matthew hofmann

Hello, my name is Matthew Hofmann, and I am a researcher deeply engaged in exploring the convergence of quantum computing and artificial intelligence. My academic background combines theoretical physics and machine learning, which naturally led me to investigate how quantum principles—such as superposition, entanglement, and probabilistic amplitudes—can be harnessed to enhance the structure and performance of AI models.

Currently, my work focuses on the development of hybrid quantum-classical architectures that aim to accelerate AI inference and expand the semantic reasoning capabilities of large language models (LLMs). I am particularly interested in designing frameworks where quantum computing supports tasks like high-dimensional optimization, contextual ambiguity resolution, and generative modeling under uncertainty.

In collaboration with interdisciplinary teams, I have built simulation environments where GPT-based models interact with quantum circuit simulators (such as Qiskit or Pennylane) to interpret measurement outcomes, design adaptive circuits, and explore novel forms of quantum-semantic representation. Through this research, I aim to contribute to the emerging field of Quantum-AI not just as a theoretical exercise, but as a practical paradigm for next-generation computation.

I believe that the integration of AI and quantum computing holds transformative potential for fields such as cryptography, materials science, and fundamental physics modeling. I am excited to contribute to this frontier by leveraging AI’s expressive power and quantum computing’s parallelism, with the goal of creating models that can reason about uncertainty, abstraction, and complexity in ways classical systems cannot.

This implementation combines:

1. Parameterized quantum circuits with entangling gates and rotation layers 1

2. Hybrid quantum-classical architecture using TensorFlow Quantum 1

3. Quantum data encoding through parameterized circuits 2

4. Classical post-processing layers for decision making 3

5. End-to-end training using gradient descent optimization 3

The code demonstrates:

• Quantum circuit construction using Cirq

• Quantum expectation calculation

• Classical neural network integration

• Training loop implementation

• Hybrid model architecture