Accurate and efficient calculations of vector inner product is one of the core components in many scientific computing tasks. In these scenarios, the quantum computing provides new paradigms, which can benefit from the persistent development of quantum hardware and software. This paper focuses on the benchmarks of the quantum inner product via the swap test circuit, and discusses its usages as the basic linear algebra subprograms (BLAS) solver for several practical applications, including 1) quantum machine learning, 2) quantum chromodynamics, 3) quantum physics, and 4) quantum chemistry. Benchmark results show that the results of quantum inner product and classical inner product are almost identical. The time complexity of the quantum inner product circuit is \( \mathcal {O}((n+M)logn) \) , where n is the dimension of the vector, M is the number of repeated measurements, thus the calculation time is acceptable when less than 9 qubits are used to implement the swap test circuit unit in these practical applications. It can be confirmed that the swap test circuit can be used as a multifunctional, high fidelity BLAS primitive that bridges quantum advantages to critical scientific fields.