Linear quantum feedback networks

Gough, John E.; Gohm, Rolf; Yanagisawa, Masahiro

doi:10.1103/physreva.78.062104

Cited by 88 publications

(121 citation statements)

References 19 publications

Supporting

Mentioning

121

Contrasting

Order By: Relevance

“…In this paper, we focus on the class of passive linear quantum system [21], [22], [23], [24], which serves as a device for several applications in quantum information technology, such as entanglement generation [25], [26], [27], [28], [29], quantum memory [30], [31], [32], [33], [34], [35], and linear quantum computing [36]. Analyzing this important class of systems provides the foundation for the general case, but it has a clear interest in its own right in the context of estimation, as described later in this section.…”

Section: Introductionmentioning

confidence: 99%

System Identification for Passive Linear Quantum Systems

Guţǎ

Yamamoto

2016

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-System identification is a key enabling component for the implementation of quantum technologies, including quantum control. In this paper, we consider the class of passive linear input-output systems, and investigate several basic questions: (1) which parameters can be identified? (2) Given sufficient inputoutput data, how do we reconstruct the system parameters? (3) How can we optimize the estimation precision by preparing appropriate input states and performing measurements on the output? We show that minimal systems can be identified up to a unitary transformation on the modes, and systems satisfying a Hamiltonian connectivity condition called "infecting" are completely identifiable. We propose a frequency domain design based on a Fisher information criterion, for optimizing the estimation precision for coherent input state. As a consequence of the unitarity of the transfer function, we show that the Heisenberg limit with respect to the input energy can be achieved using non-classical input states.Index Terms-Quantum information and control; System identification; Linear systems; Estimation; Stochastic systems I. INTRODUCTION We are currently witnessing the beginning of a quantum engineering revolution [1], marking a shift from "classical devices" which are macroscopic systems described by deterministic or stochastic equations, to "quantum devices" which exploit fundamental properties of quantum mechanics, with applications ranging from computation to secure communication and metrology [2], [3]. While control theory was developed from the need for predictability in the behavior of "classical" dynamical systems, quantum filtering and quantum feedback control theory [4], [5], [6] deal with similar questions in the mathematical framework of quantum dynamical systems.System identification is an essential component of control theory, which deals with the estimation of unknown dynamical parameters of input-output systems; in particular, the identification of linear systems is a well studied subject in classical systems theory [7]. A similar task arises in the quantum setup, and various aspects of the quantum system identification problem have been considered in the recent literature, cf.

show abstract

Section: Introductionmentioning

confidence: 99%

System Identification for Passive Linear Quantum Systems

Guţǎ

Yamamoto

2016

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

show abstract

“…Refs. [17][18][19], but the type we will focus on here is the quantum feedback network developed by one of the authors with James 18,[20][21][22][23][24] (see Ref. 25 for a recent review).…”

Section: Introductionmentioning

confidence: 99%

Coherent feedback control in quantum transport

Emary

Gough

2014

Phys. Rev. B

View full text Add to dashboard Cite

We discuss control of the quantum-transport properties of a mesoscopic device by connecting it in a coherent feedback loop with a quantum-mechanical controller. We work in a scattering approach and derive results for the combined scattering matrix of the device-controller system and determine the conditions under which the controller can exert ideal control on the output characteristics. As concrete example we consider the use of feedback to optimise the conductance of a chaotic quantum dot and investigate effects of controller dimension and decoherence. In both respects we find that the performance of the feedback geometry is well in excess of that offered by a simple series configuration.

show abstract

“…[24], [25], [23], [26], [17], [12], [13]. To illustrate the power and utility of our quantum network modeling methodology, we analyze several examples from this literature.…”

Section: Introductionmentioning

confidence: 99%

The Series Product and Its Application to Quantum Feedforward and Feedback Networks

Gough

James

2009

IEEE Trans. Automat. Contr.

428

633

View full text Add to dashboard Cite

The purpose of this paper is to present simple and general algebraic methods for describing series connections in quantum networks. These methods build on and generalize existing methods for series (or cascade) connections by allowing for more general interfaces, and by introducing an efficient algebraic tool, the series product. We also introduce another product, which we call the concatenation product, that is useful for assembling and representing systems without necessarily having connections. We show how the concatenation and series products can be used to describe feedforward and feedback networks. A selection of examples from the quantum control literature are analyzed to illustrate the utility of our network modeling methodology.

show abstract

Linear quantum feedback networks

Cited by 88 publications

References 19 publications

System Identification for Passive Linear Quantum Systems

System Identification for Passive Linear Quantum Systems

Coherent feedback control in quantum transport

The Series Product and Its Application to Quantum Feedforward and Feedback Networks

Contact Info

Product

Resources

About