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.
Get started with our collection of
Further sources:
Recommended Python version is 3.9 (3.10).
Tequila supports linux, osx and windows. However, not all optional dependencies are supported on windows.
Do not install like this: (Minecraft lovers excluded)
pip install tequila
You can install tequila from PyPi as:
pip install tequila-basicthis 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 qulacsYou can install tequila directly with pip over:
pip install git+https://github.com/tequilahub/tequila.gitInstall from devel branch (most recent updates):
pip install git+https://github.com/tequilahub/tequila.git@devel# 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")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))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!
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!
Support for specific backends (quantum simulators, optimizers, quantum chemistry) can be activated by intalling them in your environment.
Currently supported
- Qulacs (recommended)
- Qibo -- currently needs to be qibo==0.1.1
- Qiskit
- Cirq
- PyQuil
- QLM (works also whith myQLM)
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.
Currently supported
Psi4.
In a conda environment this can be installed with
conda install psi4 -c conda-forgeHere is a small tutorial that illustrates the usage.
In a conda environment this can be installed with
conda install madtequila -c kottmannThis 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.
Install with
pip install pyscfWorks similar as Psi4. Classical methods are also integrated in the madness interface allowing to use them in a basis-set-free representation.
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
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:
- Make a fork of
tequilato your own github account. - Checkout the
develbranch and make sure it is up to date with the main github repository. - Create and checkout a new branch from
develviagit branch pr-my-branch-namefollowed bygit checkout pr-my-branch-name. By typinggit branchafterwards you can check which branch is currently checked out on your computer. - Introduce changes to the code and commit them with git.
- Push the changes to your github account
- Log into github and create a pull request to the main github repository. The pull-request should be directed to the
develbranch (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).
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.
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.
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"Qiskit version 0.25 is not yet supported.
pip install --upgrade qiskit<0.25 fixes potential issues. If not: Please let us know.
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:
- Use
tq.draw(circuit, backend="qiskit")(orbackend=cirq) - translate to qiskit/cirq and use their functionality (
qiskit_circuit = tq.compile(circuit, backend='qiskit').circuit) - directly create pdfs:
tq.circuit.export_to(circuit, filename="my_name.pdf")(will also createmy_name.qpicthat can be used with qpic) - use
print(circuit)(does not look pretty, but carries the same information). - become a contributor and implement your own graphical circuit representation and create a pull-request.
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.
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.
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.