Quantum-Inspired Algorithms beyond Unitary Circuits: the Laplace Transform

TL;DR

This paper introduces a tensor-network-based quantum-inspired algorithm for computing the non-unitary Laplace transform efficiently on classical hardware, enabling large-scale data processing and precise pole identification.

Contribution

It extends quantum-inspired algorithms beyond unitary circuits by leveraging tensor networks to compute the Laplace transform, a non-unitary operation, with high efficiency.

Findings

Simulated up to 2^30 input data points

Achieved significant acceleration through MPO compression

Controlled runtime and accuracy via bond dimension

Abstract

Quantum-inspired algorithms can deliver substantial speedups over classical state-of-the-art methods by executing quantum algorithms with tensor networks on conventional hardware. Unlike circuit models restricted to unitary gates, tensor networks naturally accommodate non-unitary maps. This flexibility lets us design quantum-inspired methods that start from a quantum algorithmic structure, yet go beyond unitarity to achieve speedups. Here we introduce a tensor-network approach to compute the discrete Laplace transform, a non-unitary, aperiodic transform (in contrast to the Fourier transform). We encode a length-$N$ signal on two paired $n$-qubit registers and decompose the overall map into a non-unitary exponential Damping Transform followed by a Quantum Fourier Transform, both compressed in a single matrix-product operator. This decomposition admits strong MPO compression to low bond dimension resulting in significant acceleration. We demonstrate simulations up to $N=2^{30}$ input data points, with up to $2^{60}$ output data points, and quantify how bond dimension controls runtime and accuracy, including precise and efficient pole identification.