In this paper we present a simple algorithm for representation of statistical data of any origin by complex probability amplitudes. Numerical simulation with Mathematica-6 is performed. The Bloch's sphere is used for visualization of results of numerical simulation. On the one hand, creation of such a quantum-like (QL) representation and its numerical approval is an important step in clarification of extremely complicated interrelation between classical and quantum randomness. On the other hand, it opens new possibilities for application the mathematical formalism of QM in other domains of science.
The aim of this paper is to apply a contextual probabilistic model (in the spirit of Mackey, Gudder, Ballentine) to represent and to generalize some results of quantum logic about possible macroscopic quantum-like (QL) behaviour. The crucial point is that our model provides QL-representation of macroscopic configurations in terms of complex probability amplitudes -- wave functions of such configurations. Thus, instead of the language of propositions which is common in quatum logic, we use the language of wave functions which is common in the conventional presentation of QM. We propose a quantum-like representation algorithm, QLRA, which maps probabilistic data of any origin in complex (or even hyperbolic) Hilbert space. On the one hand, this paper clarifyes some questions in foundations of QM, since some rather mystical quantum features are illustrated on the basis of behavior of macroscopic systems. On the other hand, the approach developed in this paper may be used e.g. in biology, sociology, or psychology. Our example of QL-representation of hidden macroscopic configurations can find natural applications in those domains of science.
We present a quantum-like (QL) model in that contexts (complexes of e.g. mental, social, biological, economic or even political conditions) are represented by complex probability amplitudes. This approach gives the possibility to apply the mathematical quantum formalism to probabilities induced in any domain of science. In our model quantum randomness appears not as irreducible randomness (as it is commonly accepted in conventional quantum mechanics, e.g., by von Neumann and Dirac), but as a consequence of obtaining incomplete information about a system. We pay main attention to the QL description of processing of incomplete information. Our QL model can be useful in cognitive, social and political sciences as well as economics and artificial intelligence. In this paper we consider in a more detail one special application -- QL modeling of brain's functioning. The brain is modeled as a QL-computer.
We discuss a new approach to simulate quantum algorithms using classical probabilistic bits and circuits. Each qubit (a two-level quantum system) is initially mapped to a vector in an eight dimensional probability space (equivalently, to a classical random variable with eight probabilistic outcomes). The key idea in this mapping is to store both the amplitude and phase information of the complex coefficients that describe the qubit state in the probabilities. Due to the identical tensor product structure of combining multiple quantum systems as well as multiple probability spaces,
n qubits are then mapped to a tensor product of
n 8-dimensional probabilistic vectors (i.e., the Hilbert space of dimension
2n is mapped to a probability space of dimension
8n). After this initial mapping, we show how to implement the analogs of single-qubit and two-qubit gates in the probability space using correlation-inducing operations on these classical random variables. The key defining feature of both the mapping to the probability space and the transformations in this space (i.e., operations on the random variables) is that they are not linear, but instead affine. Using this architecture, the evolution of the
2n complex coefficients of the quantum system can be tracked in the joint fully-correlated probabilities of the polynomial number of random variables. We then give specific procedures for implementing (1) the Deutsch-Jozsa algorithm, and (2) the Quantum Fourier Transform in the probability space. Identical to the Quantum case, simulating the Quantum Fourier Transform in the probability space requires
O(n) probabilistic bits and
O(n2) (i.e., quadratic in the number of quantum bits) operations.
Implementing a digital Earth requires processing an enormous amount of data. Compared with classical algorithms, quantum algorithms can take exponential or quadratic acceleration and are more suitable for processing big data. However, existing quantum models require massive resources to process data on the Earth due to their low compatibility with spheres. So, we propose a quantum representation of the Earth based on the Bloch sphere (QREB), which is tailor-made for the Earth. Based on this model, the scale transformation and specific rotation transformation only need to change one variable. And the time complexity of the quantum point of interest extraction algorithm based on this model is the square root of the classical algorithm. Moreover, in the example of extracting thin black lines on the basketball, the accuracy is improved from 50% to 87.5%. The QREB model can reduce the resources required to process spherical data and accelerate the realization of a digital Earth.
Quantum algorithms which are performed on quantum computers are hardly developed, because the principle of quantum computers are totally different from classical computers. We would like to discuss the way of visualization to assist understanding quantum algorithms. As the first step, we have developed visualization system of the Bloch sphere, which is the popular way to show the status of one quantum bit. In the system, we can see the Bloch sphere as 3DCG and interact with it. Next, we discuss the problems of visualizing multiple quantum bits and quantum algorithms.