Compilation of QCrank Encoding Algorithm for a Dynamically Programmable Qubit Array Processor

TL;DR

This paper presents a hardware-aware compilation approach for quantum data encoding using QCrank on neutral atom-based DPQAs, demonstrating promising accuracy and efficiency for near-term quantum hardware.

Contribution

It introduces a novel encoding protocol tailored for DPQAs, integrating hardware features into compilation and evaluating its performance with realistic noise models.

Findings

QCrank achieves accurate encoding of real data in 6-20 qubits.

DPQAs show promising accuracy scaling compared to other quantum hardware.

Hardware-aware compilation improves efficiency and fidelity in quantum data storage.

Abstract

Algorithm and hardware-aware compilation co-design is essential for the efficient deployment of near-term quantum programs. We present a compilation case-study implementing QCrank -- an efficient encoding protocol for storing sequenced real-valued classical data in a quantum state -- targeting neutral atom-based Dynamically Programmable Qubit Arrays (DPQAs). We show how key features of neutral-atom arrays such as high qubits count, operation parallelism, multi-zone architecture, and natively reconfigurable connectivity can be used to inform effective algorithm deployment. We identify algorithmic and circuit features that signal opportunities to implement them in a hardware-efficient manner. To evaluate projected hardware performance, we define a realistic noise model for DPQAs using parameterized Pauli channels, implement it in Qiskit circuit simulators, and assess QCrank's accuracy for writing and reading back 24-320 real numbers into 6-20 qubits. We compare DPQA results with simulated performances of Quantinuum's H1-1E and with experimental results from IBM Fez, highlighting promising accuracy scaling for DPQAs.