Quantum measurement is a fundamental cornerstone of experimental quantum computations. The main issues in current quantum measurement strategies are the high number of measurement rounds to determine a global optimal measurement output and the low success probability of finding a global optimal measurement output. Each measurement round requires preparing the quantum system and applying quantum operations and measurements with high-precision control in the physical layer. These issues result in extremely high-cost measurements with a low probability of success at the end of the measurement rounds. Here, we define a novel measurement for quantum computations called dense quantum measurement. The dense measurement strategy aims at fixing the main drawbacks of standard quantum measurements by achieving a significant reduction in the number of necessary measurement rounds and by radically improving the success probabilities of finding global optimal outputs. We provide application scenarios for quantum circuits with arbitrary unitary sequences, and prove that dense measurement theory provides an experimentally implementable solution for gate-model quantum computer architectures.
The von Neumann measurements are a special case of a more general measurement, the POVM measurement @cite_81 @cite_62 @cite_74 @cite_33 @cite_41 @cite_30 . Without loss of generality, the POVM is a generalized measurement that can be interpreted as a von Neumann measurement that utilizes an additional quantum system (called ancilla). The POVM measurement is mathematically described by a set of positive operators such that their sum is the identity operator @cite_15 @cite_70 @cite_48 . The POVM measurements therefore can be expressed in terms of projective measurements (see also Neumark's dilation theorem @cite_29 @cite_58 @cite_5 ).
@cite_30 @cite_62 @cite_33 @cite_41 @cite_70 @cite_48 @cite_29 @cite_81 @cite_74 @cite_5 @cite_15 @cite_58
1854343180 2649051464 1988174048 2027707976 2294622949 1801773550 2951522326 1758846674 1584328375 1593038947 2407417563 2083026109
I construct a positive-operator-valued measure (POVM) which has 2d rank-1 elements and which is informationally complete for generic pure states in d dimensions, thus confirming a conjecture made by Flammia, Silberfarb, and Caves (e-print quant-ph 0404137). I show that if a rank-1 POVM is required to be informationally complete for all pure states in d dimensions, it must have at least 3d-2 elements. I also show that, in a POVM which is informationally complete for all pure states in d dimensions, for any vector there must be at least 2d-1 POVM elements which do not annihilate that vector. It is a fundamental problem to decide how many copies of an unknown mixed quantum state are necessary and sufficient to determine the state. Previously, it was known only that estimating states to error @math in trace distance required @math copies for a @math -dimensional density matrix of rank @math . Here, we give a theoretical measurement scheme (POVM) that requires @math copies to estimate @math to error @math in infidelity, and a matching lower bound up to logarithmic factors. This implies @math copies suffice to achieve error @math in trace distance. We also prove that for independent (product) measurements, @math copies are necessary in order to achieve error @math in infidelity. For fixed @math , our measurement can be implemented on a quantum computer in time polynomial in @math . The most efficient way of obtaining information about the state of a quantum system is not always a direct measurement. It is sometimes preferable to extend the original Hilbert space of states into a larger space, and then to perform a quantum measurement in the enlarged space. Such an extension is always possible, by virtue of Neumark's theorem. The physical interpretation usually given to that theorem is the introduction of an auxiliary quantum system, prepared in a standard state, and the execution of a quantum measurement on both systems together. However, this widespread interpretation is unacceptable, because the statistical properties of the supposedly standard auxiliary system are inseparably entangled with those of the original, unknown system. A different method of preparing the auxiliary system is proposed, and shown to be physically acceptable. The effects of any quantum measurement can be described by a collection of measurement operators l_brace M sub m r_brace acting on the quantum state of the measured system. However, the Hilbert space formalism tends to obscure the relationship between the measurement results and the physical properties of the measured system. In this paper, a characterization of measurement operators in terms of measurement resolution and disturbance is developed. It is then possible to formulate uncertainty relations for the measurement process that are valid for arbitrary input states. The motivation of these concepts is explained from a quantum communication viewpoint. It is shown that the intuitive interpretation of uncertainty as a relation between measurement resolution and disturbance provides a valid description of measurement back action. Possible applications to quantum cryptography, quantum cloning, and teleportation are discussed. Binary measurements arise naturally in a variety of statistics and engineering applications. They may be inherent to the problem—for example, in determining the relationship between genetics and the presence or absence of a disease—or they may be a result of extreme quantization. A recent influx of literature has suggested that using prior signal information can greatly improve the ability to reconstruct a signal from binary measurements. This is exemplified by one-bit compressed sensing , which takes the compressed sensing model but assumes that only the sign of each measurement is retained. It has recently been shown that the number of one-bit measurements required for signal estimation mirrors that of unquantized compressed sensing. Indeed, @math -sparse signals in @math can be estimated (up to normalization) from @math one-bit measurements. Nevertheless, controlling the precise accuracy of the error estimate remains an open challenge. In this paper, we focus on optimizing the decay of the error as a function of the oversampling factor @math , where @math is the number of measurements. It is known that the error in reconstructing sparse signals from standard one-bit measurements is bounded below by @math . Without adjusting the measurement procedure, reducing this polynomial error decay rate is impossible. However, we show that an adaptive choice of the thresholds used for quantization can lower the error rate to @math . This improves upon guarantees for other methods of adaptive thresholding, such as sigma–delta quantization. We develop a general recursive strategy to achieve this exponential decay and two specific polynomial-time algorithms, which fall into this framework, one based on convex programming and one on hard thresholding. Our work bridges the one-bit compressed sensing model, in which the engineer controls the measurement procedure, to sigma–delta and successive approximation quantization. Moreover, the principle is extendable to signal reconstruction problems in a variety of binary statistical models as well as statistical estimation problems like logistic regression. We consider computation of the permanent of a positive (N × N) non-negative matrix, P = (Pji|i, j = 1, ..., N), or equivalently the problem of weighted counting of the perfect matchings over the complete bipartite graph KN, N. The problem is known to be of likely exponential complexity. Stated as the partition function Z of a graphical model, the problem allows for exact loop calculus representation (Chertkov M and Chernyak V 2006 Phys. Rev. E 72 065102) in terms of an interior minimum of the Bethe free energy functional over non-integer doubly stochastic matrix of marginal beliefs, β = (βji|i, j = 1, ..., N), also correspondent to a fixed point of the iterative message-passing algorithm of the belief propagation (BP) type. Our main result is an explicit expression of the exact partition function (permanent) in terms of the matrix of BP marginals, β, as Z = Perm(P) = ZBP Perm(βji(1 − βji)) ∏i, j(1 − βji), where ZBP is the BP expression for the permanent stated explicitly in terms of β. We give two derivations of the formula, a direct one based on the Bethe free energy and an alternative one combining the Ihara graph-ζ function and the loop calculus approaches. Assuming that the matrix β of the BP marginals is calculated, we provide two lower bounds and one upper bound to estimate the multiplicative term. Two complementary lower bounds are based on the Gurvits–van der Waerden theorem and on a relation between the modified permanent and determinant, respectively. Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between genetics and the presence or absence of a disease---or they may be a result of extreme quantization. In one-bit compressed sensing it has recently been shown that the number of one-bit measurements required for signal estimation mirrors that of unquantized compressed sensing. Indeed, @math -sparse signals in @math can be estimated (up to normalization) from @math one-bit measurements. Nevertheless, controlling the precise accuracy of the error estimate remains an open challenge. In this paper, we focus on optimizing the decay of the error as a function of the oversampling factor @math , where @math is the number of measurements. It is known that the error in reconstructing sparse signals from standard one-bit measurements is bounded below by @math . Without adjusting the measurement procedure, reducing this polynomial error decay rate is impossible. However, we show that an adaptive choice of the thresholds used for quantization may lower the error rate to @math . This improves upon guarantees for other methods of adaptive thresholding as proposed in Sigma-Delta quantization. We develop a general recursive strategy to achieve this exponential decay and two specific polynomial-time algorithms which fall into this framework, one based on convex programming and one on hard thresholding. This work is inspired by the one-bit compressed sensing model, in which the engineer controls the measurement procedure. Nevertheless, the principle is extendable to signal reconstruction problems in a variety of binary statistical models as well as statistical estimation problems like logistic regression. We study, in the context of quantum information and quantum communication, a configuration of devices that includes (1) a source of some unknown bipartite quantum state that is claimed to be the Bell state Φ+ and (2) two spatially separated but otherwise unknown measurement apparatus one on each side, that are each claimed to execute an orthogonal measurement at an angle θ ∈ -π 8, 0, π 8 that is chosen by the user. We show that, if the nine distinct probability distributions that are generated by the self checking configuration, one for each pair of angles, are consistent with the specifications, the source and the two measurement apparatus are guaranteed to be identical to the claimed specifications up to a local change of basis on each side. We discuss the connection with quantum cryptography. We precise for the first time the quantum behavior of a measurement apparatus in the framework of the usual interpretation of quantum physics. We show how such a behavior can also be studied by the retrodiction of pre-measurement states corresponding to its responses. We translate in terms of these states some interesting properties of the behavior of an apparatus, such as the projectivity, the fidelity, the non-Gaussian character, or the non-classicality of measurements performed by this one. We also propose an experimental procedure allowing the tomography of these pre-measurement states for optical detectors. We illustrate the relevance of these new notions for measurements, by evaluating them for two detectors widely used in quantum optics: the avalanche photodiode and the homodyne detection. GENERAL MEASURE THEORY Measures and Measurable Functions Lusin's and Egoroff's Theorems Integrals and Limit Theorems Product Measures, Fubini's Theorem, Lebesgue Measure Covering Theorems Differentiation of Radon Measures Lebesgue Points Approximate continuity Riesz Representation Theorem Weak Convergence and Compactness for Radon Measures HAUSDORFF MEASURE Definitions and Elementary Properties Hausdorff Dimension Isodiametric Inequality Densities Hausdorff Measure and Elementary Properties of Functions AREA AND COAREA FORMULAS Lipschitz Functions, Rademacher's Theorem Linear Maps and Jacobians The Area Formula The Coarea Formula SOBOLEV FUNCTIONS. Definitions And Elementary Properties. Approximation Traces. Extensions. Sobolev Inequalities Compactness. Capacity Quasicontinuity Precise Representations of Sobolev Functions. Differentiability on Lines BV FUNCTIONS AND SETS OF FINITE PERIMETER Definitions and Structure Theorem Approximation and Compactness Traces. Extensions. Coarea Formula for BV Functions. Isoperimetric Inequalities. The Reduced Boundary The Measure Theoretic Boundary Gauss-Green Theorem. Pointwise Properties of BV Functions Essential Variation on Lines A Criterion for Finite Perimeter. DIFFERENTIABILITY AND APPROXIMATION BY C1 FUNCTIONS. Lp Differentiability a.e. Approximate Differentiability Differentiability A.E. for W1,P (P > N). Convex Functions Second Derivatives a.e. for convex functions Whitney's Extension Theorem Approximation by C1 Functions NOTATION REFERENCES The paper quantifies and compares the exact asymptotic performance of multiple measurement vector (MMV) and distributed sensing (DS) models. Both models assume multiple measurement instances yk = Akxk + wk, k = 1, 2, …, K. The difference is that MMV involves identical measurement matrices whereas DS allows different matrices for different measurement instances. It has been recognized that DS works better than MMV empirically. However, the quantification of the performance difference is not available in the literature. Our contribution is to quantify the asymptotic performance of MMV and DS in the asymptotic regime that the dimensions of the measurement matrices approach infinity proportionally but the number of measurement instances K remains a constant. The case study and numerical results justify the accuracy of the performance quantification. The analysis technique is based on the state evolution for approximate message passing. Abstract It is a central point in the quantum theory of measurement that it is impossible to discriminate perfectly between two non-orthogonal quantum states if only one physical system is considered. Standard arguments give | p | q > | 2 as the essential quantity that limits the effectiveness of discrimination between | p > and | q >. It is here shown, however, that not | p | q > | 2 but rather | p | q > | plays the role of determining the measure of distinguishability of | p > and | q > if a classification with certainty is required.