Performance Evaluations of Signed and Unsigned Noisy Approximate Quantum Fourier Arithmetic

TL;DR

This paper evaluates the performance of signed and unsigned noisy approximate quantum Fourier arithmetic on IBM's quantum hardware, analyzing how noise and superposition affect quantum addition and multiplication accuracy.

Contribution

It provides a comprehensive performance analysis of QFT-based arithmetic operations, including signed and unsigned variants, under realistic noise models and identifies optimal approximation depths.

Findings

Optimal approximation depths depend on noise levels and superposition size.

Signed and unsigned quantum addition show different sensitivities to noise.

Performance trends suggest pathways for improving quantum arithmetic under realistic conditions.

Abstract

The Quantum Fourier Transform (QFT) grants competitive advantages, especially in resource usage and circuit approximation, for performing arithmetic operations on quantum computers, and offers a potential route towards a numerical quantum-computational paradigm. In this paper, we utilize efficient techniques to implement QFT-based integer addition and multiplications. These operations are fundamental to various quantum applications including Shor's algorithm, weighted sum optimization problems in data processing and machine learning, and quantum algorithms requiring inner products. We carry out performance evaluations of these implementations based on IBM's superconducting qubit architecture using different compatible noise models. We isolate the sensitivity of the component quantum circuits on both one-/two-qubit gate error rates, and the number of the arithmetic operands' superposed integer states. We analyze performance, and identify the most effective approximation depths for unsigned quantum addition and quantum multiplication within the given context. We then perform a similar analysis of signed addition and compare to the unsigned results. We observe significant dependency of the optimal approximation depth on the degree of machine noise and the number of superposed states in certain performance regimes. Finally, we elaborate on the algorithmic challenges - relevant to signed, unsigned, modular and non-modular versions - that could also be applied to current implementations of QFT-based subtraction, division, exponentiation, and their potential tensor extensions. We analyze the performance trends in our results and speculate on possible future developments within this computational paradigm.