Iordanis Kerenidis

q-means: A quantum algorithm for unsupervised machine learning

2018 · arXiv (Cornell University)

Quantum machine learning is one of the most promising applications of a full-scale quantum computer. Over the past few years, many quantum machine learning algorithms have been proposed that can potentially offer considerable speedups over …

Low depth algorithms for quantum amplitude estimation

2020 · arXiv (Cornell University)

We design and analyze two new low depth algorithms for amplitude estimation (AE) achieving an optimal tradeoff between the quantum speedup and circuit depth. For $β\in (0,1]$, our algorithms require $N= \tilde{O}( \frac{1}{ ε^{1+β}})$ oracle …

Improved clinical data imputation via classical and quantum determinantal point processes

2023 · eLife

Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, …

Quantum Agents for Algorithmic Discovery

2025 · arXiv (Cornell University)

We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search …