Qrisp Implementation and Resource Analysis of a T-Count-Optimised Non-Restoring Quantum Square-Root Circuit

TL;DR

This paper demonstrates the first practical implementation of a T-count optimized quantum square root algorithm in Qrisp, validating theoretical resource estimates and showcasing the feasibility of resource-efficient quantum arithmetic operations.

Contribution

It provides the first complete implementation of a T-count optimized quantum square root algorithm using Qrisp, confirming theoretical resource estimates and enabling practical quantum arithmetic.

Findings

Validated T-count of 14n-14 and T-depth of 5n+3 for n-bit inputs.

Successfully translated the algorithm into executable quantum code.

Confirmed correctness through experimental validation.

Abstract

Efficient quantum arithmetic operations are essential building blocks for complex quantum algorithms, yet few theoretical designs have been implemented in practical quantum programming frameworks. This paper presents the first complete implementation of the T-count optimized non-restoring quantum square root algorithm using the Qrisp quantum programming framework. The algorithm, originally proposed by Thapliyal et al., offers better resource efficiency compared to alternative methods, achieving reduced T-count and qubit requirements while avoiding garbage output. Our implementation validates the theoretical resource estimates, confirming a T-count of 14n-14 and T-depth of 5n+3 for n-bit inputs. The modular design approach enabled by Qrisp allows construction from reusable components including reversible adders, subtractors, and conditional logic blocks built from fundamental quantum gates. The three-stage algorithm - comprising initial subtraction, iterative conditional addition/subtraction, and remainder restoration is successfully translated from algorithmic description to executable quantum code. Experimental validation across multiple test cases confirms correctness, with the circuit producing accurate integer square roots and remainders. This work demonstrates the practical realizability of resource-optimized quantum arithmetic algorithms and establishes a foundation for implementing different arithmetic operations in modern quantum programming frameworks.