Quantum computers are regarded as a technology that will break Moore’s law. Recently, research on quantum deep learning, which processes data with logarithm complexity through a quantum neural network (QNN), is attracting attention. However, the full potential of QNNs has not been discovered yet. For this, we focus on the meta-characteristic of QNN that the quantum state is a knowledge base, and the measurement basis is a kernel to output. Moreover, one of the quantum measurement properties is easy initialization and transformation while requiring low memory and computation. Motivated by these features, this paper aims to investigate the full potential of QNNs in multi-agent reinforcement learning (MARL) and continual learning, named quantum multi-agent meta reinforcement learning (QMAMRL). To realize QMAMRL, we propose meta-QNN angle training, local QNN pole training, and pole initialization. In meta-QNN angle training, artificial noises are added on a measurement basis (i.e., pole) to improve knowledge. Then, local QNN pole training realizes the cooperation of multiple agents. Finally, pole initialization helps QNNs to achieve an adaptation to varying environments. Via extensive experiments and convergence analysis, we corroborate that our proposed QMAMRL framework has new properties that the previous algorithms have not.

Quantum Multi-Agent Meta Reinforcement Learning

Quantum convolutional neural network (QCNN) has just become an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic features using quantum-based ansatz, it should use only a finite number of qubits to prevent barren plateaus, and it introduces the lack of feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)

Posters & Demos III

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Joongheon Kim

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Presentations

Quantum Multi-Agent Meta Reinforcement Learning

FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features (Student Abstract)

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