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A geometric view of quantum cellular automata

Jonathan McDonald, Paul M. Alsinga, Howard A. Blair

Published Aug 16, 2016• 0 citations
OpenAlex

Abstract

Nielsen, et al. [1, 2] proposed a view of quantum computation where determining optimal algorithms is equivalent to extremizing a geodesic length or cost functional. This view of optimization is highly suggestive of an action principle of the space of N-qubits interacting via local operations. The cost or action functional is given by the cost of evolution operators on local qubit operations leading to causal dynamics, as in Blute et. al. [3] Here we propose a view of information geometry for quantum algorithms where the inherent causal structure determines topology and information distances [4, 5] set the local geometry. This naturally leads to geometric characterization of hypersurfaces in a quantum cellular automaton. While in standard quantum circuit representations the connections between individual qubits, i.e. the topology, for hypersurfaces will be dynamic, quantum cellular automata have readily identifiable static hypersurface topologies determined via the quantum update rules. We demonstrate construction of quantum cellular automata geometry and discuss the utility of this approach for tracking entanglement and algorithm optimization.

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Nielsen, et al.1, 2 proposed a view of quantum computation where determining optimal algorithms is equivalent to extremizing a geodesic length or cost functional. This view of optimization is highly suggestive of an action principle of the space of N-qubits interacting via local operations. The cost or action functional is given by the cost of evolution operators on local qubit operations leading to causal dynamics, as in Blute et. al.3 Here we propose a view of information geometry for quantum algorithms where the inherent causal structure determines topology and information distances4, 5 set the local geometry. This naturally leads to geometric characterization of hypersurfaces in a quantum cellular automaton. While in standard quantum circuit representations the connections between individual qubits, i.e. the topology, for hypersurfaces will be dynamic, quantum cellular automata have readily identifiable static hypersurface topologies determined via the quantum update rules. We demonstrate construction of quantum cellular automata geometry and discuss the utility of this approach for tracking entanglement and algorithm optimization.

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Nielsen, et al. [1, 2] proposed a view of quantum computation where determining optimal algorithms is equivalent to extremizing a geodesic length or cost functional. This view of optimization is highly suggestive of an action principle of the space of N-qubits interacting via local operations. The cost or action functional is given by the cost of evolution operators on local qubit operations leading to causal dynamics, as in Blute et. al. [3] Here we propose a view of information geometry for quantum algorithms where the inherent causal structure determines topology and information distances [4, 5] set the local geometry. This naturally leads to geometric characterization of hypersurfaces in a quantum cellular automaton. While in standard quantum circuit representations the connections between individual qubits, i.e. the topology, for hypersurfaces will be dynamic, quantum cellular automata have readily identifiable static hypersurface topologies determined via the quantum update rules. We demonstrate construction of quantum cellular automata geometry and discuss the utility of this approach for tracking entanglement and algorithm optimization.
Nielsen, et al.1, 2 proposed a view of quantum computation where determining optimal algorithms is equivalent to extremizing a geodesic length or cost functional. This view of optimization is highly suggestive of an action principle of the space of N-qubits interacting via local operations. The cost or action functional is given by the cost of evolution operators on local qubit operations leading to causal dynamics, as in Blute et. al.3 Here we propose a view of information geometry for quantum algorithms where the inherent causal structure determines topology and information distances4, 5 set the local geometry. This naturally leads to geometric characterization of hypersurfaces in a quantum cellular automaton. While in standard quantum circuit representations the connections between individual qubits, i.e. the topology, for hypersurfaces will be dynamic, quantum cellular automata have readily identifiable static hypersurface topologies determined via the quantum update rules. We demonstrate construction of quantum cellular automata geometry and discuss the utility of this approach for tracking entanglement and algorithm optimization.
Quantum cellular automata consist in arrays of identical finite-dimensional quantum systems, evolving in discrete-time steps by iterating a unitary operator G. Moreover the global evolution G is required to be causal (it propagates information at a bounded speed) and translation-invariant (it acts everywhere the same). Quantum cellular automata provide a model/architecture for distributed quantum computation. More generally, they encompass most of discrete-space discrete-time quantum theory. We give an overview of their theory, with particular focus on structure results; computability and universality results; and quantum simulation results.
A quantum cellular automaton (QCA) is a unitary discrete-time evolution of a quantum many-body system, which can be described as a dynamics system , where A is any matrix and is a unitary operator representing the global evolution of QCA. And QCAs are characterized by locality (or causality). Among them, quantum circuits are the special classes of QCAs, which use unitary operators to control and transform the initial quantum state of qubit into a final quantum state, quantum circuits are special and widely studied in quantum information. A group is a set of elements combined with an operation, which satisfies associativity, identity and invertibility. This article shows that group theory provides the right language for studying the relation between QCAs and quantum circuits among them.
This paper proposes a novel approach to quantum algorithm design by leveraging the principles of cellular automata (CA). The core idea is to utilize CA as a hardware abstraction layer for quantum computation, mimicking the evolution rules of qubits. This allows for a parallelized design and optimization of quantum algorithms. Specifically, quantum gates are mapped to CA rules, enabling the simulation of qubit superposition and entanglement through cellular interactions and information transfer. This work explores the theoretical framework and outlines a methodology for designing quantum algorithms using CA, focusing on the potential for enhanced computational efficiency and scalability. The design methodology includes defining the CA grid, encoding quantum states into cellular configurations, translating quantum gates into CA rule updates, and analyzing the resulting algorithm execution. The presented framework offers a fundamentally different perspective on quantum algorithm development, potentially bridging the gap between classical algorithmic design and the unique characteristics of quantum systems. Further research will focus on exploring specific quantum algorithms and evaluating the performance of the CA-based approach.
Quantum topology, a fascinating field at the intersection of quantum mechanics and geometry, has emerged as a promising approach for developing novel quantum computing paradigms. This paper explores a novel computational framework predicated on the dynamic alteration of geometric topology induced by quantum entanglement. We introduce a model leveraging quantum entanglement to dynamically adjust the geometry of quantum systems, thereby enhancing computational efficiency and enabling new algorithmic possibilities. The core mechanism centers around the creation and manipulation of entangled states that govern the evolution of topological structures, ultimately impacting the performance of quantum algorithms. This work presents a foundational framework for exploring the potential of entanglement-driven geometry in quantum computation.
This paper investigates the application of non-linear topological geometry to algorithm design for quantum computation, specifically focusing on the optimization of quantum circuits and the design of novel quantum algorithms. We introduce a novel algorithm that leverages quantum entanglement to dynamically adjust the topology of geometric data structures, enabling efficient parameter optimization and quantum algorithm development. The core mechanism centers around the exploration of quantum entanglement as a means of manipulating and optimizing the geometric structure of the data, ultimately leading to improved performance in quantum computation. The algorithm's design addresses limitations of traditional quantum computing approaches by providing a mechanism for direct data manipulation and optimization, offering a potentially transformative impact on the field.