arXiv:2606.11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation, given only the circuit's OpenQASM description and execution context. Our key insight is that qu
The increasing complexity of quantum circuits necessitates more efficient simulation methods, driving innovation in AI-assisted optimization to bridge the gap between classical and quantum computing capabilities.
This development significantly lowers the barrier to entry for quantum algorithm development and enables more rapid iteration and testing of quantum circuits, accelerating the timeline for practical quantum applications.
The ability to predict quantum circuit simulation performance and optimal approximation parameters using AI transforms quantum circuit design from a trial-and-error process into a more streamlined, data-driven methodology.
- · Quantum algorithm developers
- · Quantum hardware manufacturers (design iteration)
- · AI/ML research in scientific computing
- · Cloud quantum computing platforms
- · Classical brute-force simulation methods
- · Organizations without AI integration in quantum R&D
Faster and more cost-effective development of complex quantum algorithms.
Reduced need for high-end classical supercomputing resources for quantum simulation, democratizing access to quantum R&D.
Acceleration of quantum supremacy milestones and the discoverability of novel quantum phenomena.
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