Conference

We study two related estimation problems involving phase- covariant quantum states. We first address the problem of phase estimation. We give optimal bounds for pure and mixed Gaussian states and find that for a fixed squeezing parameter a larger temperature can enhance the estimation fidelity. In addition we use state estimation concepts to give a benchmark that asses whether experimental implementations of quantum storage and teleportation protocols could be reproduced by classical means, i.e., by a measure and prepare strategy.

The field of linear optics quantum computing (LOQC) allows the construction of conditional gates using only linear optics and measurement. This quantum computing paradigm bypasses a seemingly serious problem in optical quantum computing: it appears to be very hard to produce a meaningful interaction between two single photons. But what if this obstacle were instead an advantage? By assuming that none of the physical components that make up an LOQC gate produce a direct photon-photon interaction, we dramatically reduce the space of gates which are possible for a given number of input and output qubits. In fact, by parametrizing a gate according to it\'s action on single photons, instead of on multiple photons, it is possible to exponentially reduce the number of measurements necessary to fully characterize an LOQC gate. In addition, this approach to LOQC process tomography may have additional experimental advantages when non-ideal input states are used for this characterization.

Quantum tomography and fidelity estimation of multi-partite systems is generally a time-consuming task. Nevertheless, this complexity can be reduced if the desired state can be characterized by certain symmetries measurable with the corresponding experimental setup. In this talk I could explain an efficient way (i.e., in polylog(d) time, with d the dimension of the Hilbert space) to perform tomography and estimate the fidelity of generalized coherent state (GCS) preparation. GCSs differ from the well known coherent states in that the associated Hilbert space is finite dimensional. In particular, the class of GCSs is very important in condensed matter applications. These results are useful to experimentalists seeking the simulations of some quantum systems, such as the Ising model in a transverse field. I\'d prefer to give a 30\' + talk late in the week, maybe on Thursday afternoon. Part of this work has been done in collaboration with ion-trap experimentalists J. Chiaverini and D. Berkeland, at Los Alamos National Laboratory. Rolando Somma.

Projections onto mutually unbiased bases (MUBs) have the ability to maximize information extraction per measurement and to minimize redundancy. I present an experimental demonstration of quantum state tomography of two-qubit polarization states that takes advantage of MUBs. Estimates of the state taken with this method have a measurably higher fidelity to the true state than estimates taken using standard measurement strategies. I explain how this advantage can be understood from the structure of the measurements we use.

A number of problems in quantum estimation can be formulated as a convex optimization [1]. Applications include: maximum likelihood estimation, optimal experiment design, quantum state detection, and quantum metrology under instrumentation constraints. This talk will draw on the work I have been involved with, e.g., [2], [3], [4]. Our work in optimal quantum error correction [5, 6] is also relevant. Great benefit is derived using an error model which is specific to the system. Obtaining the errors from tomography is a logical route. How to do this, however, is an open question. The constraint is the form required by the standard error-correction model upon which the optimization is constructed. I will present some ideas on how to do the tomography in this context. • Maximum Likelihood (ML) quantum estimation problems are easily formed as log-convex optimization problems [1]. These include estimation of the state (density), estimation of the distribution of known input states, estimation of the OSR elements for quantum process tomography, and estimation of the coefficients of a preselected basis set of OSR elements. Estimation of Hamiltonian parameters, unfortunately, is not a convex optimization. Associated with these estimation problems, including Hamiltonian parameter estimation, is an optimal experiment design (OED), which is convex, and which can determine the system configurations to maximize the estimation accuracy [2]. Experiments have been performed In Ian Walmsley’s Group at Oxford using these methods [7, 8]. • Quantum state detection can be formulated as a convex optimization problem in the matrices of the POVM which characterize the measurement apparatus. Minimizing the error probability is a semidefinite program (SDP) [9]. Maximizing the posterior probability of detection is a quasiconvex optimization problem [3]. • Quantum metrology subject to instrumentation constraints can be cast as a convex optimization problem [4]. Focusing on the single parameter case, the optimization problem is a linear program (LP). The Fisher information from the LP solution for the constrained problem can be compared to what is possible with no constraints, the Quantum Fisher Information. This approach is easily extended to the multi-parameter case. • Quantum Error Correction (QEC) that is optimized with respect to the specific system at hand can reduce ancilla overhead while raising error thresholds for fault-tolerant operation [5, 6, 10, 11]. The problem is cast as a bi-convex optimization problem, iterating between encoding and recovery, each being an SDP. In [5] we introduced two new aspects of this approach: (i) we modified the objective functions to account for robustness, and (ii) posed the problem in an indirect form which can be solved via a sequence of constrained least-squares problems. This opens the way for solving extremely large problems in a reasonable time period both from offline models and online from measured data, i.e., tomography.

When one makes squeezed light by downconversion of a pulsed pump laser, many temporal / spectral modes are simultaneously squeezed by different amounts. There is no guarantee that any of these modes matches the pump or the local oscillator used to measure the squeezing in homodyne detection. Therefore the state observed in homodyne detection is not pure, and many photons are present in the beam path that do not lie in the local oscillator\'s mode. These problems limit the fidelity of quantum information processing tasks with pulsed squeezed light. I will describe our attempts to make coherent state superpositions (sometimes called \'cat states\') using photon subtraction from squeezed light, the problems caused by multimode squeezing, and methods to characterize the contents of the many squeezed modes.

We propose and demonstrate experimentally a technique for estimating quantum-optical processes in the continuous-variable domain. The process data is determined by applying the process to a set of coherent states and measuring the output. The process output for an arbitrary input state can then be obtained from its Glauber-Sudarshan expansion. Although such expansion is generally singular, it can be arbitrarily well approximated with a regular function.

I will discuss our ongoing attempts to construct Symmetric Informationally Complete Positive Operator Valued Measures, or (minimal) two designs, out of Gaussian states. This poses difficulties both in principle, such as how to introduce a measure on the noncompact group of symplectic transformations, as well as in practice, such as how to truncate the space of states in an experimentally useful way. Joint work with Robin Blume-Kohout.

I\'ll survey recent results from quantum computing theory showing that,if one just wishes to learn enough about a quantum state to predictthe outcomes of most measurements that will actually be made, then itoften suffices to perform exponentially fewer measurements than wouldbe needed in quantum state tomography. I\'ll then describe the resultsof a numerical simulation of the new quantum state learning approach.The latter is joint work with Eyal Dechter.

We introduce the concept of tight POVMs. In analogy with tight frames, these are POVMs that are as close as possible to orthonormal bases for the space they span. We show that tight rank-one POVMs define the exact class of optimal measurements for linear tomography of quantum states. In this setting they are equivalent to complex projective 2-designs. We also show that tight POVMs define the optimal class of measurements on the probe state for ancilla-assisted process tomography of unital channels. In this setting they are equivalent to unitary 2-designs.