Extending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation

Abstract

Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly-coupled CPU-QPU architectures with fast-feedback. Here we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardwareagnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Pythonic extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.

Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation

“…qcor provides C++ [20] and Python [26] language extensions for heterogeneous quantum-classical computing in an effort to promote native quantum kernel programming in a single-source context. Critically, qcor puts forward a compiler runtime library that enables quantum program execution in a multi-modal, retargetable fashion.…”

“…A feature that we want to highlight is the fact that Pauli expectation accumulation is explicitly expressed as a for loop (lines [16][17][18][19][20][21][22][23][24][25][26][27][28]. Note the h quantum instruction broadcasts across all qubits in the register.…”