Poster: Fuzz Testing of Quantum Program

Abstract

Nowadays, quantum program is widely used and quickly developed. However, the absence of testing methodology restricts their quality. Different input format and operator from traditional program make this issue hard to resolve.In this paper, we present QuanFuzz, a search-based test input generator for quantum program. We define the quantum sensitive information to evaluate test input for quantum program and use matrix generator to generate test cases with higher coverage. First, we extract quantum sensitive information -measurement operations on those quantum registers and the sensitive branches associated with those measurement results, from the quantum source code. Then, we use the sensitive information guided algorithm to mutate the initial input matrix and select those matrices which improve the probability weight for a value of the quantum register to trigger the sensitive branch. The process keeps iterating until the sensitive branch triggered. We tested QuanFuzzon benchmarks and acquired 20% -60% more coverage compared to traditional testing input generation.

Quantum software testing: State of the art

“…Gray‐box fuzzing, the most deployed fuzzing strategy, combines light program instrumentation with the new input data generation. In a previous research, 41 Wang et al present QuanFuzz, a search‐based test input generation tool for quantum software. It analyzes the system under test by instrumenting the source code, identifying which parts of the source code are associated to the measurement results, and then mutates the initial input matrix, selecting those mutations which improve the probability weight for a value of the quantum register to trigger sensitive branches.…”

MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform

“…A problem related to that addressed here is how to test quantum programs. Several approaches have been proposed, including a search-based techniques [30,33], statistical assertion checks that try to limit the effects on the actual computation [14,18,19], combinatorial testing [32], and coverage-based methods [2]. In contrast to our work, these techniques test specific programs, not the platform that programs are running on.…”

The Foundations of Quantum Computing and Their Relation to Software Engineering

“…13 we show a classification of types of testing techniques for quantum software. It was extracted from [63,[74][75][76][77][78][79][80][81][82][83][84], which treat specific techniques and tools for debugging and testing.…”

“…White-Black box testing and functional testing are like classical software testing applied to quantum software. Specialized testing is included, such as fuzz testing [80,84], which is an automated software testing method that injects invalid, malformed, or unexpected inputs into a system to reveal software defects and vulnerabilities. Quantum noise provides an effective built-in fuzzing capability that is centered around the actual answer to a computation.…”