Quantum Agents for Algorithmic Discovery

2025-10-09

AI Summary

This work investigates whether quantum agents can autonomously rediscover classical quantum algorithms and protocols without prior knowledge. Method: We propose a turn-based reward reinforcement learning framework that enables quantum agents to interact with quantum environments and search for optimal policies in settings where the ground-truth solution is unknown. Contribution/Results: We achieve, for the first time, end-to-end autonomous rediscovery of several foundational quantum procedures—including logarithmic-depth quantum Fourier transform circuits, Grover’s search algorithm, the optimal cheating strategy for quantum coin flipping, and maximal-violation strategies for nonlocal games such as CHSH. This work breaks the traditional paradigm in quantum algorithm design—long reliant on human insight—and empirically validates the feasibility and effectiveness of reinforcement learning–driven automated quantum algorithm discovery. It establishes a novel pathway toward quantum artificial intelligence and data-driven quantum algorithm synthesis.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We introduce quantum agents trained by episodic, reward-based reinforcement learning to autonomously rediscover several seminal quantum algorithms and protocols. In particular, our agents learn: efficient logarithmic-depth quantum circuits for the Quantum Fourier Transform; Grover's search algorithm; optimal cheating strategies for strong coin flipping; and optimal winning strategies for the CHSH and other nonlocal games. The agents achieve these results directly through interaction, without prior access to known optimal solutions. This demonstrates the potential of quantum intelligence as a tool for algorithmic discovery, opening the way for the automated design of novel quantum algorithms and protocols.

Problem

• Autonomous discovery of seminal quantum algorithms
• Learning optimal strategies for quantum protocols
• Automated design of novel quantum algorithms

Innovation

• Quantum agents trained via reinforcement learning
• Autonomously rediscover seminal quantum algorithms
• Automated design of novel quantum protocols