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International Conference on Communications, Information, Electronic and Energy Systems (CIEES)

Simple Quantum Probability Amplitude Modulation Test on an IBM Quantum Computer

Darin Peev

Published Nov 26, 2025• 0 citations
Publisher

Abstract

Quantum Probability Amplitude Modulation (QPAM) was implemented on a real quantum computer, ibm_brisbane, one of IBM’s 127-qubit devices from the Eagle r3 family. To evaluate the errors, advantages, and limitations of such a real-world implementation, four experiments were conducted. Three of them encoded different signals: a ramp signal, a jagged signal, and a custom signal constructed from a sequence of five quantum gates. The first two signals were prepared using a built-in function from IBM’s Qiskit library, which includes an optimization step. Despite the relatively high root-mean-square errors normalized to the signal range (RMSEN) observed in the measurements, the encoding of NSanalog samples with only log2NSqubits was successfully demonstrated for NS= 8 using three qubits. The maximum RMSEN error obtained from the real quantum hardware was 8.7%. However, the need for a disproportionately large number of measurement shots to extract classical information significantly limits the practical applicability of the QPAM method, unless further quantum signal processing is performed within the quantum system.

DOI
10.1109/ciees66347.2025.11300230

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Quantum Probability Amplitude Modulation (QPAM) was implemented on a real quantum computer, ibm_brisbane, one of IBM’s 127-qubit devices from the Eagle r3 family. To evaluate the errors, advantages, and limitations of such a real-world implementation, four experiments were conducted. Three of them encoded different signals: a ramp signal, a jagged signal, and a custom signal constructed from a sequence of five quantum gates. The first two signals were prepared using a built-in function from IBM’s Qiskit library, which includes an optimization step. Despite the relatively high root-mean-square errors normalized to the signal range (RMSEN) observed in the measurements, the encoding of NSanalog samples with only log2NSqubits was successfully demonstrated for NS= 8 using three qubits. The maximum RMSEN error obtained from the real quantum hardware was 8.7%. However, the need for a disproportionately large number of measurement shots to extract classical information significantly limits the practical applicability of the QPAM method, unless further quantum signal processing is performed within the quantum system.
Quantum Amplitude Estimation (QAE) is a fundamental component of many quantum computing algorithms and applications. The practical implementation of QAE workflows on current Noisy Intermediate-Scale Quantum (NISQ) platforms requires accurate probability amplitude representation and reliable measurement recovery. Understanding the behavior of individual computational stages is therefore essential for improving the overall performance of practical quantum estimation procedures. This study presents a component-wise validation of the amplitude encoding process within a controlled quantum simulation environment. The experimental implementation was developed using the Qiskit framework, where classical probability values were encoded into single-qubit quantum states through parameterized rotation operations and subsequently recovered from measurement statistics. A systematic evaluation was performed across the probability interval from 0.1 to 0.9 using 8192 measurement shots for each experiment. The experimental results demonstrate that the recovered measurement probabilities closely match the corresponding target values throughout the investigated domain. Statistical evaluation produced a Mean Absolute Error (MAE) of 0.003521, a Mean Squared Error (MSE) of 1.67 × 10⁻⁵, and a Root Mean Squared Error (RMSE) of 0.004087, indicating high numerical consistency under ideal simulation conditions. The findings suggest that the amplitude encoding stage accurately represents classical probability values and provides a reliable baseline for studying more advanced quantum estimation workflows. The component-wise validation methodology adopted in this work offers a practical framework for investigating individual stages of quantum algorithms and supports future research on hybrid quantum-classical processing strategies for NISQ-era quantum computing.
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.
Nowadays, quantum computing has emerged as a global technology trend, attracting active research worldwide. One of the most popular quantum algorithms is the quantum search algorithm, commonly known as Grover's algorithm, for unstructured search purposes. This paper presents the implementation of the algorithm with a search space of 3 to 10 qubits using the Python-based Qiskit tool developed by IBM. The implementation results on the IBM QSAM simulator and on the IBM Eagle r3 quantum hardware are analyzed and compared. This paper also provides results with circuits of varying iteration numbers. Finally, the paper discusses the advantages of this quantum search algorithm compared to traditional methods, and the obstacles towards realizing its hardware implementation.
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware.
Abstract In this report, we explore benchmarking gate-based quantum devices based on the certification of quantum measurements, which can be viewed as an extension of quantum hypothesis testing. Here, we are interested in a binary certification scheme in which the null and alternative hypotheses are qubit von Neumann measurements. The certification procedure can be used to evaluate the accuracy of Noisy Intermediate-Scale Quantum devices. To support this methodology, we contribute benchmarks to PyQBench, an open-source Python library for benchmarking gate-based quantum computers. We provide a user-friendly command-line interface for certifying measurements from the Fourier family. For more advanced scenarios, PyQBench can also be used as a Python library, allowing users to define custom measurement schemes and error models. The latest version of PyQBench is designed to support IBM Q devices through integration with the Qiskit library, enabling seamless benchmarking on real quantum hardware. By providing flexible benchmarking tools, PyQBench addresses the critical need for reliable performance metrics in the NISQ era, contributing to the development of error mitigation techniques and the verification of quantum measurement fidelity.
Classical computational fluid dynamics (CFD) methods encounter significant computational bottlenecks in large systems and at high Reynolds number flows due to the rapid increase in degrees of freedom, even on flagship supercomputers. As quantum computers emerge, the Quantum Lattice Boltzmann Method (QLBM) has been proposed as a quantum-native alternative for solving fluid dynamics problems. However, until recently, nearly all studies have relied on classical quantum simulators rather than real quantum hardware. This study presents a benchmark of a one-dimensional QLBM implementation executed on three different IBM Quantum hardware. We design and test a quantum circuit for the advection-diffusion equation using amplitude encoding, a Bhatnagar-Gross-Krook (BGK)-type collision operator, and a streaming step with periodic boundary conditions. Our numerical experiments evaluate transpiled two-qubit gate count, total circuit depth, runtime, and accuracy metrics including maximum absolute error (L∞L_\inftyL∞​ norm) and root-mean-square error (RMSE). We find that the present collision and streaming operators dominate the overall cost and become the primary scalability bottleneck on current hardware. To mitigate the prohibitive cost of full quantum state tomography on hardware-based benchmarking exercises, we introduce a sampler-based measurement strategy that reconstructs macroscopic density fields directly from computational-basis measurements. This approach substantially reduces measurement overhead while preserving the expected advection-diffusion dynamics on hardware. These results provide a quantitative assessment of QLBM performance in the noisy intermediate-scale quantum (NISQ) regime and identify the principal algorithmic and experimental limitations that must be addressed for QLBM to become viable for fluid dynamics.