lakadigital
Member
Quantum computing has been developing independently of machine learning for quite some time. Quantum computing promises significant performance advantages over traditional computing methods. For example, it may be possible to use quantum computing to break complex cryptographic schemes, thus rendering many ciphers easily crackable. While industrial grade quantum computers remain somewhat limited due to the relatively low number of qubits and noise-related technical difficulties, the field is advancing rapidly.
Cost: £299.00
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Learning Outcomes:
This course provides a comprehensive introduction to quantum computing, exploring its principles and applications in machine learning and optimization. Beginning with the foundational postulates of quantum mechanics, it establishes the theoretical framework necessary to understand quantum systems. The course then delves into variational circuits as machine learning methods, covering quantum neural networks, data encoding, and training techniques. It further explores quantum models as kernel methods, including optimization techniques such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm. Finally, the course examines potential quantum advantages, such as quantum annealing and Monte Carlo methods, with practical applications in finance and simulation.
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Course Modules & Outline
Module 1: Introduction to Quantum Computing
Principles of Quantum Mechanics
Quantum Neural Networks
Q annealing
Cost: £299.00----------------------------------------------------------------------------------------
Learning Outcomes:
This course provides a comprehensive introduction to quantum computing, exploring its principles and applications in machine learning and optimization. Beginning with the foundational postulates of quantum mechanics, it establishes the theoretical framework necessary to understand quantum systems. The course then delves into variational circuits as machine learning methods, covering quantum neural networks, data encoding, and training techniques. It further explores quantum models as kernel methods, including optimization techniques such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm. Finally, the course examines potential quantum advantages, such as quantum annealing and Monte Carlo methods, with practical applications in finance and simulation.
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Course Modules & Outline
Module 1: Introduction to Quantum Computing
Principles of Quantum Mechanics
- Postulate 1 – Statics
- Postulate 2 – Dynamics
- Postulate 3 – Measurement
- Postulate 4 – Composite systems
Quantum Neural Networks
- From classical to quantum
- Data encoding
- Training QNN
- QCBM
- Kernels
- QCBM vs RCBM
- Quantum reservoir Computing for error bounds
- Optimisation from a quantum perspective
- Variational Quantum Eigensolver
- Quantum Approximate Optimisation Algorithm
Q annealing
- Simulated annealing
- Quantum annealing
- Adding noise.....
- Example in Finance
- Classical Monte Carlo
- Quantum Monte Carlo
- Quantum simulation
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