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arXiv:1407.2830 · New Journal of Physics

Quantum-enhanced deliberation of learning agents using trapped ions

Vedran Dunjko, Nicolai Friis, Hans J. Briegel

Published Jan 30, 2015• 35 citations
arXivHTMLPDF

Abstract

A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are 'upgraded' to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.

DOI
10.1088/1367-2630/17/2/023006

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A scheme that successfully employs quantum mechanics in the design of autonomous learning agents has recently been reported in the context of the projective simulation (PS) model for artificial intelligence. In that approach, the key feature of a PS agent, a specific type of memory which is explored via random walks, was shown to be amenable to quantization, allowing for a speed-up. In this work we propose an implementation of such classical and quantum agents in systems of trapped ions. We employ a generic construction by which the classical agents are 'upgraded' to their quantum counterparts by a nested process of adding coherent control, and we outline how this construction can be realized in ion traps. Our results provide a flexible modular architecture for the design of PS agents. Furthermore, we present numerical simulations of simple PS agents which analyze the robustness of our proposal under certain noise models.
We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.
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.
We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions. The decision-making process of a quantum learning agent within the projective simulation paradigm for machine learning is implemented in a system of two qubits. The latter are realized using hyperfine states of two frequency-addressed atomic ions exposed to a static magnetic field gradient. We show that the deliberation time of this quantum learning agent is quadratically improved with respect to comparable classical learning agents. The performance of this quantum-enhanced learning agent highlights the potential of scalable quantum processors taking advantage of machine learning.
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
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.
Quantum Reinforcement Learning (QRL) is an emerging field with many novel approaches suitable for noisy intermediate-scale quantum (NISQ) devices being proposed recently. Projective Simulation (PS) is a classical machine learning method based on random walks in a Directed Acyclic Graph (DAG), and is mainly used to train agents in a Reinforcement Learning setup. This graph corresponds to the Episodic Compositional Memory (ECM) of the agent. For the quantum version of PS we use quantum walks of single photons in quantum optical circuits, which go through tunable components (beamsplitters and phase shifters). We call this specific model Quantum Optical Projective Simulation (QOPS). In this work, we propose a general framework to translate any 2-layer ECM to a quantum optical circuit and an algorithm for solving quantum strategic games using QOPS. Moreover, we introduce QOPS with an entangled state as input. The application that we are using to evaluate the performance of the framework is the Prisoner's Dilemma. We executed the implementation using Altair's noisy simulator, designed to emulate a single photon-based quantum processor by Quandela. The algorithm provided promising results even in the presence of noise, which makes it a strong candidate for runs on single-photon-based quantum processors.