5 citations · 9 across the 9 of their papers we have counts for
9 papers
Architecture-aware Unitary Synthesis
Frans Perkkola, Arianne Meijer-van de Griend, Jukka K. Nurminen
We present a novel architecture-aware transpilation method for exact general unitary gate synthesis on superconducting quantum hardware. Our approach is tightly integrated with the…
Explainable quantum regression algorithm with encoded data structure
C. -C. Joseph Wang, F. Perkkola, I. Salmenperä +2
Hybrid variational quantum algorithms are promising for solving practical problems, such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and…
The Average Relative Entropy and Transpilation Depth determines the noise robustness in Variational Quantum Classifiers
Aakash Ravindra Shinde, Arianne Meijer - van de Griend, Jukka K. Nurminen
Variational Quantum Algorithms (VQAs) have been extensively researched for applications in Quantum Machine Learning (QML), Optimization, and Molecular simulations. Although designe…
Influence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models
Aakash Ravindra Shinde, Jukka K. Nurminen
Data dimensionality reduction techniques are often utilized in the implementation of Quantum Machine Learning models to address two significant issues: the constraints of NISQ quan…
Perspectives on Utilization of Measurements in Quantum Algorithms
Valter Uotila, Ilmo Salmenperä, Leo Becker +3
Measurement is a fundamental operation in quantum computing and has many important use cases in quantum algorithms. This article provides a comprehensive overview of the basic meas…
Optimizing State Preparation for Variational Quantum Regression on NISQ Hardware
Frans Perkkola, Ilmo Salmeperä, Arianne Meijer-van de Griend +3
The execution of quantum algorithms on modern hardware is often constrained by noise and qubit decoherence, limiting the circuit depth and the number of gates that can be executed.…
Performance Estimation in Binary Classification Using Calibrated Confidence
Juhani Kivimäki, Jakub Białek, Wojtek Kuberski +1
Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model's performance after deployment. Traditionally, perfor…
How Large Language Models Are Changing MOOC Essay Answers: A Comparison of Pre- and Post-LLM Responses
Leo Leppänen, Lili Aunimo, Arto Hellas +2
The release of ChatGPT in late 2022 caused a flurry of activity and concern in the academic and educational communities. Some see the tool's ability to generate human-like text tha…
Explainable quantum regression algorithm with encoded data structure
C. -C. Joseph Wang, F. Perkkola, I. Salmenperä +3
Hybrid variational quantum algorithms (VQAs) are promising for solving practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning…