5 papers · 1 filter
Scaling Behavior of Parameterized Quantum Circuits from a Lie-Algebraic Perspective
Hiroshi Ohno
Understanding how the performance of parameterized quantum circuits scales with available resources is important for characterizing their trainability and effective model capacity.…
Lie-Group Mode Connectivity in Quantum Machine Learning from a Dynamical Lie Algebra Perspective
Hiroshi Ohno
Mode connectivity has been widely studied in classical machine learning as a geometric property of low-loss regions in parameter space. In quantum machine learning (QML), however,…
A regularization method for quantum neural networks using data symmetry
Hiroshi Ohno
Leveraging data symmetries has recently become a key strategy in quantum neural networks (QNNs) to improve training efficiency. In this study, we propose a symmetry-informed regula…
Approximate Cosine Similarity Estimation via an Angle-Encoding Hadamard Test
Hiroshi Ohno
The Hadamard test is a standard quantum primitive for estimating inner products and expectation values, but in data-processing settings its practical utility is often limited by th…
Generalization analysis of quantum neural networks using dynamical Lie algebras
Hiroshi Ohno
The paper presents a generalization bound for quantum neural networks based on a dynamical Lie algebra. Using covering numbers derived from a dynamical Lie algebra, the Rademacher…