6 papers
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
Hiroki Hamaguchi, Yuya Hikima, Hiroshi Sawada +1
We study optimization under performative prediction, where deploying a model affects the future data distribution. For this setting, several gradient-based approaches have been pro…
Bounded-depth spacetime lattice surgery for resource-efficient fault-tolerant quantum computation
Kou Hamada, Hiroki Hamaguchi, Yosuke Ueno +3
Fault-tolerant quantum computing based on lattice surgery requires place-and-route compilation with low spacetime overhead. Routing, in particular, faces a basic tension between su…
Practical Regularized Quasi-Newton Methods with Inexact Function Values
Hiroki Hamaguchi, Naoki Marumo, Akiko Takeda
Many practical optimization problems involve objective function values that are corrupted by unavoidable numerical errors. In smooth nonconvex optimization, quasi-Newton methods co…
Initial Placement for Fruchterman--Reingold Force Model With Coordinate Newton Direction
Hiroki Hamaguchi, Naoki Marumo, Akiko Takeda
The Fruchterman--Reingold (FR) force model is widely used in force-directed graph drawing, and multilevel approaches such as sfdp in Graphviz scale these methods effectively. A cru…
Sample Complexity of Low-rank Tensor Recovery from Uniformly Random Entries
Hiroki Hamaguchi, Shin-ichi Tanigawa
We show that a generic tensor of order and CP rank can be uniquely recovered from $n\log n+dn\log \log n +o(n\log \log n)…
Faster computation of nonstabilizerness
Hiroki Hamaguchi, Kou Hamada, Naoki Marumo +1
The characterization of nonstabilizerness is fruitful due to its application in gate synthesis and classical simulation. In particular, the resource monotone called the stabilizer…