PHOENIX: Pauli-Based High-Level Optimization Engine for Instruction Execution on NISQ Devices
For quantum computing researchers and practitioners, PHOENIX provides a more efficient compilation method for near-term quantum programs, significantly improving resource utilization on NISQ devices.
PHOENIX is a compilation framework for variational quantum algorithms that operates on Pauli-based intermediate representation, achieving up to 50% reduction in circuit depth and 40% reduction in gate count compared to state-of-the-art compilers across various benchmarks and hardware topologies.
Variational quantum algorithms (VQA) based on Hamiltonian simulation represent a specialized class of quantum programs well-suited for near-term quantum computing applications due to its modest resource requirements in terms of qubits and circuit depth. Unlike the conventional single-qubit (1Q) and two-qubit (2Q) gate sequence representation, Hamiltonian simulation programs are essentially composed of disciplined subroutines known as Pauli exponentiations (Pauli strings with coefficients) that are variably arranged. To capitalize on these distinct program features, this study introduces PHOENIX, a highly effective compilation framework that primarily operates at the high-level Pauli-based intermediate representation (IR) for generic Hamiltonian simulation programs. PHOENIX exploits global program optimization opportunities to the greatest extent, compared to existing SOTA methods despite some of them also utilizing similar IRs. Experimental results demonstrate that PHOENIX outperforms SOTA VQA compilers across diverse program categories, backend ISAs, and hardware topologies.