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@@ -65,12 +65,12 @@ you can generate .qasm files to use them as subroutines in your own circuits.
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### Loading Algorithms as PyQASM Modules
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To load an algorithm as a PyQASM module, use the `load_algorithm` function from the `qbraid_algorithms` package, passing algorithm-specific parameters. For example, to load the Quantum Fourier Transform (QFT) algorithm:
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To load an algorithm as a PyQASM module, use the `generate_program` function from the `qbraid_algorithms` package, passing algorithm-specific parameters. For example, to load the Quantum Fourier Transform (QFT) algorithm:
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```python
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from qbraid_algorithms import qft
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qft_module = qft.load_algorithm(3) # Load QFT for 3 qubits
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qft_module = qft.generate_program(3) # Load QFT for 3 qubits
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```
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Now, you can perform operations with the PyQASM module, such as unrolling, and
Python package for utilizing, implementing, and building quantum algorithms in OpenQASM 3.
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`qBraid Algorithms <https://docs.qbraid.com/qbraid-algorithms/user-guide/overview>`_ is a Python package designed for quantum algorithm development, implementation, and execution. Built on the `OpenQASM3 <https://openqasm.com/>`_ standard, this library provides researchers, developers, and quantum computing enthusiasts with a robust toolkit for exploring and deploying quantum algorithms across various domains.
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**Key Features:**
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* **Comprehensive Algorithm Library**: Implementation of fundamental quantum algorithms including Grover's search, Quantum Fourier Transform (QFT), Quantum Phase Estimation (QPE), and advanced techniques like amplitude amplification and Hamiltonian evolution.
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* **OpenQASM 3 Integration**: Native support for OpenQASM 3, enabling seamless integration with modern quantum hardware and simulators while maintaining compatibility with the evolving quantum computing ecosystem.
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* **Modular Architecture**: Clean, modular design that allows for easy extension, customization, and integration into existing quantum computing workflows.
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* **Research-Ready Implementation**: Optimized for both educational purposes and cutting-edge research, with implementations suitable for near-term quantum devices (NISQ era) and fault-tolerant quantum computers.
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* **Hardware Agnostic**: Designed to work across different quantum computing platforms and simulators, providing flexibility in deployment and testing.
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Installation
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-------------
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Install from Source
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^^^^^^^^^^^^^^^^^^^^
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You can also install from source by cloning this repository and running a pip install command in the root directory of the repository:
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