Models to Reduce the Complexity of Simulating a Quantum Computer

TL;DR

This paper introduces various simulation models to assess the feasibility of quantum computers, focusing on reducing complexity while accurately modeling errors like decoherence and inaccuracies.

Contribution

It defines detailed and simplified simulation models based on implementation, demonstrating uncorrelated errors to reduce simulation efforts.

Findings

Simplified models produce accurate results with less complexity

Decoherence and inaccuracies are uncorrelated, simplifying simulations

Feasibility of simulating larger quantum systems is improved

Abstract

Recently Quantum Computation has generated a lot of interest due to the discovery of a quantum algorithm which can factor large numbers in polynomial time. The usefulness of a quantum com puter is limited by the effect of errors. Simulation is a useful tool for determining the feasibility of quantum computers in the presence of errors. The size of a quantum computer that can be simulat ed is small because faithfully modeling a quantum computer requires an exponential amount of storage and number of operations. In this paper we define simulation models to study the feasibility of quantum computers. The most detailed of these models is based directly on a proposed imple mentation. We also define less detailed models which are exponentially less complex but still pro duce accurate results. Finally we show that the two different types of errors, decoherence and inaccuracies, are uncorrelated. This decreases the number of simulations which must be per formed.