Projective Simulation was introduced as a novel approach to Artificial Intelligence. It involves a deliberation procedure that consists of a random walk on a graph of clips and allows for the learning agent to project itself into the future before committing to an action. Here we study and analyze a quantum mechanical version in which the random walk is performed by two kinds of Hamiltonians. The first kind is implemented by naively embedding the classical model in a quantum model by turning the clips into qubits. The other allows for storing clips in superpositions of qubits allowing for a potentially purely quantum mechanical learning procedure in which the perception of the environment is purely quantum mechanical but the action is classical. We lastly introduce the concept of interacting projective agents for both the classical and quantum mechanical case.
Projective Simulation was introduced as a novel approach to Artificial Intelligence. It involves a deliberation procedure that consists of a random walk on a graph of clips and allows for the learning agent to project itself into the future before committing to an action. Here we study and analyze a quantum mechanical version in which the random walk is performed by two kinds of Hamiltonians. The first kind is implemented by naively embedding the classical model in a quantum model by turning the clips into qubits. The other allows for storing clips in superpositions of qubits allowing for a potentially purely quantum mechanical learning procedure in which the perception of the environment is purely quantum mechanical but the action is classical. We lastly introduce the concept of interacting projective agents for both the classical and quantum mechanical case.
Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices. A variational approach to reinforcement learning has been recently proposed, incorporating linear-optical interferometers and a classical learning model known as projective simulation (PS). PS is a decision-making tool for reinforcement learning and can be classically represented as a random walk on a graph that describes the agent's memory. In its optical quantum version, this approach utilizes quantum walks of single photons on a mesh of tunable beamsplitters and phase shifters to select actions. In this work, we present the implementation of this algorithm on Ascella, a single-photon-based quantum computer from Quandela. The focus is drawn on solving a test bed task to showcase the potential of the quantum agent with respect to the classical agent. Published by the American Physical Society 2024
This paper investigates a novel approach to Quantum Key Distribution (QKD) enhanced by Quantum Projective Simulation (QPS), a quantum-inspired reinforcement learning model. By allowing agents to simulate future actions through quantum random walks over a memory network of quantum clips, QPS enables Alice and Bob to dynamically adapt their key distribution strategies based on past experiences. Simulation results show that the QPS-based QKD model achieves faster convergence to optimal strategies, improves resilience against eavesdropping attacks, and maintains a higher success probability compared to traditional QKD methods. These findings suggest that integrating QPS into QKD can significantly enhance the efficiency and security of quantum communication systems in dynamic and adversarial environments, positioning it as a promising candidate for future 6G networks. Index Terms—QKD, QPS, Quantum Random Walk, Artificial Intelligence.
Projective simulation is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speedup in deliberation time. Projective simulation agents have been applied successfully in the context of complex skill learning in robotics and the design of the state-of-the-art quantum experiments. In this paper, we study the performance of projective simulation in two benchmarking problems in navigation, namely, the grid world and the mountain car problem. The performance of projective simulation is compared to standard tabular reinforcement learning approaches, Q-learning, and SARSA. Our comparison demonstrates that the performance of projective simulation and standard learning approaches are qualitatively and quantitatively similar, while it is much easier to choose optimal model parameters in the case of projective simulation with reduced computational effort of one to two orders of magnitude. Our results show that the projective simulation model stands out for its simplicity in terms of the number of model parameters, which makes it simple to set up the learning agent in unknown task environments.
Abstract Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits. However, both approaches suffer from a clear limitation, that is a lack of interpretability. Here, we present a variational method to quantize projective simulation (PS), a reinforcement learning model aimed at interpretable artificial intelligence. Decision making in PS is modeled as a random walk on a graph describing the agent’s memory. To implement the quantized model, we consider quantum walks of single photons in a lattice of tunable Mach–Zehnder interferometers trained via variational algorithms. Using an example from transfer learning, we show that the quantized PS model can exploit quantum interference to acquire capabilities beyond those of its classical counterpart. Finally, we discuss the role of quantum interference for training and tracing the decision making process, paving the way for realizations of interpretable quantum learning agents.
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to exploit quantum resources specifically for the broader context of reinforcement learning were found is projective simulation. Projective simulation presents an agent-based reinforcement learning approach designed in a manner which may support quantum walk-based speedups. Although classical variants of projective simulation have been benchmarked against common reinforcement learning algorithms, very few formal theoretical analyses have been provided for its performance in standard learning scenarios. In this paper, we provide a detailed formal discussion of the properties of this model. Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes. This proof shows that a physically inspired approach to reinforcement learning can guarantee to converge.
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.