qiskit-alt 0.1.7

Julia backend for Qiskit

qiskit_alt

qiskit_alt

This Python package uses a backend written in Julia to implement high performance features for standard Qiskit. This package is a proof of concept with little high-level code.

Installing and managing Julia and its packages is automated. So you don't need to learn anything about Julia to get started.

The highlights thus far are in benchmark code, which is presented in the demonstration notebooks. There is one demonstration notebook using pyjulia and another demonstration notebook using juliacall.

The main application-level demonstration is computing a qubit Hamiltonian as a qiskit.quantum_info.SparsePauliOp from a Python list specifiying the molecule geometry in the same format as that used by qiskit_nature.

• The Jordan-Wigner transform in qiskit_alt is 30 or so times faster than in qiskit-nature.
• Computing a Fermionic Hamiltonian from pyscf integrals is several times faster, with the factor increasing with the problem size.
• Converting an operator from the computational basis, as a numpy matrix, to the Pauli basis, as a qiskit.quantum_info.SparsePauliOp, is many times faster with the factor increasing rapidly in the number of qubits.

You might want to skip to installation instructions

Table of contents

• Motivations

• Demonstration

  • Zapata demo of Jordan-Wigner transformation in Julia; The same thing as the main demonstration in qiskit_alt.

• Installation and configuration notes

  • Compilation Compiling a system image to eliminate compilation at run time.

  • Using qiskit_alt First steps.

  • Manual Steps Details of automatic installation.

• Julia Packages Julia packages that qiskit_alt depends on.

• Troubleshooting

• Communication between Python and Julia Options for the language interface.

• Development. Instructions for developing qiskit_alt.

Demonstration

• There are a few demos in this demonstration benchmark notebook

• The benchmark code is a good place to get an idea of what qiskit_alt can do.

• Here are some demonstration notebooks of the Julia packages behind qiskit_alt.

• Zapata demo of Jordan-Wigner transformation in Julia; The same thing as the main demonstration in qiskit_alt. This is from JuliaCon 2020.

Installation and Configuration Notes

Basic

• qiskit_alt is available on pypi: pip install qiskit_alt

• Complete installation by running import qiskit_alt followed by qiskit_alt.project.ensure_init(). See julia_project for more information.

  • If no Julia executable is found, jill.py will be used to download and install it. It is not necessary to add the installation path or symlink path to your search PATH to use julia from qiskit_alt. Before offering to install Julia, qiskit_alt will search for julia as described here.

  • The Julia packages are installed the first time you run qiskit_alt.project.ensure_init() from Python. If this fails, see the log file qiskit_alt.log and the manual steps below.

• Check that the installation is not completely broken by running benchmark scripts, with the string "alt" in the name:

python ./bench/run_all_bench.py

Note that the folder bench is not included when you install via pip install qiskit_alt. But it can be downloaded here.

More installation details

• qiskit_alt depends on the following three packages. It is probably not necessary to read about them to install qiskit_alt, but might help.

  • pyjulia is used to communicate with Julia. The installation notes may be useful.

  • juliacall.

  • julia_project for managing Julia dependencies.

• pyjulia and juliacall are two packages for communication between Python and Julia. You only need to import one of them. But, you won't import them directly.

• When you initialize with qiskit_alt.project.ensure_init() the default communication package is chosen. You can also choose explicitly with qiskit_alt.project.ensure_init(calljula="pyjulia") or qiskit_alt.project.ensure_init(calljula="juliacall")

• The installation is interactive. How to do a non-interactive installation with environment variables is described below.

• You may allow qiskit_alt to download and install Julia for you, using jill.py. Otherwise you can follow instructions for installing Julia manually.

• We recommend using a virtual Python environment with venv or conda. For example python -m venv ./env, which creates a virtual environment for python packages needed to run qiskit_alt. You can use whatever name you like in place of the directory ./env.

• Activate the environment using the file required for your shell. For example source ./env/bin/activate for venv and bash.

• Install qiskit_alt with pip install qiskit_alt.

• Install whatever other packages you want. For example pip install ipython.

• Configuring installation with environment variables. If you set these environment variables, you will not be prompted during installation.

  • Set QISKIT_ALT_JULIA_PATH to the path to a Julia executable (in a Julia installation). This variable takes precedence over other methods of specifying the path to the executable.
  • Set QISKIT_ALT_INSTALL_JULIA to y or n to confirm or disallow installing julia via jill.py.
  • Set QISKIT_ALT_COMPILE to y or n to confirm or disallow compiling a system image after installing Julia packages
  • Set QISKIT_ALT_DEPOT to y or n to force using or not using a Julia "depot" specific to this project.

• qiskit_alt.project.update() will delete Manifest.toml files; upgrade packages; rebuild the manifest; delete compiled system images. If you call update() while running a compiled system image, you should exit Python and start again before compiling

Compilation

• To speed up loading and reduce delays due to just-in-time compilation, you can compile a system image for qiskit_alt as follows.

[1]: import qiskit_alt

In [2]: qiskit_alt.project.ensure_init()

In [3]: qiskit_alt.project.compile()

Compilation takes about four minutes. The new Julia system image will be found the next time you import qiskit_alt.

• import qiskit_alt takes about 8.5s before compilation, and 1.4s after compilation.

• The code is "exercised" during compilation by running the test suites of some of the included packages. Code paths that are exercised during compilation will suffer no delay in the future, just like statically compiled libraries. More test suites and exercise scripts can be included in the compilation. And more Julia code can be moved from qiskit_alt into compiled modules.

• "compilation" has different meanings in Julia. Code is always precompiled and cached in a .ji file. What happens during precompilation is described here. But, this is not the kind of compilation we are considering here.

Using qiskit_alt

This is a very brief introduction.

• The pyjulia interface is exposed via the julia module. The juliacall module is called juliacall. However you should not do import julia or import juliacall before import qiskit_alt, and qiskit_alt.project.ensure_init() (or qiskit_alt.project.ensure_init(calljulia="pyjulia") or juliacall with qiskit_alt.project.ensure_init(calljulia="juliacall")) This is because import julia will circumvent the facilities described above for choosing the julia executable and the compiled system image.

• Julia modules are loaded like this. Note that qiskit_alt.project.julia points to either julia or juliacall depending on which was chosen.

import qiskit_alt
qiskit_alt.project.ensure_init(calljulia=interface_choice)
Main = qiskit_alt.project.julia.Main

import qiskit_alt; import julia; from julia import PkgName. After this, all functions and symbols in PkgName are available. You can do, for example

In [1]: import qiskit_alt

In [2]: qiskit_alt.project.ensure_init()

In [3]: julia = qiskit_alt.project.julia

In [4]: julia.Main.cosd(90)
Out[4]: 0.0

In [5]: QuantumOps = qiskit_alt.project.simple_import("QuantumOps")

In [6]: pauli_sum = QuantumOps.rand_op_sum(QuantumOps.Pauli, 3, 4); pauli_sum
Out[6]:
<PyCall.jlwrap 4x3 QuantumOps.PauliSum{Vector{Vector{QuantumOps.Paulis.Pauli}}, Vector{Complex{Int64}}}:
IIZ * (1 + 0im)
XYI * (1 + 0im)
YIX * (1 + 0im)
ZIZ * (1 + 0im)>

In the last example above, PauliSum is a Julia object. The PauliSum can be converted to a Qiskit SparsePauliOp like this.

In [7]: from qiskit_alt.pauli_operators import PauliSum_to_SparsePauliOp

In [8]: PauliSum_to_SparsePauliOp(pauli_sum)
Out[8]:
SparsePauliOp(['ZII', 'IYX', 'XIY', 'ZIZ'],
              coeffs=[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j])

Managing Julia packages

• Available Julia modules are those in the standard library and those listed in Project.toml. You can add more packages (and record them in Project.toml) by doing qiskit_alt.project.julia.Pkg.add("PackageName"). You can also do the same by avoiding Python and using the julia cli.

Julia Packages

• The Julia repos QuantumOps.jl and ElectronicStructure.jl and ElectronicStructurePySCF.jl and QiskitQuantumInfo.jl are not registered in the General Registry, but rather in QuantumRegistry which contains just a handful of packages for this project.

Testing

The test folder is mostly out of date.