Linear combination of Hamiltonian simulation for non-unitary dynamics with optimal state preparation cost

03/02/2023
by   Dong An, et al.
0

We propose a simple method for simulating a general class of non-unitary dynamics as a linear combination of Hamiltonian simulation (LCHS) problems. LCHS does not rely on converting the problem into a dilated linear system problem, or on the spectral mapping theorem. The latter is the mathematical foundation of many quantum algorithms for solving a wide variety of tasks involving non-unitary processes, such as the quantum singular value transformation (QSVT). The LCHS method can achieve optimal cost in terms of state preparation. We also demonstrate an application for open quantum dynamics simulation using the complex absorbing potential method with near-optimal dependence on all parameters.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/22/2018

A quantum algorithm for simulating non-sparse Hamiltonians

We present a quantum algorithm for simulating the dynamics of Hamiltonia...
research
10/04/2022

Quantum communication complexity of linear regression

Dequantized algorithms show that quantum computers do not have exponenti...
research
11/04/2021

Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics

We propose a simple quantum algorithm for simulating highly oscillatory ...
research
04/06/2018

Approximating Hamiltonian dynamics with the Nyström method

Simulating the time-evolution of quantum mechanical systems is BQP-hard ...
research
06/30/2022

Practical Black Box Hamiltonian Learning

We study the problem of learning the parameters for the Hamiltonian of a...
research
09/07/2022

Synthesizing efficient circuits for Hamiltonian simulation

We provide a new approach for compiling quantum simulation circuits that...
research
07/25/2021

Assertion-based Approaches to Auditing Complex Elections, with application to party-list proportional elections

Risk-limiting audits (RLAs), an ingredient in evidence-based elections, ...

Please sign up or login with your details

Forgot password? Click here to reset