A toolbox that analyses loss landscapes by combining metrics, ansatz characteristics and ZX-calculus
The code contained in this repository requires the following dependencies:
- matplotlib==3.5.2
- networkx==2.8.8
- numpy==1.24.1
- orqviz==0.5.0
- PennyLane==0.27.0
- scipy==1.13.1
- torch==2.2.0
- cirq==0.13.1
- pyquil==3.0.1
- qiskit==1.2.4
- qiskit-aer==0.14.1
- tensorflow==2.15.0
- Flask==2.1.2
- flask-smorest==0.39.0
- ply==3.11
- qiskit_qasm3_import
- marshmallow~=3.23.1
- sympy~=1.13.3
- tqdm~=4.67.0
Install dependencies using pip install -r requirements.txt
Python 3.9.13 is the version compatible with the dependencies.
Steps to be performed to run the application using docker.
Prerequisites: Installed docker and docker compose. On Windows you can easily install Docker Desktop, which includes both.
Start the corresponding container by executing:
docker compose up
If changes were made, execute:
docker compose up --build
To forward port 8000 of the container to port 8000 of your host computer, run:
docker run -p 8000:8000 qmltoolbox
The OpenAPI documentation can be accessed at http://localhost:8000/api/swagger-ui
-
circuitName
must be one of:introExample
,iqpExample
,sim1
,sim2
,sim9
,sim10
,sim11
,sim12
,sim15
,iqp1
,iqp2
,iqp3
. Here,introExample
andiqpExample
are the example circuits we discuss in Sections 5.4.1 and 5.5.4 respectively. -
pauliString
represents the measurement Hamiltonian, for exampleZXIIYX
. Should have lengthnumQubits
. -
parameterIdx
is the parameter with regards to which the derivative is analysed. Counting starts at 0.
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