Climbing Mount Scalable: Physical Resource Requirements for a Scalable Quantum Computer

The primary resource for quantum computation is Hilbert-space dimension. Whereas Hilbert space itself is an abstract construction, the number of dimensions available to a system is a physical quantity that requires physical resources. Avoiding a demand for an exponential amount of these resources places a fundamental constraint on the systems that are suitable for scalable quantum computation. To be scalable, the effective number of degrees of freedom in the computer must grow nearly linearly with the number of qubits in an equivalent qubit-based quantum computer.     

Second-order conditions in C 1,1 constrained vector optimization

We consider the constrained vector optimization problem min _C f(x), g(x) ∈ ?K, where f:? ⁿ →? ^m and g:? ⁿ →? ^p are C ^1,1 functions, and C ? ^m and K ? ^p are closed convex cones with nonempty interiors. Two type of solutions are important for our considerations, namely w-minimizers (weakly efficient points) and i-minimizers (isolated minimizers). We formulate and prove in terms of the Dini directional derivative second-order necessary conditions for a point x ⁰ to be a w-minimizer and second-order sufficient conditions for x ⁰ to be an i-minimizer of order two. We discuss the reversal of the sufficient conditions under suitable constraint qualifications of Kuhn-Tucker type. The obtained results improve the ones in Liu, Neittaanmäki, K?í?ek [21].     

Data-sparse approximation to a class of operator-valued functions

In earlier papers we developed a method for the data-sparse approximation of the solution operators for elliptic, parabolic, and hyperbolic PDEs based on the Dunford-Cauchy representation to the operator-valued functions of interest combined with the hierarchical matrix approximation of the operator resolvents. In the present paper, we discuss how these techniques can be applied to approximate a hierarchy of the operator-valued functions generated by an elliptic operator .

     

Quantum Hamiltonians with Weak Random Abstract Perturbation. I. Initial Length Scale Estimate

We study random Hamiltonians on finite-size cubes and waveguide segments of increasing diameter. The number of random parameters determining the operator is proportional to the volume of the cube. In the asymptotic regime where the cube size, and consequently the number of parameters as well, tends to infinity, we derive deterministic and probabilistic variational bounds on the lowest eigenvalue, i.e., the spectral minimum, as well as exponential off-diagonal decay of the Green function at energies above, but close to the overall spectral bottom.     

The Tikhonov regularization method in elastoplasticity

The numerical simulation of the mechanical behavior of industrial materials is widely used for viability verification, improvement and optimization of designs. Elastoplastic models have been used to forecast the mechanical behavior of different materials. The numerical solution of most elastoplastic models comes across problems of ill-condition matrices. A complete representation of the nonlinear behavior of such structures involves the nonlinear equilibrium path of the body and handling of singular (limit) points and/or bifurcation points. Several techniques to solve numerical problems associated to these points have been disposed in the specialized literature. Two examples are the load-controlled Newton–Raphson method and displacement controlled techniques. However, most of these methods fail due to convergence problems (ill-conditioning) in the neighborhood of limit points, specially when the structure presents snap-through or snap-back equilibrium paths. This study presents the main ideas and formalities of the Tikhonov regularization method and shows how this method can be used in the analysis of dynamic elastoplasticity problems. The study presents a rigorous mathematical demonstration of existence and uniqueness of the solution of well-posed dynamic elastoplasticity problems. The numerical solution of dynamic elastoplasticity problems using Tikhonov regularization is presented in this paper. The Galerkin method is used in this formulation. Effectiveness of Tikhonov’s approach in the regularization of the solution of elastoplasticity problems is demonstrated by means of some simple numerical examples.     

Serial MR imaging of intracranial metastases after radiosurgery

Purpose: To evaluate the spatiotemporal evolution of radiosurgical induced changes both in metastases and in normal brain tissue adjacent to the lesions by serial magnetic resonance (MR) imaging. Methods and Materials: Thirty-five intracranial metastases of different primaries were treated in 25 patients by single high-dose radiosurgery. MR images acquired before radiosurgery were available in all patients. Sixty-three follow-up MR studies were performed in these patients including T₂- and contrast-enhanced T₁-weighted MR images. The average follow-up time was 9 ± 5 months (mean ± standard deviation [SD]). Based on contrast-enhanced T₁-weighted MR images, tumor response was radiologically classified in the following four groups: stable disease was assumed if the average tumor diameter after treatment did not show a tumor shrinkage of more than 50% and an increase of more than 25%, partial remission as a shrinkage of tumor size of more than 50%, a disappearance of contrast-enhancing tumor as a complete remission, and an increase of tumor diameter of more than 25% as tumor progress. Moreover, we analysed signal changes on T₂-weighted images in brain parenchyma adjacent to the enhancing metastases. Results: The overall mean survival time was 10.5 ± 7 months, with a 1-year actuarial survival rate of 40%. Stable disease, partial or complete remission of the metastatic tumor was observed in 22 patients (88%). Central or homogeneous loss of contrast enhancement appeared to be a good prognostic sign for stable disease or partial remission. This association was statistically significant (p < 0.05). Three patients (12%) suffered from tumor progression. In eight patients (32%) with stable disease or partial remission, signal changes on T₂-weighted images were observed in tissue adjacent to the contrast enhancing lesions. A progression of the high signal on T₂-weighted images was seen in seven of the eight patients between 3 and 6 months after therapy, followed by a signal regression 6–18 months after irradiation. Conclusion: MR imaging is a sensitive imaging tool to evaluate tumor response as well as the presence or absence of adjacent parenchymal changes following radiosurgery. Loss of homogeneous or central contrast enhancement on Gd-enhanced MR images appeared to be a good prognostic sign for tumor response. Tumor shrinkage seems not to be dependent on time. In addition, most cases of radiation induced changes in normal brain parenchyma observed on T₂-weighted images seem to be self limited.     

Recursive state estimation for hybrid systems

The paper deals with recursive state estimation for hybrid systems. An unobservable state of such systems is changed both in a continuous and a discrete way. Fast and efficient online estimation of hybrid system state is desired in many application areas. The presented paper proposes to look at this problem via Bayesian filtering in the factorized (decomposed) form. General recursive solution is proposed as the probability density function, updated entry-wise. The paper summarizes general factorized filter specialized for (i) normal state-space models; (ii) multinomial state-space models with discrete observations; and (iii) hybrid systems. Illustrative experiments and comparison with one of the counterparts are provided.     

Asymptotic approximation of functions and their derivatives by Müller’s Gamma operators

We obtain the complete asymptotic expansion of the image functions of Müller’s Gamma operators and of their derivatives. All expansion coefficients are explicitly calculated. Moreover, we study linear combinations of Gamma operators having a better degree of approximation than the operators themselves. Using divided differences we define general classes of linear combinations of which special cases were recently introduced and investigated by other authors.