Qrisp Implementation and Resource Analysis of a T-Count-Optimised Non-Restoring Quantum Square-Root Circuit
- URL: http://arxiv.org/abs/2507.12603v1
- Date: Wed, 16 Jul 2025 19:45:50 GMT
- Title: Qrisp Implementation and Resource Analysis of a T-Count-Optimised Non-Restoring Quantum Square-Root Circuit
- Abstract summary: This paper presents the first complete implementation of the T-count optimized non-restoring quantum square root algorithm using the Qrisp quantum programming framework.<n>Our implementation validates the theoretical resource estimates, confirming a T-count of 14n-14 and T-depth of 5n+3 for n-bit inputs.<n>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.
- Score: 0.3529736140137004
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Verifiable quantum advantage in extremely low depth [52.51019642214249]
We give a problem that is solvable by shallow quantum circuits, hard for computation in time under lattice-based assumptions.<n>We prove that shallow quantum circuits have sufficient structure to solve certain classically hard tasks whose solutions can be verified efficiently.
arXiv Detail & Related papers (2026-09-01T15:54:34Z) - Quantum Non-Moduler Multiplication with QFT-Based Multi Input Parallelized Adder [0.0]
We propose an efficient quantum multiplication approach based on a QFT-assisted parallelized addition scheme.<n>The proposed design eliminates the repeated application of QFT and inverse QFT operations during intermediate summation processes.
arXiv Detail & Related papers (2026-03-31T21:52:58Z) - Reusability Report: Optimizing T-count in General Quantum Circuits with AlphaTensor-Quantum [0.0]
We extend AlphaTensor-Quantum's capabilities to simplify random quantum circuits with varying qubit counts.<n>Our experiments show that a general agent trained on 5- to 8-qubit circuits achieves greater T-count reduction than previous methods for a large fraction of quantum circuits.
arXiv Detail & Related papers (2025-11-13T04:29:51Z) - QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
arXiv Detail & Related papers (2024-10-10T14:24:30Z) - Quantum Circuit Optimization with AlphaTensor [47.9303833600197]
We develop AlphaTensor-Quantum, a method to minimize the number of T gates that are needed to implement a given circuit.
Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets.
Remarkably, it discovers an efficient algorithm akin to Karatsuba's method for multiplication in finite fields.
arXiv Detail & Related papers (2024-02-22T09:20:54Z) - A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for
Non-Ideal Training Sets [15.53642141764581]
A novel efficient quantum perceptron algorithm based on unitary weights is proposed.
The example validation of quantum gates H, S, T, CNOT, Toffoli, Fredkin shows that our algorithm can accurately implement arbitrary quantum gates within one iteration.
arXiv Detail & Related papers (2023-09-23T15:24:41Z) - Quantum Circuit Optimization of Arithmetic circuits using ZX Calculus [0.0]
We propose a technique to optimize quantum arithmetic algorithms by reducing the hardware resources and the number of qubits based on ZX calculus.
We are able to achieve a significant reduction in the number of ancilla bits and T-gates as compared to the originally required numbers to achieve fault-tolerance.
arXiv Detail & Related papers (2023-06-04T05:05:57Z) - Semantic embedding for quantum algorithms [0.0]
A need has developed for an assurance of the correctness of high-level quantum algorithmic reasoning.
Many quantum algorithms have been unified and improved using quantum signal processing (QSP) and quantum singular value transformation (QSVT)
We show that QSP/QSVT can be treated and combined modularly, purely in terms of the functional transforms they embed.
We also identify existing quantum algorithms whose use of semantic embedding is implicit, spanning from distributed search to soundness in quantum cryptography.
arXiv Detail & Related papers (2023-04-27T17:55:40Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Synthesis of Quantum Circuits with an Island Genetic Algorithm [44.99833362998488]
Given a unitary matrix that performs certain operation, obtaining the equivalent quantum circuit is a non-trivial task.
Three problems are explored: the coin for the quantum walker, the Toffoli gate and the Fredkin gate.
The algorithm proposed proved to be efficient in decomposition of quantum circuits, and as a generic approach, it is limited only by the available computational power.
arXiv Detail & Related papers (2021-06-06T13:15:25Z) - Quantum Gate Pattern Recognition and Circuit Optimization for Scientific
Applications [1.6329956884407544]
We introduce two ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL.
AQCEL is deployed on an iterative and efficient quantum algorithm designed to model final state radiation in high energy physics.
Our technique is generic and can be useful for a wide variety of quantum algorithms.
arXiv Detail & Related papers (2021-02-19T16:20:31Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.