Compilation of Qcrank Encoding Algorithm for a Dynamically Programmable Qubit Array Processor
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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, multizone 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.