QLEO (Quobly Logical Emulator Online): Python Interface for Quantum Circuits

Welcome to QLEO, a Python library designed for building, simulating, and analyzing quantum circuits powerd by MIMIQ emulator of QPerfect.

If you encounter any errors or bugs while using QLEO, please report them by opening an issue in the QLEO repository here.
Note: This package uses a proprietary C++ backend that is not part of the public repository.

Installation

Install the library directly from the Quobly GitHub repository:

pip install qleo

Optional Dependencies

To enable visualization feature, ensure you install:

pip install qleo[visualization]

Getting Started

Start by importing the qleo library. This library provides tools for creating, manipulating, and simulating quantum circuits.

1. Import the Library

   By using from qleo import *, you gain access to all classes and methods required for quantum circuit operations.

   from qleo import *
   

2. Create and Manipulate Circuits In this step, you create a quantum circuit with Circuit() and add gates to it. Hadamard Gate (GateH): Places each qubit in a superposition state. Controlled-Z Gate (GateCZ): Introduces entanglement between qubits by applying a phase shift if both qubits are in the |1⟩ state.

   n = 10
   c = Circuit()
   c.push(GateH(), range(n))
   c.push(GateCZ(), range(n-1), range(1,n))
   c.push(GateCZ(), 0, n-1)
   

3. Execute & Get Result Here, you execute the circuit on QuoblySimulator (Simulates the quantum circuit using a state vector approach) and retrieve the result.

   processor = Qleo()
   result = processor.execute(c, nsamples=1000)
   
   print(result)
   

   The result contains detailed information about the circuit execution:

   QCSResults:
   ├── simulator: Quantanium StateVector 1.0
   ├── timings:
   │    ├── apply time: 0.000318101s
   │    └── sample time: 0.000277592s
   ├── fidelity estimate: 1
   ├── average multi-qubit gate error estimate: 0
   ├── most sampled:
   │    ├── bs"0100001000" => 5
   │    ├── bs"0000000000" => 5
   │    ├── bs"1001010001" => 5
   │    ├── bs"0111011110" => 5
   │    └── bs"0010001010" => 4
   ├── 1 executions
   ├── 0 amplitudes
   └── 1000 samples
   

   The output provides a summary of your simulation results, including key information about the backend used, execution details, and measurement outcomes. You'll find details such as the simulator type, timings for circuit execution and measurement, fidelity estimates, and the most frequently observed quantum states. Additionally, it includes the total number of samples and any specific execution metrics for analyzing the performance and accuracy of your quantum circuit.

4. Get Histogram of the samples The result.histogram() method generates a counts distribution of the sampled measurement outcomes.

   result.histogram()
   

5. Visualizing the results To visualize the results of your quantum circuits, you can use the plothistogram function provided by the qleo.visualization module.

   Example

   from qleo.visualization import plothistogram
   plothistogram(result)
   

   

Documentation

The Quobly Framework is based on MIMIQ by QPerfect. Comprehensive documentation, tutorials, and examples on how to build circuits with the Quobly Framework and MIMIQ are available at docs.qperfect.io. Please remember to substitute any occurrences of mimiqcircuits in the documentation with quoblyframwork. For example, in place of from mimiqcircuits import *, use from qleo import *.

GPU support

Qleo supports GPU-accelerated simulation through NVIDIA’s cuStateVec library. This feature is currently available only on Linux systems equipped with an NVIDIA GPU.

To run a simulation on the GPU, use the following command:

sim = Qleo(use_gpu=True)
sim.execute(circuit)

Contact

For issues, suggestions, or inquiries, reach out via:

• Suggestions: contact (Quobly) or contact (QPerfect)
• Email: support (QPerfect)