A quantum auto-correlation function integrated over an extra time variable yields eigenenergies and eigenstate observables for Hermitian, non-Hermitian, and Floquet systems, demonstrated on a silicon-photonic chip.
Fast Laplace transforms on quantum computers
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abstract
While many classical algorithms rely on Laplace transforms, it has remained an open question whether these operations could be implemented efficiently on quantum computers. In this work, we introduce the Quantum Laplace Transform (QLT), which enables the implementation of $N\times N$ discrete Laplace transforms on quantum states encoded in $\lceil \log_2(N)\rceil$-qubits. In many cases, the associated quantum circuits have a depth that scales with $N$ as $O(\log(\log(N)))$ and a size that scales as $O(\log(N))$, requiring exponentially fewer operations and double-exponentially less computational time than their classical counterparts. These efficient scalings open the possibility of developing a new class of quantum algorithms based on Laplace transforms, with potential applications in physics, engineering, chemistry, machine learning, and finance.
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quant-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Universal Quantum Computational Spectroscopy on a Quantum Chip
A quantum auto-correlation function integrated over an extra time variable yields eigenenergies and eigenstate observables for Hermitian, non-Hermitian, and Floquet systems, demonstrated on a silicon-photonic chip.