2022 journal article

An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian Simulation

*SIAM Journal on Matrix Analysis and Applications*.

author keywords: quantum computing; quantum circuit synthesis; algebraic circuit compression; Hamiltonian simulation; free fermions; NISQ

TL;DR:
This work derives localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions and produces quantum circuits that have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.
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Source: ORCID

Added: August 4, 2022

Quantum computing is a promising technology that harnesses the peculiarities of quantum mechanics to deliver computational speedups for some problems that are intractable to solve on a classical computer. Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in terms of chip size and error rates. Shallow quantum circuits with uncomplicated topologies are essential for successful applications in the NISQ era. Based on matrix analysis, we derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions. The depth of the compressed circuits is independent of simulation time and grows linearly with the number of spins. The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $\mathcal{O}(10^3)$ spins. The resulting quantum circuits have a simple nearest-neighbor topology, which makes them ideally suited for NISQ devices.