[Paper Review] The Future of Computing: Bits + Neurons + Qubits

Darı́o Gil, William M. J. Green|arXiv (Cornell University)|Nov 15, 2019

Quantum Computing Algorithms and Architecture53 references4 citations

TL;DR

This paper proposes a transformative future of computing built on the integration of classical bits, artificial neurons, and quantum qubits—combining digital logic, neuro-symbolic AI, and quantum computation to enable intelligent, mission-critical systems capable of solving problems beyond current classical or AI-only capabilities. The key contribution is a unified vision for next-generation computing that accelerates scientific discovery and enables broad, generalizable intelligence.

ABSTRACT

The laptops, cell phones, and internet applications commonplace in our daily lives are all rooted in the idea of zeros and ones - in bits. This foundational element originated from the combination of mathematics and Claude Shannon's Theory of Information. Coupled with the 50-year legacy of Moore's Law, the bit has propelled the digitization of our world. In recent years, artificial intelligence systems, merging neuron-inspired biology with information, have achieved superhuman accuracy in a range of narrow classification tasks by learning from labelled data. Advancing from Narrow AI to Broad AI will encompass the unification of learning and reasoning through neuro-symbolic systems, resulting in a form of AI which will perform multiple tasks, operate across multiple domains, and learn from small quantities of multi-modal input data. Finally, the union of physics and information led to the emergence of Quantum Information Theory and the development of the quantum bit - the qubit - forming the basis of quantum computers. We have built the first programmable quantum computers, and although the technology is still in its early days, these systems offer the potential to solve problems which even the most powerful classical computers cannot. The future of computing will look fundamentally different than it has in the past. It will not be based on more and cheaper bits alone, but rather, it will be built upon bits + neurons + qubits. This future will enable the next generation of intelligent mission-critical systems and accelerate the rate of science-driven discovery.

Motivation & Objective

• To articulate a vision for the future of computing beyond Moore's Law and classical digital systems.
• To address the limitations of narrow AI by proposing neuro-symbolic systems that unify learning and reasoning.
• To position quantum computing as a complementary pillar in a new computing paradigm.
• To outline how the convergence of bits, neurons, and qubits enables mission-critical, science-driven applications.

Proposed method

• Proposes a tripartite foundation for future computing: classical bits (digital logic), artificial neurons (neural networks), and quantum qubits (quantum information processing).
• Integrates insights from information theory, neuroscience, and quantum physics to define a new computing paradigm.
• Describes neuro-symbolic AI as a bridge between deep learning and symbolic reasoning, enabling multi-domain, few-shot learning.
• Highlights programmable quantum computers as emerging systems capable of solving intractable problems for classical computers.
• Emphasizes the role of multi-modal, small-data learning in advancing from narrow to broad artificial intelligence.
• Frames the convergence of these three elements as essential for accelerating scientific discovery and real-world applications.

Experimental results

Research questions

• RQ1 How can classical computing, artificial intelligence, and quantum computing be unified into a single transformative computing paradigm?
• RQ2 What are the key limitations of current narrow AI systems, and how can neuro-symbolic AI overcome them?
• RQ3 In what ways can quantum computing extend the capabilities of classical systems beyond their current limits?
• RQ4 How can small amounts of multi-modal data enable generalization across domains in AI systems?
• RQ5 What role will the integration of bits, neurons, and qubits play in accelerating scientific discovery?

Key findings

• The integration of bits, neurons, and qubits forms a new foundation for computing that transcends the limitations of classical digital systems.
• Neuro-symbolic AI enables systems to perform multiple tasks across domains and learn from minimal, multi-modal data.
• Programmable quantum computers have been built and show potential to solve problems intractable for classical computers.
• The convergence of information theory, neuroscience, and quantum physics enables a new era of intelligent, mission-critical systems.
• This unified paradigm is expected to significantly accelerate science-driven discovery and innovation.
• The future of computing will not rely solely on increasing bit density, but on synergistic integration across computational paradigms.