14 Quantum computing for solving data association problems
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Abstract
This chapter explores the intersection of sensor data fusion, target tracking, and quantum computing. It begins with a foundational introduction to quantum computing, covering key concepts, such as qubits, the Bloch sphere, and quantum gates. The chapter then delves into Grover’s algorithm and adiabatic quantum computing. The latter is then used to solve the data association problem in multi-target tracking by a maximum a posteriori estimate of the association matrix. Specifically, it discusses how adiabatic quantum computing can be applied to the k-rooks problem, providing a general formulation and showcasing exemplary results that demonstrate the advantages of quantum approaches in sensor data fusion.
Abstract
This chapter explores the intersection of sensor data fusion, target tracking, and quantum computing. It begins with a foundational introduction to quantum computing, covering key concepts, such as qubits, the Bloch sphere, and quantum gates. The chapter then delves into Grover’s algorithm and adiabatic quantum computing. The latter is then used to solve the data association problem in multi-target tracking by a maximum a posteriori estimate of the association matrix. Specifically, it discusses how adiabatic quantum computing can be applied to the k-rooks problem, providing a general formulation and showcasing exemplary results that demonstrate the advantages of quantum approaches in sensor data fusion.
Chapters in this book
- Frontmatter I
- Dedication V
- Foreword VII
- Preface IX
- Contents XXIII
-
Part I Artificially intelligent sensing
- 1 Sound source classification using deep learning image classification networks 3
- 2 Advances in array calibration 19
- 3 Optimal sensor placement using genetic algorithms 37
-
Part II Data-driven learning algorithms
- 4 Machine learning for electronic intelligence 59
- 5 Trajectory optimization with reinforcement learning 75
- 6 Data-driven state prediction and target tracking 93
-
Part III Discussion of advanced applications
- 7 Track-before-detect for passive radar 109
- 8 Data fusion for reconnaissance of radio nuclides 125
-
Part IV Managing multifunctional sensors
- 9 Multifunction RF sensor management 147
- 10 Perspectives on artificial intelligence in sensor resources management 161
- 11 Intelligent sensor network management 175
-
Part V Quantum algorithms for data fusion
- 12 Quantum algorithms for data fusion 193
- 13 Indistinguishability and anti-symmetry in multiple target tracking 205
- 14 Quantum computing for solving data association problems 225
-
Part VI Issues of certification and ethical alignment
- 15 Sensor data integrity in maritime multi-sensor networks 247
- 16 Explainable and certifiable AI 265
- 17 Ethical issues of AI-based sensing 275
- List of abbreviations
- Index 293
Chapters in this book
- Frontmatter I
- Dedication V
- Foreword VII
- Preface IX
- Contents XXIII
-
Part I Artificially intelligent sensing
- 1 Sound source classification using deep learning image classification networks 3
- 2 Advances in array calibration 19
- 3 Optimal sensor placement using genetic algorithms 37
-
Part II Data-driven learning algorithms
- 4 Machine learning for electronic intelligence 59
- 5 Trajectory optimization with reinforcement learning 75
- 6 Data-driven state prediction and target tracking 93
-
Part III Discussion of advanced applications
- 7 Track-before-detect for passive radar 109
- 8 Data fusion for reconnaissance of radio nuclides 125
-
Part IV Managing multifunctional sensors
- 9 Multifunction RF sensor management 147
- 10 Perspectives on artificial intelligence in sensor resources management 161
- 11 Intelligent sensor network management 175
-
Part V Quantum algorithms for data fusion
- 12 Quantum algorithms for data fusion 193
- 13 Indistinguishability and anti-symmetry in multiple target tracking 205
- 14 Quantum computing for solving data association problems 225
-
Part VI Issues of certification and ethical alignment
- 15 Sensor data integrity in maritime multi-sensor networks 247
- 16 Explainable and certifiable AI 265
- 17 Ethical issues of AI-based sensing 275
- List of abbreviations
- Index 293
