Efficient measurement-based quantum computing with continuous-variable systems

TL;DR

This paper introduces efficient, scalable measurement-based quantum computing schemes using continuous-variable systems with non-Gaussian resource states, overcoming Gaussian state limitations and enabling error correction.

Contribution

It proposes new measurement-based quantum computing schemes with non-Gaussian states, utilizing tensor network frameworks and finite-dimensional encodings for scalability and error correction.

Findings

Schemes are based on non-Gaussian resource states prepared via light-matter interactions or optical methods.

Overcome limitations of Gaussian cluster states, enabling universal quantum computation.

Identify fundamental limitations in continuous-variable quantum computing schemes.

Abstract

We present strictly efficient schemes for scalable measurement-based quantum computing using continuous-variable systems: These schemes are based on suitable non-Gaussian resource states, ones that can be prepared using interactions of light with matter systems or even purely optically. Merely Gaussian measurements such as optical homodyning as well as photon counting measurements are required, on individual sites. These schemes overcome limitations posed by Gaussian cluster states, which are known not to be universal for quantum computations of unbounded length, unless one is willing to scale the degree of squeezing with the total system size. We establish a framework derived from tensor networks and matrix product states with infinite physical dimension and finite auxiliary dimension general enough to provide a framework for such schemes. Since in the discussed schemes the logical encoding is finite-dimensional, tools of error correction are applicable. We also identify some further limitations for any continuous-variable computing scheme from which one can argue that no substantially easier ways of continuous-variable measurement-based computing than the presented one can exist.