Quantum Computing from Addition-rule-based Cellular Automata

Shailendra Bhandari, Sebastian Overskott, Ioannis Adamopoulos +3 more

Shuichi Inokuchi, Kazumasa Honda, Hyen Yeal Lee +3 more

Bijections between sets may be seen as discrete (or crisp) unitary transformations used in quantum computations. So discrete quantum cellular automata are cellular automata with reversible transition functions. This note studies on 1d reversible cellular automata with triplet local rules.

Karoline Wiesner

Massimo Macucci

Jincheng Zhang

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.

Logan Edward Hillberry, Matthew Timothy Jones, David L. Vargas +6 more

Cellular automata are interacting classical bits that display diverse emergent behaviors, from fractals to random-number generators to Turing-complete computation. We discover that quantum cellular automata (QCA) can exhibit complexity in the sense of the complexity science that describes biology, sociology, and economics. QCA exhibit complexity when evolving under 'Goldilocks rules' that we define by balancing activity and stasis. Our Goldilocks rules generate robust dynamical features (entangled breathers), network structure and dynamics consistent with complexity, and persistent entropy fluctuations. Present-day experimental platforms—Rydberg arrays, trapped ions, and superconducting qubits—can implement our Goldilocks protocols, making testable the link between complexity science and quantum computation exposed by our QCA.