Survey of control performance in quantum information processing

There is a rich variety of physics underlying the fundamental gating operations for quantum information processing (QIP). A key aspect of a QIP system is how noise may enter during quantum operations and how suppressing or correcting its effects can best be addressed. Quantum control techniques have been developed to specifically address this effort, although a detailed classification of the compatibility of controls schemes with noise sources found in common quantum systems has not yet been performed. This work numerically examines the performance of modern control methods for suppressing decoherence in the presence of noise forms found in viable quantum systems. The noise-averaged process matrix for controlled one-qubit and two-qubit operations are calculated across noise found in systems driven by Markovian open quantum dynamics. Rather than aiming to describe the absolute best control scheme for a given physical circumstance, this work serves instead to classify quantum control behavior across a large class of noise forms so that opportunities for improving QIP performance may be identified.     

How exciting can a signal really be?

The rate of parameter convergence in a number of adaptive estimation schemes is related to the smallest eigenvalue of the average information matrix determined by the regression vector. Using a very simple examples, we illustrate that the input signals that maximize this minimum eigenvalue may be quite different from the input signals that optimize more classical input design criteria, e.g. D-optimal criterion.     

Adaptive control via parameter set estimation

An adaptive control system is examined where the traditional parameter estimator is replaced by a parameter set estimator which provides a measure of uncertainty of the estimated parameters. it is shown how to construct a parameter set estimator with the property that the true system is always in the model set, and furthermore, how to design the estimation experiment so that the set of uncertainty is as small as possible given some a priori information.     

Design of linear systems with saturating linear control and bounded states

A theorem is developed, based on the circle criterion, which gives sufficient conditions for the stability of a saturating linear control for a linear system with bounded states. This theorem can be applied to determine a range of gains for the linear control which ensures stability. It is shown by example that this approach results in an increased speed of response over that obtained by previous methods which do not account for state bounds.     

PEET: a Matlab tool for estimating physical gate errors in quantum information processing systems

A Physical Error Estimation Tool (PEET) is introduced in Matlab for predicting physical gate errors of quantum information processing (QIP) operations by constructing and then simulating gate sequences for a wide variety of user-defined, Hamiltonian-based physical systems. PEET is designed to accommodate the interdisciplinary needs of quantum computing design by assessing gate performance for users familiar with the underlying physics of QIP, as well as those interested in higher-level computing operations. The structure of PEET separates the bulk of the physical details of a system into Gate objects, while the construction of quantum computing gate operations are contained in GateSequence objects. Gate errors are estimated by Monte Carlo sampling of noisy gate operations. The main utility of PEET, though, is the implementation of QuantumControl methods that act to generate and then test gate sequence and pulse-shaping techniques for QIP performance. This work details the structure of PEET and gives instructive examples for its operation.     

Suboptimal control of linear time-invariant systems subject to control structure constraints

A method is presented for designing controllers for linear time-invariant systems whose states are not all available or accessible for measurement and where the structure of the controller is constrained to be a linear time-invariant combination of the measurable states of the system. Two types of structure constraints are considered: 1) each control channel is constrained to be a linear, time-invariant combination of one set of measurable states; 2) each control channel is constrained to he a linear, time-invariant combination of different sets of measurable states. The control system, subject to these constraints is selected such that the resulting closed-loop system performs as "near" to some known optimal system as is possible, i.e., suboptimal. The nearness of the optimal system to the suboptimal system is defined in two ways and thus, two types of suboptimal controllers are found.     

Stability theory for adaptive systems: Method of averaging and persistency of excitation

A method of averaging is developed for the stability analysis of linear differential equations with small time-varying coefficients which do not necessarily possess an average value. The technique is then applied to determine the stability of a linear equation which arises in the study of adaptive systems where the adaptive parameters are slowly varying. The stability conditions are stated in the frequency domain, which shows the relation between persistent excitation and unmodeled dynamics.     

A local stability analysis for a class of adaptive systems

An analysis of adaptive systems is presented where a localL_{infty}-stability is ensured under a persistent excitation condition.     

Quantum error correction via convex optimization

We show that the problem of designing a quantum information processing error correcting procedure can be cast as a bi-convex optimization problem, iterating between encoding and recovery, each being a semidefinite program. For a given encoding operator the problem is convex in the recovery operator. For a given method of recovery, the problem is convex in the encoding scheme. This allows us to derive new codes that are locally optimal. We present examples of such codes that can handle errors which are too strong for codes derived by analogy to classical error correction techniques.