NNQA: Neural-Native Quantum Arithmetic for End-to-End Polynomial Synthesis

TL;DR

NNQA introduces a method to compile classical neural network representations into precise quantum arithmetic circuits, enabling scalable polynomial synthesis on quantum hardware with high accuracy.

Contribution

It provides a theoretical framework for universal quantum polynomial arithmetic and demonstrates practical scalability and accuracy on real quantum devices.

Findings

Achieved over 99.5% accuracy for polynomials up to degree 35.

Demonstrated scalability on IonQ hardware up to 36 qubits and circuit depths of 70.

Error primarily limited by device noise, not the method itself.

Abstract

Hybrid classical quantum learning is often bottlenecked by communication overhead and approximation error from generic variational ansatzes. In this study, we introduce Neural Native Quantum Arithmetic (NNQA), which compiles classically learned nonlinear representations into precise quantum arithmetic composed of native unitary blocks. Theoretically, we prove that the universal approximation of quantum polynomial arithmetic can be realized by transforming a classical neural network into a quantum circuit, with the resulting error arising solely from measurement shot noise, thereby extending classical operator-level estimation guarantees into the quantum regime. Empirical validation on IBM Quantum Heron3 and IonQ Forte processors shows performance limited primarily by device noise without variational fine tuning: we achieve over 99.5% accuracy for polynomials up to degree 35 and demonstrate scalability on IonQ hardware up to 36 qubits and circuit depths of 70, reaching a negligible RMSE of 0.005. Overall, NNQA establishes a new paradigm of synthesizing quantum arithmetic for native quantum computation.