Using Quantum Computers to Speed Up Dynamic Testing of Software
- URL: http://arxiv.org/abs/2209.04860v1
- Date: Sun, 11 Sep 2022 13:28:26 GMT
- Title: Using Quantum Computers to Speed Up Dynamic Testing of Software
- Authors: Andriy Miranskyy
- Abstract summary: As the number and possible values of input parameters increase, the cost of dynamic testing rises.
This paper examines whether quantum computers (QCs) can help speed up the dynamic testing of programs written for classical computers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software under test can be analyzed dynamically, while it is being executed,
to find defects. However, as the number and possible values of input parameters
increase, the cost of dynamic testing rises.
This paper examines whether quantum computers (QCs) can help speed up the
dynamic testing of programs written for classical computers (CCs). To
accomplish this, an approach is devised involving the following three steps:
(1) converting a classical program to a quantum program; (2) computing the
number of inputs causing errors, denoted by $K$, using a quantum counting
algorithm; and (3) obtaining the actual values of these inputs using Grover's
search algorithm.
This approach can accelerate exhaustive and non-exhaustive dynamic testing
techniques. On the CC, the computational complexity of these techniques is
$O(N)$, where $N$ represents the count of combinations of input parameter
values passed to the software under test. In contrast, on the QC, the
complexity is $O(\varepsilon^{-1} \sqrt{N/K})$, where $\varepsilon$ is a
relative error of measuring $K$.
The paper illustrates how the approach can be applied and discusses its
limitations. Moreover, it provides a toy example executed on a simulator and an
actual QC. This paper may be of interest to academics and practitioners as the
approach presented in the paper may serve as a starting point for exploring the
use of QC for dynamic testing of CC code.
Related papers
- Benchmarking quantum computers with any quantum algorithm [0.0]
Application-based benchmarks are increasingly used to quantify and compare quantum computers' performance.<n>We present a method for creating scalable and efficient benchmarks from any quantum algorithm or application.
arXiv Detail & Related papers (2025-08-07T18:11:22Z) - An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.
We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.
We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
arXiv Detail & Related papers (2025-04-13T11:49:53Z) - 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) - Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Quantum sampling algorithms for quantum state preparation and matrix block-encoding [0.0]
We first present an algorithm based on QRS that prepares a quantum state $|psi_frangle propto sumN_x=1 f(x)|xrangle$.
We then adapt QRS techniques to the matrix block-encoding problem and introduce a QRS-based algorithm for block-encoding a given matrix $A = sum_ij A_ij |irangle langle j|$.
arXiv Detail & Related papers (2024-05-19T03:46:11Z) - Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent [37.59299233291882]
We propose Q-Newton, a hybrid quantum-classical scheduler for accelerating neural network training with Newton's GD.<n>Q-Newton utilizes a streamlined scheduling module that coordinates between quantum and classical linear solvers.<n>Our evaluation showcases the potential for Q-Newton to significantly reduce the total training time compared to commonly used quantum machines.
arXiv Detail & Related papers (2024-04-30T23:55:03Z) - A two-circuit approach to reducing quantum resources for the quantum lattice Boltzmann method [41.66129197681683]
Current quantum algorithms for solving CFD problems use a single quantum circuit and, in some cases, lattice-based methods.
We introduce the a novel multiple circuits algorithm that makes use of a quantum lattice Boltzmann method (QLBM)
The problem is cast as a stream function--vorticity formulation of the 2D Navier-Stokes equations and verified and tested on a 2D lid-driven cavity flow.
arXiv Detail & Related papers (2024-01-20T15:32:01Z) - End-to-end complexity for simulating the Schwinger model on quantum computers [0.6449786007855248]
We propose an efficient implementation of block-encoding of the Schwinger model Hamiltonian.
As an end-to-end application, we compute the vacuum persistence amplitude.
Our results provide insights into predictions about the performance of quantum computers in the FTQC era.
arXiv Detail & Related papers (2023-11-29T06:36:11Z) - Simulation of IBM's kicked Ising experiment with Projected Entangled
Pair Operator [71.10376783074766]
We perform classical simulations of the 127-qubit kicked Ising model, which was recently emulated using a quantum circuit with error mitigation.
Our approach is based on the projected entangled pair operator (PEPO) in the Heisenberg picture.
We develop a Clifford expansion theory to compute exact expectation values and use them to evaluate algorithms.
arXiv Detail & Related papers (2023-08-06T10:24:23Z) - Wave Matrix Lindbladization I: Quantum Programs for Simulating Markovian
Dynamics [6.345523830122166]
Density Matrix Exponentiation is a technique for simulating Hamiltonian dynamics when the Hamiltonian to be simulated is available as a quantum state.
We present a natural analogue to this technique, for simulating Markovian dynamics governed by the well known Lindblad master equation.
We propose a quantum algorithm for this task, called Wave Matrix Lindbladization, and we also investigate its sample complexity.
arXiv Detail & Related papers (2023-07-27T15:22:04Z) - On sampling determinantal and Pfaffian point processes on a quantum
computer [49.1574468325115]
DPPs were introduced by Macchi as a model in quantum optics the 1970s.
Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a classical computer is easier than on a classical one.
Vanilla sampling consists in two steps, of respective costs $mathcalO(N3)$ and $mathcalO(Nr2)$ operations on a classical computer, where $r$ is the rank of the kernel matrix.
arXiv Detail & Related papers (2023-05-25T08:43:11Z) - Quantum Resources Required to Block-Encode a Matrix of Classical Data [56.508135743727934]
We provide circuit-level implementations and resource estimates for several methods of block-encoding a dense $Ntimes N$ matrix of classical data to precision $epsilon$.
We examine resource tradeoffs between the different approaches and explore implementations of two separate models of quantum random access memory (QRAM)
Our results go beyond simple query complexity and provide a clear picture into the resource costs when large amounts of classical data are assumed to be accessible to quantum algorithms.
arXiv Detail & Related papers (2022-06-07T18:00:01Z) - Implementing Quantum Finite Automata Algorithms on Noisy Devices [0.0]
We present improved circuit based implementations for QFA algorithms recognizing the $ MOD_p $ problem using the Qiskit framework.
We run the circuits on real IBM quantum devices but due to the limitation of the real quantum devices in the NISQ era, the results are heavily affected by the noise.
arXiv Detail & Related papers (2021-05-13T10:51:28Z) - Quantum Proofs of Proximity [6.160793572747927]
We show that QMA proofs of proximity can be exponentially stronger than classical proofs of proximity and quantum testers.
Our results include an algorithmic framework that enables quantum speedups for testing an expressive class of properties.
We also propose a QMA algorithm for testing graph bipartitneness, a property that lies outside of this family.
arXiv Detail & Related papers (2021-05-08T13:15:30Z) - On solving classes of positive-definite quantum linear systems with
quadratically improved runtime in the condition number [0.0]
We show that solving a Quantum Linear System entails a runtime linear in $kappa$ when $A$ is positive definite.
We present two new quantum algorithms featuring a quadratic speed-up in $kappa$.
arXiv Detail & Related papers (2021-01-28T08:41:21Z) - Quantum Gram-Schmidt Processes and Their Application to Efficient State
Read-out for Quantum Algorithms [87.04438831673063]
We present an efficient read-out protocol that yields the classical vector form of the generated state.
Our protocol suits the case that the output state lies in the row space of the input matrix.
One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
arXiv Detail & Related papers (2020-04-14T11:05:26Z)
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.