to contact us first. This way we might be able to avoid conflicts before they arise.

If you used tequila for your research, feel free to include your algorithms here, either by integrating it into the core libraries or by demonstrating it with a notebook in the tutorials section. If you let us know about it, we will also add your research article in the list of research projects that use tequila (see above).

Troubleshooting

If you experience trouble of any kind or if you either want to implement a new feature or want us to implement a new feature that you need: Don't hesitate to contact us directly or raise an issue here on github.

PySCF

If pyscf crashes on import with

Using default_file_mode other than 'r' is no longer supported. Pass the mode to h5py.File() instead

then you need to downgrade the h5py version

pip install --upgrade 'h5py <= 3.1'

The issue will probably be fixed soon in pyscf.

import errors on openfermion and cirq

This is fixed on master and devel but not yet on PyPi (v1.5.1)
You can avoid it by downgrading cirq and openfermion

pip install --upgrade "openfermion<=1.0.0"
pip install --upgrade "cirq<=0.9.1"

Circuit drawing

Standard graphical circuit representation within a Jupyter environment is often done using tq.draw. Without further keywords tequial will try to create and compile a qpic file. For proper display you will need the following dependencies: qpic, pdflatex and convert/ImageMagick (pre-installed on most GNU/Linux distributions, not pre-installed on macs). On GNU/Linux distributions sometimes the permissions of convert to convert pdf to png are not granted, resulting in an error when trying to use tq.draw. Click here for a possible solution.

In general, there is no reason to worry if tq.draw does not function properly. It is just one way to display circuits, but not neccessary to have. Alternatives are:

Qulacs simulator

You will need cmake to install the qulacs simulator pip install cmake

You don't need qulacs for tequila to run (although is is recommended) To install without qulacs just remove the qulacs line from requirements.txt It can be replaced by one (or many) of the other supported simulators. Note that simulators can also be installed on a later point, they don't need to be installed with tequila. As long as they are installed within the same python environment tequila can detect them.

Windows

You can in principle use tequila with windows as OS and have almost full functionality. You will need to replace Jax with autograd for it to work. In order to do so: Remove jax and jaxlib from setup.py and requirements.txt and add autograd instead.

In order to install qulacs you will need latest GNU compilers (at least gcc-7). They can be installed for example over visual studio.

Mac OS

Tequila runs on Mac OSX. You might get in trouble with installing qulacs since it currently does not work with Apple's clang compiler. You need to install latest GNU compile (at least gcc-7 and g++7) and set them as default before installing qulacs over pip.