Quantum Program Testing Through Commuting Pauli Strings on IBM's Quantum Computers

• URL: http://arxiv.org/abs/2408.00501v1
• Date: Thu, 1 Aug 2024 12:10:10 GMT
• Title: Quantum Program Testing Through Commuting Pauli Strings on IBM's Quantum Computers
• Authors: Asmar Muqeet, Shaukat Ali, Paolo Arcaini,
• Abstract summary: We present QOPS, a novel quantum software testing approach. QOPS introduces a new definition of test cases based on Pauli strings to improve compatibility with different quantum programs. We empirically evaluate QOPS on 194,982 real quantum programs, demonstrating effective performance in test assessment compared to the state-of-the-art with a perfect F1-score, precision, and recall.
• Score: 6.925738064847176
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The most promising applications of quantum computing are centered around solving search and optimization tasks, particularly in fields such as physics simulations, quantum chemistry, and finance. However, the current quantum software testing methods face practical limitations when applied in industrial contexts: (i) they do not apply to quantum programs most relevant to the industry, (ii) they require a full program specification, which is usually not available for these programs, and (iii) they are incompatible with error mitigation methods currently adopted by main industry actors like IBM. To address these challenges, we present QOPS, a novel quantum software testing approach. QOPS introduces a new definition of test cases based on Pauli strings to improve compatibility with different quantum programs. QOPS also introduces a new test oracle that can be directly integrated with industrial APIs such as IBM's Estimator API and can utilize error mitigation methods for testing on real noisy quantum computers. We also leverage the commuting property of Pauli strings to relax the requirement of having complete program specifications, making QOPS practical for testing complex quantum programs in industrial settings. We empirically evaluate QOPS on 194,982 real quantum programs, demonstrating effective performance in test assessment compared to the state-of-the-art with a perfect F1-score, precision, and recall. Furthermore, we validate the industrial applicability of QOPS by assessing its performance on IBM's three real quantum computers, incorporating both industrial and open-source error mitigation methods.

Related papers

• Search-Based Quantum Program Testing via Commuting Pauli String [6.124926843021984]
  SB-QOPS is a search-based quantum program testing approach via commuting Pauli strings.<n>We conduct a large-scale empirical evaluation on quantum circuits of up to 29 qubits on real quantum computers and emulators.<n>Results show that SB-QOPS significantly outperforms QOPS, achieving a fault-detection score of 100% for circuits up to 29 qubits.
  arXiv  Detail & Related papers  (2026-02-12T02:13:12Z)
• QSPE: Enumerating Skeletal Quantum Programs for Quantum Library Testing [11.99653900003681]
  We propose QSPE, a practical approach that follows the differential testing principle and extends the existing approach, SPE, for quantum libraries.<n> QSPE is fully automated, requiring no pre-set configurations or domain expertise, and can effectively generate a large set of diverse program variants.<n>In our experiments, the QSPE approach demonstrates remarkable effectiveness in generating 22,770 program variants across multiple quantum computing platforms.
  arXiv  Detail & Related papers  (2026-01-17T08:10:40Z)
• Achieving Utility-Scale Applications through Full Stack Co-Design of Fault Tolerant Quantum Computers [0.0]
  We show how quantum computers could realistically and practically tackle CO$$ utilization for green energy production.<n>We bring down the quantum runtime from 22 years to just 1 day, achieving a significant 7.9e03 reduction from previous state-of-the-art work.
  arXiv  Detail & Related papers  (2025-10-30T14:39:44Z)
• Benchmarking fault-tolerant quantum computing hardware via QLOPS [2.0464713282534848]
  To run quantum algorithms, it is essential to develop scalable quantum hardware with low noise levels.<n>Various fault-tolerant quantum computing schemes have been developed for different hardware platforms.<n>We propose Quantum Logical Operations Per Second (QLOPS) as a metric for assessing the performance of FTQC schemes.
  arXiv  Detail & Related papers  (2025-07-16T08:31:51Z)
• 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)
• Sequential Quantum Computing [41.94295877935867]
  We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple or heterogeneous quantum processors.<n>SQC overcomes the limitations of each type of quantum computer by combining their complementary strengths.<n>These results highlight SQC as a powerful and versatile approach for addressing complex quantum optimization problems.
  arXiv  Detail & Related papers  (2025-06-25T17:51:29Z)
• 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 [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• A Framework for the Efficient Evaluation of Runtime Assertions on Quantum Computers [4.704614749567071]
  This work proposes a framework that assists developers in the evaluation of runtime assertions on real quantum computers.<n>It translates a variety of assertions into sets of measurements, reduces execution overhead where possible and evaluates the results after the execution even in the presence of noise.
  arXiv  Detail & Related papers  (2025-05-06T18:00:07Z)
• Assessing Quantum Extreme Learning Machines for Software Testing in Practice [7.687075675562935]
  We study how quantum noise affects QELM in three industrial and real-world classical software testing case studies. Our results show that QELMs are significantly affected by quantum noise, with a performance drop of 250% in regression tasks and 50% in classification tasks. While error mitigation techniques can enhance noise resilience, achieving an average 3.0% performance drop in classification, but their effectiveness varies by context.
  arXiv  Detail & Related papers  (2024-10-20T20:17:58Z)
• 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)
• Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization [39.58317527488534]
  We present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO) problems. Our protocol is experimentally validated using 156 qubits on an IBM quantum processor with a heavy-hex architecture.
  arXiv  Detail & Related papers  (2024-09-05T17:38:59Z)
• Pauli Check Sandwiching for Quantum Characterization and Error Mitigation during Runtime [8.860010205263116]
  This work presents a novel quantum system characterization and error mitigation framework that applies Pauli check sandwiching (PCS) PCS combined with multi-programming unlocks non-trivial fidelity improvements in quantum program outcomes.
  arXiv  Detail & Related papers  (2024-08-10T14:21:45Z)
• Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers [37.69303106863453]
  We propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers. Our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average.
  arXiv  Detail & Related papers  (2024-06-17T14:10:10Z)
• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers [8.50998018964906]
  Current quantum computers have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers. This paper proposes a practical machine learning approach, called Q-LEAR, to mitigate noise errors in quantum software outputs. Results show that, compared to the baseline, Q-LEAR achieved a 25% average improvement in error mitigation on both real quantum computers and simulators.
  arXiv  Detail & Related papers  (2024-04-19T13:51:40Z)
• Quantum Computing for Solid Mechanics and Structural Engineering -- a Demonstration with Variational Quantum Eigensolver [3.8061090528695534]
  Variational quantum algorithms exploit the features of superposition and entanglement to optimize a cost function efficiently. We implement and demonstrate the numerical processes on the 5-qubit and 7-qubit quantum processors on the IBM Qiskit platform.
  arXiv  Detail & Related papers  (2023-08-28T17:52:47Z)
• Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications [1.7041248235270652]
  This survey focuses on selected supervised and unsupervised learning applications executed on quantum hardware. It covers techniques like encoding, ansatz structure, error mitigation, and gradient methods to address these challenges.
  arXiv  Detail & Related papers  (2023-07-03T10:12:34Z)
• Testing quantum computers with the protocol of quantum state matching [0.0]
  The presence of noise in quantum computers hinders their effective operation. We suggest the application of the so-called quantum state matching protocol for testing purposes. For systematically varied inputs we find that the device with the smaller quantum volume performs better on our tests than the one with larger quantum volume.
  arXiv  Detail & Related papers  (2022-10-18T08:25:34Z)
• 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)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

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.