Concolic Testing of Quantum Programs

• URL: http://arxiv.org/abs/2405.04860v2
• Date: Mon, 29 Jul 2024 11:51:40 GMT
• Title: Concolic Testing of Quantum Programs
• Authors: Shangzhou Xia, Jianjun Zhao, Fuyuan Zhang, Xiaoyu Guo,
• Abstract summary: This paper presents the first concolic testing framework specifically designed for quantum programs. The framework defines quantum conditional statements that quantify quantum states and presents a symbolization method for quantum variables.
• Score: 5.3611583388647635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the first concolic testing framework specifically designed for quantum programs. The framework defines quantum conditional statements that quantify quantum states and presents a symbolization method for quantum variables. Utilizing this framework, we generate path constraints for each concrete execution path of a quantum program. These constraints guide the exploration of new paths, with a quantum constraint solver determining the outcomes to generate novel input samples and enhance branch coverage. We implemented this framework in Python and integrated it with Qiskit for practical evaluation. Experimental results demonstrate that our concolic testing framework significantly improves branch coverage and the quality of quantum input samples, demonstrating its effectiveness and efficiency in quantum software testing.

Related papers

• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Learning Quantum Processes with Quantum Statistical Queries [0.0]
  This paper introduces the first learning framework for studying quantum process learning within the Quantum Statistical Query model. We propose an efficient QPSQ learner for arbitrary quantum processes accompanied by a provable performance guarantee. This work marks a significant step towards understanding the learnability of quantum processes and shedding light on their security implications.
  arXiv  Detail & Related papers  (2023-10-03T14:15:20Z)
• 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)
• Design by Contract Framework for Quantum Software [1.9988400064884826]
  We propose a design-by-contract framework for quantum software. It provides assertions on the input and output states of all quantum circuits built by certain procedures. Our framework has sufficient expressive power to verify the whole procedure of quantum software.
  arXiv  Detail & Related papers  (2023-03-31T00:21:28Z)
• Extending the Q-score to an Application-level Quantum Metric Framework [0.0]
  evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice. A prominent quantum metric is given by the Q-score metric of Atos. We show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers.
  arXiv  Detail & Related papers  (2023-02-01T18:03:13Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Application-Oriented Performance Benchmarks for Quantum Computing [0.0]
  benchmarking suite is designed to be readily accessible to a broad audience of users. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years.
  arXiv  Detail & Related papers  (2021-10-07T01:45:06Z)
• 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)
• 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.