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Centrum Wiskunde & Informatica

Academic institutioneurope · nl
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Research library194linked papers
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Selected work

Representative Papers

Tight Bounds for Quantum Phase Estimation and Related Problems

May 08, 2023Embedded Systems and Applications

This work establishes tight query complexity bounds—up to logarithmic factors—for quantum phase estimation (QPE) and its variants across all parameter regimes. We consider three problems: standard QPE, QPE with a prior auxiliary state overlapping the target eigenspace by at least γ, and maximum eigenphase estimation. Using techniques including trigonometric polynomial analysis, information-theoretic lower bound derivation, constructive algorithm design, and error amplification, we prove that achieving precision δ with failure probability ε requires Ω((1/δ) log(1/ε)) queries—matching the best-known upper bounds. Our results precisely quantify the utility of auxiliary states and prior knowledge, revealing fundamental limits on their effectiveness. Moreover, we fully resolve the query complexity of the Unitary recurrence time problem.

18 citations4 influentialRead paper

Minimum Star Partitions of Simple Polygons in Polynomial Time

Nov 17, 2023Symposium on the Theory of Computing

This paper resolves the long-standing “minimum star-shaped partition of a simple polygon” problem—open since 1981—by covering a given simple polygon with the fewest non-overlapping star-shaped subpolygons, allowing Steiner points. The proposed method integrates geometric decomposition, visibility graph optimization, dynamic programming, and structural analysis of star kernels, constructing the DP state space over triangulations. It yields the first exact polynomial-time algorithm applicable to arbitrary simple polygons, overcoming prior restrictions to monotone or orthogonal polygons and eliminating the requirement to forbid Steiner points. The algorithm runs in O(n⁹) time, a substantial improvement over exponential brute-force approaches. This theoretical breakthrough enables direct applications in CNC pocket milling, motion planning, and shape parameterization, where minimal star-shaped decompositions are essential for efficient toolpath generation, collision-free navigation, and domain mapping.

4 citations1 influentialRead paper

Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia

Dec 03, 2025

Existing evaluation methods inadequately measure large language model (LLM) agents’ cooperative generalization capability in novel, mixed-motive social scenarios. Method: We conduct a systematic zero-shot evaluation on the Concordia multi-agent simulation platform, assessing LLM agents’ ability to recognize and realize mutual benefit across diverse social interaction tasks—including negotiation and collective action—using a novel quantitative framework for general cooperative intelligence. This framework emphasizes high-generalization dimensions such as persuasion and norm enforcement. Contribution/Results: Empirical analysis of NeurIPS 2024 Concordia Competition data reveals substantial limitations in current LLM agents’ cross-context cooperative generalization, particularly in dynamic coordination and implicit norm modeling. Our work establishes a new paradigm for benchmarking and diagnosing cooperative intelligence, advancing both methodological rigor and diagnostic precision in multi-agent cooperation research.

2 citationsRead paper

Learning junta distributions, quantum junta states, and QAC$^0$ circuits

Oct 21, 2024

This work unifies the learning problem for three classes of objects exhibiting low-dependence structure: $k$-juntas (probability distributions depending on at most $k$ variables), quantum $k$-juntas (states where only a $k$-qubit subsystem is nontrivial, while the rest is maximally mixed), and QAC$^0$ circuits. Methodologically, it introduces the first formal definition of quantum $k$-juntas; establishes structural connections between junta distributions/states and the Choi states of QAC$^0$ circuits; and leverages total variation/trace distance analysis, Pauli spectrum techniques, Choi representation, tensor network decomposition, and concentration inequalities. Key contributions are: (1) optimal sample complexity $O(2^k log n / varepsilon^2)$ for learning classical $k$-junta distributions; (2) the first efficient single-copy learning algorithm for quantum $k$-juntas, requiring $O(12^k log n / varepsilon^2)$ samples; and (3) an exponential improvement in learning QAC$^0$ circuits—reducing sample complexity from $n^{mathrm{poly}}$ to $2^{O((log(s^2 2^a))^d)} log n$, where $s$ is size and $a$ is fan-in.

2 citationsRead paper

Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling

Feb 11, 2026

Traditional recurrent neural networks (RNNs) struggle to model long-range dependencies due to their fixed update mechanism, often suffering from memory decay. This work proposes the Selective Update RNN (suRNN), which introduces neuron-level binary gates that update hidden states only at information-rich timesteps, thereby decoupling recurrent dynamics from sequence length. For the first time, this approach enables neurons to adaptively learn when to update over time, combining sparse activation with nonlinear recurrent structure to construct gradient shortcut paths across timesteps while preserving RNN efficiency. Experiments demonstrate that suRNN matches or exceeds Transformer accuracy on benchmarks such as the Long Range Arena and WikiText, while significantly reducing computational and memory costs for long sequences.

1 citationsRead paper
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