The future of computing

Shrinkhala Shinghai, Jyotir Moy Chatterjee

AI Trinity Labo

This paper introduces Quantum Future-Trial Computing (QFTC), a new computational paradigm that extends standard quantum computation by exploiting reversible geometric time-evolution. QFTC enables a quantum processor to explore multiple future computational branches, roll back to its initial state without decoherence, and selectively commit only the successful branches. This “try-future then rewind” architecture produces computational capabilities beyond BQP, offering ultra-efficient search, optimization, quantum simulation, and AI-driven parameter selection. The framework formalizes future-trial operators, rollback operators, and commit mechanisms, establishing QFTC as a next-generation model for superclassical computation.

Richard P. Feynman

Daniel Benjamin Cordova, Osman Guzide

Quantum computers once only existed in dreams and science fiction. Now, they are very much a reality. With quantum computers entering the market, questions are being asked. What is a quantum computer? What does it do? What are some of its features? And what is the future of quantum computers? This paper presents an analysis of quantum computers’ purpose, functionality, and future uses.

Napat Ngoenriang, Minrui Xu, Jiawen Kang +3 more

With the advantages of high-speed parallel processing, quantum computers can efficiently solve large-scale complex optimization problems in future networks. However, due to the uncertain qubit fidelity and quantum channel noise, distributed quantum computing, which relies on quantum networks connected through entanglement, faces many challenges in exchanging information across quantum computers. In this article, we propose an adaptive distributed quantum computing approach, called DQC2O, to manage quantum computers and quantum networks for solving optimization tasks in future networks. Firstly, we describe the fundamentals of quantum computing and its distributed concept in quantum networks. Secondly, to address the uncertainty of future demands of collaborative optimization tasks and instability over quantum networks, we propose a quantum resource allocation scheme based on stochastic programming for minimizing quantum resource consumption. Finally, based on the proposed approach, we discuss the potential military applications of collaborative optimization in future networks, such as smart grid management, IoT cooperation, and semantic communications. Promising research directions that can lead to the design and implementation of future distributed quantum computing frameworks are also highlighted.