QUT: A Unit Testing Framework for Quantum Subroutines
- URL: http://arxiv.org/abs/2509.17538v1
- Date: Mon, 22 Sep 2025 08:57:32 GMT
- Title: QUT: A Unit Testing Framework for Quantum Subroutines
- Authors: Mykhailo V. Klymenko, Thong Hoang, Hoa Nguyen, Samuel A. Wilkinson, Bahar Goldozian, Xing Zhenchang, Qinghua Lu, Muhammad Usman, Liming Zhu,
- Abstract summary: We present the architectural design and prototype implementation of QUT (Quantum Unit Testing), a framework for unit testing of quantum subroutines.<n>The framework is developed with a focus on usability and simplicity, making the complex theoretical concepts behind quantum unit testing accessible to a wide range of users.
- Score: 9.792920473518146
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present the architectural design and prototype implementation of QUT (Quantum Unit Testing), a framework for unit testing of quantum subroutines. The framework is developed with a focus on usability and simplicity, making the complex theoretical concepts behind quantum unit testing accessible to a wide range of users with diverse backgrounds. This is achieved through the implementation of polymorphic probabilistic assertions, whose evaluation methods adapt to the data types of the arguments used in assertion statements, which may vary according to the context-dependent semantics of quantum subroutines. These arguments can be represented as qubit measurement outcomes, density matrices, or Choi matrices. For each type, the architecture integrates a specific testing protocol - such as quantum process tomography, quantum state tomography, or Pearson's chi-squared test - while remaining flexible enough to incorporate additional protocols in the future. The framework is built on the Qiskit software stack, providing compatibility with a broad range of quantum hardware backends and simulation platforms. Drawing on the reasoning provided by the denotational semantics of quantum subroutines, this work also highlights the key distinctions between quantum unit testing and its classical counterpart.
Related papers
- Many-body Quantum Score: a scalable benchmark for digital and analog quantum processors and first test on a commercial neutral atom device [0.0]
We propose the Many-body Quantum Score (MBQS) to evaluate the capabilities of quantum processing units (QPUs)<n>MBQS quantifies performance by identifying the maximum number of qubits with which a QPU can reliably reproduce correlation functions of the transverse-field Ising model following a specific quantum quench.
arXiv Detail & Related papers (2026-01-06T23:19:35Z) - A Framework for Quantum Data Center Emulation Using Digital Quantum Computers [4.4249067508724815]
We propose a framework that emulates a distributed quantum computing system using a single quantum processor.<n>We introduce an experimentally grounded noise model based on quantum collision dynamics to quantify the interconnect-induced noise.<n>The framework is validated using IBM's quantum hardware, demonstrating the successful execution of remote gates.
arXiv Detail & Related papers (2025-09-04T09:04:54Z) - Quantum Executor: A Unified Interface for Quantum Computing [46.36953285198747]
Quantum Executor is a backend-agnostic execution engine designed to orchestrate quantum experiments across heterogeneous platforms.<n>Key features include support for asynchronous and distributed execution, customizable execution strategies and a unified API for managing quantum experiments.
arXiv Detail & Related papers (2025-07-10T09:55:32Z) - Context-Aware Unit Testing for Quantum Subroutines [14.117812847408523]
Testing quantum software presents unique challenges due to the non-deterministic nature of quantum information, the high dimensionality of the underlying Hilbert space, complex hardware noise, and the inherent non-local properties of quantum systems.<n>We propose incorporating context-awareness into the testing process to address the computational complexity associated with unit testing in quantum systems.
arXiv Detail & Related papers (2025-06-12T04:58:56Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - Quantum Concolic Testing [5.3611583388647635]
This paper presents the first concolic testing framework explicitly designed for quantum programs.<n>We generate path constraints for each concrete execution path of a quantum program, with a quantum constraint solver determining outcomes to create novel input samples.<n>Our framework has been implemented in Python and integrated with Qiskit for practical evaluation.
arXiv Detail & Related papers (2024-05-08T07:32:19Z) - Testing Multi-Subroutine Quantum Programs: From Unit Testing to Integration Testing [2.8611507672161265]
This paper addresses the specific testing requirements of multi-subroutine quantum programs.
We focus on testing criteria and techniques based on the whole testing process perspective.
We conduct comprehensive testing on typical quantum subroutines, including diverse mutants and randomized inputs.
arXiv Detail & Related papers (2023-06-30T05:31:56Z) - TeD-Q: a tensor network enhanced distributed hybrid quantum machine learning framework [48.491303218786044]
TeD-Q is an open-source software framework for quantum machine learning.<n>It seamlessly integrates classical machine learning libraries with quantum simulators.<n>It provides a graphical mode in which the quantum circuit and the training progress can be visualized in real-time.
arXiv Detail & Related papers (2023-01-13T09:35:05Z) - A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - QuaSiMo: A Composable Library to Program Hybrid Workflows for Quantum
Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-05-17T16:17:57Z) - Scalable Benchmarks for Gate-Based Quantum Computers [5.735035463793008]
We develop and release an advanced quantum benchmarking framework.
It measures the performance of universal quantum devices in a hardware-agnostic way.
We present the benchmark results of twenty-one different quantum devices from IBM, Rigetti and IonQ.
arXiv Detail & Related papers (2021-04-21T18:00:12Z) - Composable Programming of Hybrid Workflows for Quantum Simulation [48.341084094844746]
We present a composable design scheme for the development of hybrid quantum/classical algorithms and for applications of quantum simulation.
We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library.
arXiv Detail & Related papers (2021-01-20T14:20:14Z) - 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)
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.