Photonic approaches to reinforcement learning are reviewed. We present single-photon-based and chaotic-laser-based decision makers to solve multi-armed bandit problems as well as entangled-photon-based social decision-making by utilizing the unique attributes of classical and quantum light.
Decision making in dynamically changing uncertain environments is one of the most important elements in information and communications technology, in applications ranging from resource assignment in data centers to wireless communications and search functions. Here, we review our research on the efficient physical realization or acceleration of decision making using photonics. The problem of interest is the multi-armed bandit (MAB) problem, where the purpose is to maximize the total rewards in unknown environments that involve difficult tradeoffs in the so-called exploration-exploitation dilemma. We describe the principle of solving MAB problems by utilizing the wave-particle duality of single photons in which the probabilistic attributes of single light quanta are employed for exploration. The principle is transformed into ultrafast laser chaos, where the chaotically oscillating irregular time series provides fast and scalable decision making abilities. The problem becomes even more difficult when multiple players are involved, in the so-called competitive MAB (CMAB) problem, where the expected value is related to maximizing the social benefit and ensuring fairness among individuals. We demonstrate that entangled photons can resolve the CMAB problem. Theoretical studies on photonic decision making are also reviewed, showing that six entities in total interact with each other according to an octahedral structure.
Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example of reinforcement learning. However, the bandit problem deals with a static environment where the agent's action does not influence the reward probabilities. This study aims to extend the conventional approach to a more general multi-agent reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of agents' actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, discontinuous bandit Q-learning, in view of a potential photonic implementation. Here, state-action pairs in the environment are regarded as slot machines in the context of the bandit problem and an updated amount of Q-value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a multi-agent architecture in which agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state-action pair selections among agents. We demonstrate that multi-agent reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.
Recently, extensive studies on photonic reinforcement learning to accelerate the process of calculation by exploiting the physical nature of light have been conducted. Previous studies utilized quantum interference of photons to achieve collective decision-making without choice conflicts when solving the competitive multi-armed bandit problem, a fundamental example in reinforcement learning. However, the bandit problem deals with a static environment where the agent’s actions do not influence the reward probabilities. This study aims to extend the conventional approach to a more general type of parallel reinforcement learning targeting the grid world problem. Unlike the conventional approach, the proposed scheme deals with a dynamic environment where the reward changes because of the agent’s actions. A successful photonic reinforcement learning scheme requires both a photonic system that contributes to the quality of learning and a suitable algorithm. This study proposes a novel learning algorithm, a modified bandit Q-learning method, in view of a potential photonic implementation. Here, state–action pairs in the environment are regarded as slot machines in the context of the bandit problem and a change in Q -value is regarded as the reward of the bandit problem. We perform numerical simulations to validate the effectiveness of the bandit algorithm. In addition, we propose a parallel architecture in which multiple agents are indirectly connected through quantum interference of light and quantum principles ensure the conflict-free property of state–action pair selections among agents. We demonstrate that parallel reinforcement learning can be accelerated owing to conflict avoidance among multiple agents.
Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.
Quantum optics utilizes the unique properties of light for computation or communication. In this work, we explore its ability to solve certain reinforcement learning tasks, with a particular view towards the scalability of the approach. Our method utilizes the Orbital Angular Momentum (OAM) of photons to solve the Competitive Multi-Armed Bandit (CMAB) problem while maximizing rewards. In particular, we encode each player's preferences in the OAM amplitudes, while the phases are optimized to avoid conflicts. We find that the proposed system is capable of solving the CMAB problem with a scalable number of options and demonstrates improved performance over existing techniques. As an example of a system with simple rules for solving complex tasks, our OAM-based method adds to the repertoire of functionality of quantum optics.
Understanding and using natural processes for intelligent functionalities, referred to as natural intelligence, has recently attracted interest from a variety of fields, including post-silicon computing for artificial intelligence and decision making in the behavioural sciences. In a past study, we successfully used the wave-particle duality of single photons to solve the two-armed bandit problem, which constitutes the foundation of reinforcement learning and decision making. In this study, we propose and confirm a hierarchical architecture for single-photon-based reinforcement learning and decision making that verifies the scalability of the principle. Specifically, the four-armed bandit problem is solved given zero prior knowledge in a two-layer hierarchical architecture, where polarization is autonomously adapted in order to effect adequate decision making using single-photon measurements. In the hierarchical structure, the notion of layer-dependent decisions emerges. The optimal solutions in the coarse layer and in the fine layer, however, conflict with each other in some contradictive problems. We show that while what we call a tournament strategy resolves such contradictions, the probabilistic nature of single photons allows for the direct location of the optimal solution even for contradictive problems, hence manifesting the exploration ability of single photons. This study provides insights into photon intelligence in hierarchical architectures for future artificial intelligence as well as the potential of natural processes for intelligent functionalities.