Tequila

Enables high-level definition of quantum circuits, Hamiltonians, and objectives using Python objects, allowing researchers to prototype algorithms without backend-specific code.

Built-in gradient computation for quantum objectives facilitates efficient optimization with classical methods like BFGS, crucial for variational algorithms.

Supports execution on various simulators and hardware (e.g., Qulacs, Qiskit, Cirq) with automatic detection, enabling flexible testing across platforms.

Provides seamless interfaces to chemistry backends like Psi4, PySCF, and Madness for molecular Hamiltonian generation and ansatz design, tailored for VQE applications.