Marten Folkertsma

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3ranked-venue papers
2as first author
3since 2021 · last 2026
—ORCID · none

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Theory of computation · 3 · 2 first-author · 3 since 2021
YearPublicationVenuePosition
2026 Fully Characterizing Lossy Catalytic Computation
abstract
A catalytic machine is a model of computation where a traditional space-bounded machine is augmented with an additional, significantly larger, “catalytic” tape, which, while being available as a work tape, has the caveat of being initialized with an arbitrary string, which must be preserved at the end of the computation. Despite this restriction, catalytic machines have been shown to have surprising additional power; a logspace machine with a polynomial length catalytic tape, known as catalytic logspace (CL), can compute problems which are believed to be impossible for L. A fundamental question of the model is whether the catalytic condition, of leaving the catalytic tape in its exact original configuration, is robust to minor deviations. This study was initialized by Gupta et al. (2024), who defined lossy catalytic logspace (LCL[e]) as a variant of CL where we allow up to e errors when resetting the catalytic tape. They showed that LCL[e]=CL for any e=O(1), which remains the frontier of our understanding. In this work we completely characterize lossy catalytic space (LCSPACE[s,c,e]) in terms of ordinary catalytic space (CSPACE[s,c]). We show that (Formula presented.) In other words, allowing e errors on a catalytic tape of length c is equivalent, up to a constant stretch, to an equivalent errorless catalytic machine with an additional elogc bits of ordinary working memory. As a consequence, we show that for any e, LCL[e]=CL implies SPACE[elogn]⊆ZPP, thus giving a barrier to any improvement beyond LCL[O(1)]=CL. We also extend all our results to every variant of catalytic space.
Marten Folkertsma, Ian Mertz, Florian Speelman, Quinten Tupker
Algorithmica1
2025 Fully Characterizing Lossy Catalytic Computation
Marten Folkertsma, Ian Mertz, Florian Speelman, Quinten Tupker
ITCS1
2023 Improved Hardness Results for the Guided Local Hamiltonian Problem
abstract
Estimating the ground state energy of a local Hamiltonian is a central problem in quantum chemistry. In order to further investigate its complexity and the potential of quantum algorithms for quantum chemistry, Gharibian and Le Gall (STOC 2022) recently introduced the guided local Hamiltonian problem (GLH), which is a variant of the local Hamiltonian problem where an approximation of a ground state (which is called a guiding state) is given as an additional input. Gharibian and Le Gall showed quantum advantage (more precisely, BQP-completeness) for GLH with 6-local Hamiltonians when the guiding state has fidelity (inverse-polynomially) close to 1/2 with a ground state. In this paper, we optimally improve both the locality and the fidelity parameter: we show that the BQP-completeness persists even with 2-local Hamiltonians, and even when the guiding state has fidelity (inverse-polynomially) close to 1 with a ground state. Moreover, we show that the BQP-completeness also holds for 2-local physically motivated Hamiltonians on a 2D square lattice or a 2D triangular lattice. Beyond the hardness of estimating the ground state energy, we also show BQP-hardness persists when considering estimating energies of excited states of these Hamiltonians instead. Those make further steps towards establishing practical quantum advantage in quantum chemistry.
Chris Cade, Marten Folkertsma, Sevag Gharibian, Ryu Hayakawa, François Le Gall, Tomoyuki Morimae, Jordi Weggemans
ICALP2