Magic-Informed Quantum Architecture Search
📰 ArXiv cs.AI
Learn to optimize quantum architecture search using magic-informed techniques and Monte Carlo Tree Search with Graph Neural Networks
Action Steps
- Apply Monte Carlo Tree Search to explore quantum circuit designs
- Utilize Graph Neural Networks to estimate magic in quantum circuits
- Optimize quantum architecture search using magic-informed techniques
- Evaluate the performance of quantum circuits using magic as a resource
- Implement the proposed technique in a quantum computing framework to improve circuit design
Who Needs to Know This
Quantum computing researchers and engineers can benefit from this technique to improve quantum circuit design and optimization. It can be applied in teams working on quantum advantage and quantum architecture development
Key Insight
💡 Magic-informed techniques can improve quantum architecture search by controlling nonstabilizerness in quantum circuits
Share This
💡 Magic-informed quantum architecture search optimizes quantum circuits using Monte Carlo Tree Search & Graph Neural Networks #QuantumComputing #QAS
Full Article
Title: Magic-Informed Quantum Architecture Search
Abstract:
arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (QAS) technique that enables control over a quantum resource within the general framework of circuit design. Inspired by the AlphaGo approach, we tackle the problem with a Monte Carlo Tree Search technique equipped with a Graph Neural Network (GNN) that estimates the mag
Abstract:
arXiv:2605.03932v1 Announce Type: cross Abstract: Nonstabilizerness, commonly referred to as magic, is a fundamental resource underpinning quantum advantage. In this paper, we propose a magic-informed quantum architecture search (QAS) technique that enables control over a quantum resource within the general framework of circuit design. Inspired by the AlphaGo approach, we tackle the problem with a Monte Carlo Tree Search technique equipped with a Graph Neural Network (GNN) that estimates the mag
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