Authors:
Matthias Klusch、Jörg Lässig、Daniel Müssig、Antonio Macaluso、Frank K. Wilhelm
Paper:
https://arxiv.org/abs/2408.10726
Introduction
Quantum Artificial Intelligence (QAI) represents the convergence of quantum computing and artificial intelligence (AI), promising significant advancements in both fields. This paper provides an overview of the current state of QAI, highlighting key achievements and identifying open questions for future research. The focus is on the feasibility and potential of using quantum computing to solve complex AI problems and leveraging AI methods to enhance quantum computing.
Quantum Computing in a Nutshell
Quantum computing leverages the principles of quantum mechanics to process information, potentially surpassing classical computing capabilities. There are two primary models of quantum computing: gate-based quantum computing and adiabatic quantum computing.
Gate-Based Quantum Computing
Gate-based quantum computing uses quantum gates to manipulate quantum information, analogous to classical computing but with quantum bits (qubits) that can exist in superpositions of states. Qubits can be represented on the Bloch sphere, where their state is described by angles θ and φ.
Adiabatic Quantum Computing
Adiabatic quantum computing (AQC) relies on the adiabatic theorem, evolving a quantum system towards its lowest energy state to solve computational problems. Quantum annealing, a practical application of AQC principles, focuses on finding approximate solutions to combinatorial optimization problems.
Hybrid Quantum-Classical Computation
Hybrid quantum-classical computation combines quantum and classical resources to exploit near-term quantum devices. Variational quantum algorithms (VQAs) are a prominent example, using parameterized quantum circuits (PQCs) to address optimization problems.
Quantum Computing for AI
This section explores the use of quantum computing to solve computational problems in various AI subfields.
Quantum Machine Learning
Quantum machine learning (QML) aims to leverage quantum computing for traditional machine learning tasks. Hybrid quantum-classical approaches, such as quantum neural networks (QNNs), are used in supervised and reinforcement learning, potentially offering performance gains over classical methods.
Quantum Planning and Scheduling
Quantum automated planning and scheduling (QPS) research focuses on quantum-supported methods for automated planning and scheduling in AI. This includes quantum models for partially observable Markov decision processes (POMDPs) and quantum-supported job shop scheduling (JSS).
Quantum Computer Vision
Quantum computer vision (QCV) investigates quantum-supported methods for computer vision tasks, such as image recognition, classification, and segmentation. Quantum image processing techniques are used to represent and process digital images on quantum computers.
Quantum Natural Language Processing
Quantum natural language processing (QNLP) leverages quantum computing to model uncertainties and ambiguities in language. The Categorical Distributional Compositional (DisCoCat) model is commonly used to encode the meaning of words and phrases as quantum states and processes.
Quantum Agents and Multi-Agent Systems
Quantum multi-agent systems (QMAS) research focuses on developing autonomous agents and multi-agent systems for hybrid quantum-classical environments. Quantum-supported methods for coordination and cooperation in multi-agent systems are also explored.
AI for Quantum Computing
This section summarizes approaches to using AI, particularly machine learning (ML), to support various aspects of quantum computing.
Quantum Algorithm and Experiment Design
ML methods are used to assist in the design and implementation of quantum algorithms and experiments. Techniques such as reinforcement learning and projective simulation help discover new experimental methods and optimize protocols.
Near-Optimal PQC Parameter Search
Classical ML algorithms are employed to find near-optimal parameters for PQCs in hybrid quantum-classical algorithms, reducing the number of quantum circuit evaluations required during optimization.
Transpilation of Quantum Circuits
ML-based algorithms are utilized in the transpilation process to optimize quantum circuits for specific hardware backends. This includes evolutionary algorithms, neural networks, and reinforcement learning methods.
Quantum Error Correction and Mitigation
ML techniques are applied to improve quantum error correction and mitigation, enhancing the reliability and performance of quantum computing devices.
Calibration of Quantum Computing Devices
ML methods are used to calibrate and design quantum computing devices, optimizing their performance and ensuring accurate operation.
Conclusions
Quantum AI is an emerging interdisciplinary field with significant progress in both directions: using quantum computing for AI and leveraging AI for quantum computing. Future research should focus on practical applications, investigating the feasibility and potential of QAI methods on advanced quantum devices. Collaboration between the physics and computer science communities, along with sustained support from government and industry, is essential for the continued advancement of QAI.
This blog provides a detailed overview of the paper “Quantum Artificial Intelligence: A Brief Survey,” summarizing its key findings and insights across various subfields of QAI. The illustrations included help visualize complex concepts and enhance understanding.
