Artificial Quantum Intelligence Project

Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn +4 more

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Deepika Ghai, Suman Lata Tripathi, Ramandeep Sandhu +1 more

Emma Andrews, Prabhat Mishra

Vikrant Salar

This technical note presents a human-centered engineering framework for understanding quantum computing in the age of artificial intelligence. It argues that quantum computing should not be treated as magic, hype, or a universal replacement for classical computing, but as a disciplined computational model grounded in probability, linear algebra, measurement, physical systems, and resource constraints. The report connects quantum foundations with artificial intelligence, semiconductor systems, post-quantum security, scientific simulation, engineering education, and civilization-scale technology planning. It proposes a practical suitability framework for deciding when a quantum-AI approach is technically justified, resource-feasible, explainable, and socially useful. This report is a concise scholarly companion to the broader book project Quantum Computing in the Age of Artificial Intelligence: Engineering the Next Civilization by Vikrant Salar, Founder & Principal of SALAR Technologies LLC. It is intended for researchers, engineers, educators, technical founders, policy-minded readers, and reviewers interested in quantum computing, AI-assisted science, hybrid computing systems, post-quantum cryptography, and responsible technology development.

Vijayarangan Natarajan

Alexey A. Melnikov, Adi Makmal, Vedran Dunjko +1 more

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.