It Takes (Only) Two: Adversarial Generator-Encoder Networks

We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparable quality to some recently-proposed more complex architectures

Quantum Algorithmic Gate-Based Computing: Grover Quantum Search Algorithm Design in Quantum Software Engineering

The difference between classical and quantum algorithms (QA) is following: problem solved by QA is coded in the structure of the quantum operators. Input to QA in this case is always the same. Output of QA says which problem coded. In some sense, give a function to QA to analyze and QA returns its property as an answer without quantitative computing. QA studies qualitative properties of the functions. The core of any QA is a set of unitary quantum operators or quantum gates. In practical representation, quantum gate is a unitary matrix with particular structure. The size of this matrix grows exponentially with an increase in the number of inputs, which significantly limits the QA simulation on a classical computer with von Neumann architecture. Quantum search algorithm (QSA) - models apply for the solution of computer science problems as searching in unstructured data base, quantum cryptography, engineering tasks, control system design, robotics, smart controllers, etc. Grovers algorithm is explained in details along with implementations on a local computer simulator. The presented article describes a practical approach to modeling one of the most famous QA on classical computers, the Grover algorithm.Comment: arXiv admin note: text overlap with arXiv:quant-ph/0112105 by other author

Fast quantum search algorithm modelling on conventional computers: Information analysis of termination problem

The simplest technique for simulating a quantum algorithm - QA described based on the direct matrix representation of the quantum operators. Using this approach, it is relatively simple to simulate the operation of a QA and to perform fidelity analysis. A more efficient fast QA simulation technique is based on computing all or part of the operator matrices on an as needed current computational basis. Using this technique, it is possible to avoid storing all or part of the operator matrices. The compute on demand approach benefits from a study of the quantum operators, and their structure so that the matrix elements can be computed more efficiently. Effective simulation of Grover quantum search algorithm as example on computer with classical architecture is considered