Skip to content

Latest commit

 

History

History
153 lines (108 loc) · 5.75 KB

index.md

File metadata and controls

153 lines (108 loc) · 5.75 KB

Welcome to EMU-MPS

EMU-MPS is a Pulser backend, designed to EMUlate the dynamics of programmable arrays of neutral atoms, with matrix product states (MPS). MPSs are a way of encoding quantum states such that the memory required to represent the wavefunction depends on its entanglement, roughly, how much quantum information is stored in it. For product states, an MPS only takes d*N numbers to store the state for N d-level qudits, while for maximally entangled states, it'll take a multiple of the memory in a state vector. For systems of interest, MPSs are expected to be more efficient than state-vectors, allowing the user to simulate more qubits. For more information, see Tensor Network. EMU-MPS is built on PyTorch, and in the future we intend to make it differentiable.

Setup

You can install from source, or download the package from the private pypi registry that pasqal maintains in gitlab. For developers, we recommend installing from source, for users we recommend installing from the registry.

Warning: installing emu-mps will update pulser-core

We always recommend using a virtual environment.

Click me to see how it is done

Creating a virtual environment using python:

python -m venv .venv

Or

python -m venv /path/to/new/virtual/environment

Replace /path/to/new/virtual/environment with your desired directory path.

Then activate the environment On linux or MacOS

source /path/to/new/virtual/environment/bin/activate

While on Windows it's

C:\> /path/to/new/virtual/environment/Scripts/activate

Remember to replace /path/to/new/virtual/environment with the actual path to your virtual environment. Once the environment is activated, you can clone emu_mps and install it using

installing from the registry

When pip is configured to know about the pasqal registry, EMU-MPS installs as

pip install emu-mps

When pip is not already configured, the easiest way to do so, is to add a file ~/.config/pip/pip.conf

containing the following:

[global]
extra-index-url=https://gitlab.pasqal.com/api/v4/projects/597/packages/pypi/simple
                possible.other.urls

The repo above is publically accessible without authentication.

It is also possible to add the extra-index-url to the pip install command directly, if you somehow don't want to create a pip.conf file.

installing from source

git clone this repository or download

Then, cd into the root folder of the repo and type

pip install -e .
Guidelines for developers We recommend using an environment, git clone the repository, then inside the `emu_mps` folder 
pip install -e .

Also, the installation of pytest, nbmake, pre-commit.

Do not forget to run the unit test suite by simply running pytest command.

Another way can be using hatch.

virtual environment with hatch

python -m pip install hatch
python -m hatch -v shell

When inside the shell with development dependencies, install first the pre-commit hook:

pre-commit install

Running a Pulser sequence and getting results

Several example notebooks are included in the online documentation. The index page for them can be found here.

Supported features

The following features are currently supported:

  • All Pulser sequences that use only the rydberg channel
  • MPS and MPO can be constructed using the abstract Pulser format.
  • The following noise types:
  • The following basis states in a sequence:
  • The following properties from a Pulser Sequence are also correctly applied:
  • Customizable output, with the folowing inbuilt options:
    • The quantum state in MPS format
    • Bitstrings
    • The fidelity with respect to a given state
    • The expectation of a given operator
    • The qubit density (magnetization)
    • The correlation matrix
    • The mean, second moment and variance of the energy
  • Specification of
    • initial state
    • various precision parameters
    • whether to run on cpu or gpu(s)
    • the $U_{ij}$ coefficients from here
    • A cutoff below which $U_{ij}$ are set to 0 (this makes the computation more memory efficient)

Planned features

  • Parallel TDVP on multiple GPUs
  • More noise:
    • the currently unsupported noises in the Pulser NoiseModel
  • Differentiability

More Info

Please see the API specification for a list of available config options (see here), and the Observables page for how to compute observables (see here). Those configuration options relating to the mathematical functioning of the backend are explained in more detail in the Config page (see here). For notebooks with examples for how to do various things, please see the notebooks page (see here).