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Limitations on the simulation of non-sparse hamiltonians

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
abstract

The problem of simulating sparse Hamiltonians on quantum computers is well studied. The evolution of a sparse N x N Hamiltonian H for time t can be simulated using O(||Ht||poly(log N)) operations, which is essentially optimal due to a no--fast-forwarding theorem. Here, we consider non-sparse Hamiltonians and show significant limitations on their simulation. We generalize the no--fast-forwarding theorem to dense Hamiltonians, ruling out generic simulations taking time o(||Ht||), even though ||H|| is not a unique measure of the size of a dense Hamiltonian $H$. We also present a stronger limitation ruling out the possibility of generic simulations taking time poly(||Ht||,log N), showing that known simulations based on discrete-time quantum walk cannot be dramatically improved in general. On the positive side, we show that some non-sparse Hamiltonians can be simulated efficiently, such as those with graphs of small arboricity.

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quant-ph 2

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2026 2

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UNVERDICTED 2

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representative citing papers

Analog photonic simulator for large-scale transport

quant-ph · 2026-05-30 · unverdicted · novelty 5.0

Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.

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Showing 2 of 2 citing papers.

  • An Oracle-Free Quantum Algorithm for Nonadiabatic Quantum Molecular Dynamics quant-ph · 2026-04-21 · unverdicted · none · ref 93

    An oracle-free Trotter-based quantum algorithm for nonadiabatic molecular dynamics achieves circuit depth advantages over QROM architectures and retains T-gate scalability compared to quantum signal processing.

  • Analog photonic simulator for large-scale transport quant-ph · 2026-05-30 · unverdicted · none · ref 22 · internal anchor

    Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.