Circuit size is nonlinear in depth

AbstractTwo fundamental complexity measures for a Boolean function f are its circuit depth d(f) and its circuit size c(f). It is shown that c≳ 14d·log2d for all f

Towards Verifying Nonlinear Integer Arithmetic

We eliminate a key roadblock to efficient verification of nonlinear integer arithmetic using CDCL SAT solvers, by showing how to construct short resolution proofs for many properties of the most widely used multiplier circuits. Such short proofs were conjectured not to exist. More precisely, we give n^{O(1)} size regular resolution proofs for arbitrary degree 2 identities on array, diagonal, and Booth multipliers and quasipolynomial- n^{O(\log n)} size proofs for these identities on Wallace tree multipliers.Comment: Expanded and simplified with improved result

CUBIT: Capacitive qUantum BIT

In this letter, it is proposed that cryogenic quantum bits can operate based on the nonlinearity due to the quantum capacitance of two-dimensional Dirac materials, and in particular graphene. The anharmonicity of a typical superconducting quantum bit is calculated, and the sensitivity of quantum bit frequency and anharmonicity with respect to temperature are found. Reasonable estimates reveal that a careful fabrication process can reveal expected properties, putting the context of quantum computing hardware into new perspectives.Comment: Published: 2 July 201

Nonlinear optical properties of photoresists for projection lithography

Optical beams are self-focused and self-trapped upon initiating crosslinking in photoresists. This nonlinear optical phenomenon is apparent only for low average optical intensities and produces index of refraction changes as large as 0.04. We propose using the self-focusing and self-trapping phenomenon in projection photolithography to enhance the resolution and depth of focus

Solid immersion lens applications for nanophotonic devices

Solid immersion lens (SIL) microscopy combines the advantages of conventional microscopy with those of near-field techniques, and is being increasingly adopted across a diverse range of technologies and applications. A comprehensive overview of the state-of-the-art in this rapidly expanding subject is therefore increasingly relevant. Important benefits are enabled by SIL-focusing, including an improved lateral and axial spatial profiling resolution when a SIL is used in laser-scanning microscopy or excitation, and an improved collection efficiency when a SIL is used in a light-collection mode, for example in fluorescence micro-spectroscopy. These advantages arise from the increase in numerical aperture (NA) that is provided by a SIL. Other SIL-enhanced improvements, for example spherical-aberration-free sub-surface imaging, are a fundamental consequence of the aplanatic imaging condition that results from the spherical geometry of the SIL. Beginning with an introduction to the theory of SIL imaging, the unique properties of SILs are exposed to provide advantages in applications involving the interrogation of photonic and electronic nanostructures. Such applications range from the sub-surface examination of the complex three-dimensional microstructures fabricated in silicon integrated circuits, to quantum photoluminescence and transmission measurements in semiconductor quantum dot nanostructures

Continuous-variable quantum neural networks

We introduce a general method for building neural networks on quantum computers. The quantum neural network is a variational quantum circuit built in the continuous-variable (CV) architecture, which encodes quantum information in continuous degrees of freedom such as the amplitudes of the electromagnetic field. This circuit contains a layered structure of continuously parameterized gates which is universal for CV quantum computation. Affine transformations and nonlinear activation functions, two key elements in neural networks, are enacted in the quantum network using Gaussian and non-Gaussian gates, respectively. The non-Gaussian gates provide both the nonlinearity and the universality of the model. Due to the structure of the CV model, the CV quantum neural network can encode highly nonlinear transformations while remaining completely unitary. We show how a classical network can be embedded into the quantum formalism and propose quantum versions of various specialized model such as convolutional, recurrent, and residual networks. Finally, we present numerous modeling experiments built with the Strawberry Fields software library. These experiments, including a classifier for fraud detection, a network which generates Tetris images, and a hybrid classical-quantum autoencoder, demonstrate the capability and adaptability of CV quantum neural networks