Browsing by Subject "quantum computing"

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Access status: Restricted ,
Analysis of D'Wave2000Q Applicability for Job Scheduling Problems
(Data obrony: 2020-07-16) Tomasiewicz, Dawid
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Open Access ,
AQMLATOR – An auto quantum machine learning e-platform
(Wydawnictwa AGH, 2025) Rybotycki, Tomasz; Gawron, Piotr
The successful implementation of a machine-learning (ML) model requires three main components: a training data set, a suitable model architecture, and a suit able training procedure. Given the data set and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on an automatic architecture search– a meta method that aims to remove the need for human interaction with the ML system-design process. The success of ML and the development of quantum computing (QC) in recent years has led to the birth of a new fascinating field called quantum machine learning (QML), which incorporates quantum computers into ML models (among other things). In this paper, we present AQMLator, an auto quantum machine-learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. In this way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.
Access status: Open Access ,
Quantum inspired chaotic salp swarm optimization for dynamic optimization
(Wydawnictwa AGH, 2024) Pathak, Sanjai; Mani, Ashish; Sharma, Mayank; Chatterjee, Amlan
Many real-world problems are dynamic optimization problems that are unknown beforehand. In practice, unpredictable events such as the arrival of new jobs, due date changes, and reservation cancellations, changes in parameters or constraints make the search environment dynamic. Many algorithms are designed to deal with stationary optimization problems, but these algorithms do not face dynamic optimization problems or manage them correctly. Although some optimization algorithms are proposed to deal with the changes in dynamic environments differently, there are still areas of improvement in existing algorithms due to limitations or drawbacks, especially in terms of locating and following the previously identified optima. With this in mind, we studied a variant of SSA known as QSSO, which integrates the principles of quantum computing. An attempt is made to improve the overall performance of standard SSA to deal with the dynamic environment effectively by locating and tracking the global optima for DOPs. This work is an extension of the proposed new algorithm QSSO, known as the Quantum-inspired Chaotic Salp Swarm Optimization (QCSSO) Algorithm, which details the various approaches considered while solving DOPs. A chaotic operator is employed with quantum computing to respond to change and guarantee to increase individual searchability by improving population diversity and the speed at which the algorithm converges. We experimented by evaluating QCSSO on a well-known generalized dynamic benchmark problem (GDBG) provided for CEC 2009, followed by a comparative numerical study with well-regarded algorithms. As promised, the introduced QCSSO is discovered as the rival algorithm for DOPs.
Access status: Open Access ,
Randomized and quantum algorithms for solving initial-value problems in ordinary differential equations of order k
(2008) Goćwin, Maciej; Szczęsny, Marek
The complexity of initial-value problems is well studied for systems of equations of first order. In this paper, we study the $\varepsilon$-complexity for initial-value problems for scalar equations of higher order. We consider two models of computation, the randomized model and the quantum model. We construct almost optimal algorithms adjusted to scalar equations of higher order, without passing to systems of first order equations. The analysis of these algorithms allows us to establish upper complexity bounds. We also show (almost) matching lower complexity bounds. The $\varepsilon$-complexity in the randomized and quantum setting depends on the regularity of the right-hand side function, but is independent of the order of equation. Comparing the obtained bounds with results known in the deterministic case, we see that randomized algorithms give us a speed-up by $1/2$, and quantum algorithms by $1$ in the exponent. Hence, the speed-up does not depend on the order of equation, and is the same as for the systems of equations of first order. We also include results of some numerical experiments which confirm theoretical results.
Access status: Restricted ,
Solving Optimization problems using Qiskit Aqua
(Data obrony: 2020-07-16) Stachoń, Małgorzata
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Złożoność na komputerze kwantowym - od problemów dyskretnych do zadań ciągłych
(Data obrony: 2020-12-10) Bednarczyk, Ernest
Wydział Matematyki Stosowanej