Abstract

A quantum channel is said to be amixed-unitarychannel if it can be expressed as a convex combination of unitary channels. We prove that, given the Choi representation of a quantum channelΦ, it is NP-hard with respect to polynomial-time Turing reductions to determine whether or notΦis a mixed-unitary channel. This hardness result holds even under the assumption thatΦis not within an inverse-polynomial distance (in the dimension of the space upon whichΦacts) of the boundary of the mixed-unitary channels.

Highlights

  • In the theory of quantum information, quantum channels represent discrete-time changes in systems that can, in an idealized sense, be realized by physical processes

  • A unitary channel is a channel of the form Φ : L(Cn) → L(Cn) that is given by Φ(X) = U XU ∗ for every X ∈ L(Cn), for some fixed choice of a unitary operator U ∈ L(Cn)

  • The main purpose of this section is to clarify some of the notation and conventions we use throughout the paper, and to define two decision problems: one is the mixed-unitary detection problem, whose hardness is the primary focus of this paper, and the second is the unitary quadratic minimization problem, which serves as an intermediate problem through which an NP-complete problem is reduced to the mixed-unitary detection problem

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Summary

Introduction

In the theory of quantum information, quantum channels represent discrete-time changes in systems that can, in an idealized sense, be realized by physical processes. En}, the operator Ei,j is represented by the matrix having a 1 in entry (i, j) and all other entries 0.) We prove that it is NP-hard, with respect to polynomial-time Turing reductions, to determine whether or not a given quantum channel is mixed-unitary. We note that our main result can, be closely linked with the problem of separability testing, in the sense that testing if a channel is mixed-unitary may alternatively be formulated as a problem concerning the expression of a bipartite density operator in a certain way.

Preliminaries
Computational complexity
Linear algebra and quantum information
Problem statements
Reduction from graph 3-coloring to unitary quadratic optimization
The reduction
Analysis: yes-instances map to yes-instances
Analysis: no-instances map to no-instances
Reduction from unitary quadratic optimization to mixed-unitary detection
The number δ satisfies γ1
Full-dimensional real convex sets for mixed-unitary optimization
From unitary quadratic minimization to weak optimization
From weak membership to mixed-unitary detection
Conclusion
Full Text
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