Introduction
The alqorithms package is part of the HQS Quantum Libraries, which contain the modules alqorithms, qonvert, noise-mapper and the bath-mapper, as shown in Figure 1.
Figure 1: HQS Quantum Libraries
alqorithms is a powerful package which implements the generation of circuits from Hamiltonians. With advanced algorithms such as the CNOT algorithm, the QSWAP algorithm, and the Mølmer-Sørensen algorithm, alqorithms provides seamless circuit generation capabilities. Whether the user has a pure system Hamiltonian or a system-bath Hamiltonian, the generated circuits can be executed on a quantum computer using our qoqo-backends.
Functionality
The current supported algorithms in the HQS alqorithms package are
- the CNOT algorithm, which assumes qubits with all-to-all connectivity,
- the Mølmer-Sørensen-Algorithm, which is similar to the CNOT algorithm but implements a different two-qubit gate,
- and the QSWAP algorithm, which assumes qubits with linear connectivity.
All of these algorithms can take a pure system Hamiltonian or a system-bath Hamiltonian, and the generated circuit can be run on a quantum computer using the HQS open source qoqo-backends.
Furthermore, alqorithms includes tools to create quantum circuits that prepare measurements, which are essential to read out information in quantum computing algorithms. Additionally, the HQS package provide functionalities to perform quantum simulations of two-point correlators in the infinite temperature state. The user can also apply a symmetrization on a spin system to simplify the computation.
The core alqorithms package is written in Rust, and features a Python interface named py-alqorithms. Thus, it combines the performance and memory safety benefits of Rust with the user-friendly accessibility of Python. Rust ensures high-efficiency and reliable execution. The Python interface makes the package usable for a broader audience from the Python's extensive ecosystem, while leveraging the Rust-based core. This setup offers the best of both worlds—high performance at the core and easy integration and prototyping through Python.
API Documentation
The API documentation can be found at the following link: