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Journal of Student Research

Investigation of Quantum Adder Error Rate on IBM Quantum Systems

Cayden Tu, Anthony J. Hoffman

Published Aug 31, 2022• 0 citations
Publisher

Abstract

This paper reports the results of an investigation into the performance of quantum addition operations carried out by two quantum adders. These algorithms were run on IBM quantum computers and simulated with a noise model on Qiskit. Following this experimentation, we studied the rates of errors and the differences between the two adders. This study illuminated the benefits and drawbacks of the adders as well as the capabilities of a noise simulation consisting of coupling and basis gates noise to emulate real hardware. Our work provides additional data to inform the development of more efficient addition algorithms aimed at helping minimize error rates.

DOI
10.47611/jsrhs.v11i3.4009

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This paper reports the results of an investigation into the performance of quantum addition operations carried out by two quantum adders. These algorithms were run on IBM quantum computers and simulated with a noise model on Qiskit. Following this experimentation, we studied the rates of errors and the differences between the two adders. This study illuminated the benefits and drawbacks of the adders as well as the capabilities of a noise simulation consisting of coupling and basis gates noise to emulate real hardware. Our work provides additional data to inform the development of more efficient addition algorithms aimed at helping minimize error rates.
Quantum Fourier Transform (QFT) is an essential algorithm for quantum computers. There are many uses of QFT in the application of quantum computing. In this work, we proposed a generalized adder circuit that was fundamental for QFT. We designed and ran the experiments with the proposed adder circuit on an IBM quantum computer facility. We observed that the number of qubits was one factor in the error rate. We found that our proposed two-qubits adder circuit running on the IBM quantum computer had an error rate of around 25%. The complexity of the adder circuit includes qubit connectivity, physical devices, and error from noise due to the environment. We demonstrated the constraints of the proposed adder circuit.
We explore the field of quantum computing and specifically address the crucial problem of error reduction in the context of IBM simulators. Although quantum computing is a rapidly developing technology with great potential, its practical applications are severely hindered by its inherent error-proneness. We tackle this problem by introducing new developments in quantum error correction techniques designed for IBM simulators. In contrast to conventional methods that frequently depend on large matrices or pre-established error models; our technique presents a flexible and dynamic approach. By utilizing several variations of a main circuit, in which classically efficient gates are used in place of non-Clifford gates, we are able to discover the best compensation scheme from scratch for actual quantum systems. The suggested method exhibits great flexibility in handling different levels of noise and different kinds of noise, such as spatially and temporally correlated variations. It also effectively reduces errors on IBM quantum hardware as well as precisely emulated imperfect quantum computers. By offering a thorough investigation of sophisticated error mitigation techniques, this research advances the continuing quest for dependable quantum computations and opens up interesting new directions for improving both the accuracy and performance of quantum computing operations carried out on IBM simulators.
Abstract This practical guide presents methods for constructing noise models using the Qiskit Aer noise module within the IBM Q ecosystem. The objective is to simulate quantum circuits under realistic error conditions, enabling accurate emulation of noisy quantum processors. The guide demonstrates how to: Use the Qiskit Aer noise module to define custom noise models. Incorporate gate errors, readout errors, and decoherence effects into simulations. Apply noise channels (e.g., depolarizing, amplitude damping, phase damping). Attach noise models to quantum circuit simulations. Analyze how noise affects algorithm performance and output distributions. Building noise models is essential for studying the behavior of quantum algorithms in the NISQ (Noisy Intermediate-Scale Quantum) era. It allows researchers to evaluate algorithm robustness, optimize circuit depth, and test error mitigation strategies before execution on real IBM Quantum hardware. This approach provides a bridge between ideal theoretical circuits and physically realistic quantum devices, enhancing the reliability and applicability of quantum computing experiments.
Quantum computing has gained a lot of interest recently because of its claims of solving some problems that are almost impossible solving in classical computing.Prime factorization of big numbers using Shor's algorithm and Grover database search algorithm are two of them.Furthermore, quantum algorithms are developed to make it more realistic in real world.In classical computing the basic unit of computation is a bit which is in 0 or 1 state at a time, whereas in quantum computing it is called a qbit which can be 0 and 1 at the same time and we call it superposition.This paper proposes an implementation of quantum full adder using IBM quantum experience.Quantum circuits are drawn using Qiskit and run on Qiskit simulators, IBM quantum simulators and real IBM quantum machines.Proposed circuit will help in future for drawing more complex circuits and algorithms.
Quantum computing is a transformative technology providing exponential speedup over classical computing for specific computational problems. At the foundation of computing lies arithmetic, with addition as a fundamental operation for subtraction, multiplication, division, and other operations. In this paper, we evaluate the performance of quantum arithmetic circuits – including half adders, full adders, Toffoli-based adders, Quantum Fourier Transform (QFT)-based adders, and quantum comparators – on IBM’s Eagle Processor, a 127-qubit superconducting Quantum Processing Unit (QPU) accessible through the IBM Quantum cloud platform. These circuits are implemented using IBM Qiskit and tested on both ideal and noisy simulators to validate their functionality before execution on quantum hardware. Experimental results show that the probabilities of accurate computations are notably low, even for smaller input sizes, due to hardware imperfections such as shorter qubit coherence times and limited qubit stability. A comparative analysis of Toffoli-based and QFT-based adders demonstrates that Toffoli-based adders have higher accuracy, making them more suitable for precise quantum arithmetic operations. These findings highlight the significant impact of quantum hardware imperfections on the reliability of arithmetic operations.
Abstract This practical guide explores the use of IBM Quantum cloud simulators for modeling and analyzing quantum circuits under realistic conditions. Using the IBM Q platform and Qiskit Runtime, the work demonstrates how to configure the ibmq_qasm_simulator and map a basic noise model derived from a physical IBM Quantum™ device. The guide focuses on: Setting up the ibmq_qasm_simulator in Qiskit. Extracting and applying a hardware-based noise model from a real IBM Quantum backend. Performing noisy simulations of QuantumCircuits. Using Sampler and Estimator primitives in Qiskit Runtime to analyze circuit outputs. Studying the impact of realistic quantum errors such as decoherence, gate infidelity, and readout noise. By incorporating noise models into simulations, researchers and developers can better understand how quantum circuits behave on real hardware, optimize algorithms before deployment, and design more robust quantum programs. This approach bridges the gap between idealized simulations and physical quantum devices, making it a critical step toward practical quantum application development.