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Simulating Complex Quantum Dynamics

Yaakov S. Weinstein, C. Stephen Hellberg

Published Feb 20, 2004• 0 citations
OpenAlex

Abstract

We demonstrate scalable quantum algorithms that realize pseudo-random operators that reproduce statistical properties of the three universal classes of random matrices: unitary, symmetric, and symplectic. These algorithms can be used to simulate and study characteristics of many complex quantum systems and processes which are well described by random matrices, such as the nuclear level spacings of large atoms and chaotic scattering. Modified versions of the algorithms are introduced for quantum cellular automata.

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We demonstrate scalable quantum algorithms that realize pseudo-random operators that reproduce statistical properties of the three universal classes of random matrices: unitary, symmetric, and symplectic. These algorithms can be used to simulate and study characteristics of many complex quantum systems and processes which are well described by random matrices, such as the nuclear level spacings of large atoms and chaotic scattering. Modified versions of the algorithms are introduced for quantum cellular automata.
Quantum computation based on quantum-cellular automata (QCA) can greatly reduce the control and precision necessary for experimental implementations of quantum information processing. A QCA system consists of a few species of qubits in which all qubits of a species evolve in parallel. We show that, in spite of its inherent constraints, a QCA system can be used to study complex quantum dynamics. To this aim, we demonstrate scalable operations on a QCA system that fulfill statistical criteria of randomness and explore which criteria of randomness can be fulfilled by operators from various QCA architectures. Other means of realizing random operators with only a few independent operators are also discussed.
In close analogy to the fundamental role of random numbers in classical information theory, random operators are a basic component of quantum information theory. Unfortunately, the implementation of random unitary operators on a quantum processor is exponentially hard. Here we introduce a method for generating pseudo-random unitary operators that can reproduce those statistical properties of random unitary operators most relevant to quantum information tasks. This method requires exponentially fewer resources, and hence enables the practical application of random unitary operators in quantum communication and information processing protocols. Using a nuclear magnetic resonance quantum processor, we were able to realize pseudorandom unitary operators that reproduce the expected random distribution of matrix elements.
We demonstrate the connection between an operator's matrix element distribution and entangling power via numerical simulations of random, pseudo-random, and quantum chaotic operators. Creating operators with a random distribution of matrix elements is more difficult than creating operators that reproduce other statistical properties of random matrices. Thus, operators that fulfill many random matrix statistical properties may not generate states of high multi-partite entanglement. To quantify the randomness of various statistical distributions and, by extension, entangling power, we use properties of interpolating ensembles that transition between integrable and random matrix ensembles.
Quantum-enhanced simulation of cellular automata aims to surpass current limitations by leveraging quantum mechanics to model emergent behavior at a fundamental level within cellular automata. This paper introduces a novel algorithm for simulating long-term evolution of cellular automata patterns, incorporating quantum state evolution to enhance accuracy and potentially unlock novel emergent properties. The core mechanism involves implementing a quantum-enhanced 'quantum state evolution' process to simulate the long-term dynamics of cellular automata. Current cellular automata simulations often rely on approximations, while quantum enhancement offers a more accurate representation of complex system dynamics. This research explores the potential of quantum mechanics to fundamentally alter the simulation process, leading to more realistic and insightful models.
This paper presents a novel random matrix generation algorithm based on quantum entanglement. The algorithm leverages the unique properties of quantum entanglement to generate complex matrix structures with adaptive probability distributions, offering enhanced simulation capabilities for intricate systems. Traditional random matrix generation methods often lack the depth of quantum entanglement integration, resulting in a significant improvement in the algorithm's ability to produce robust and statistically diverse matrices. The core mechanism involves carefully manipulating entangled quantum states to influence the matrix generation process, leading to a substantially increased level of complexity and adaptability. We demonstrate the effectiveness of this approach through simulations of complex systems, showcasing the algorithm's capacity to generate matrices exhibiting intricate entanglement topologies.