Reinforcement Learning Challenges in Designing Efficient Quantum Circuits
Reinforcement learning (RL) can be leveraged to improve the search for viable quantum circuit architectures, but current RL approaches face significant challenges in this domain.
Efficient Quantum Circuit Design Using a Standard Cell Approach for Neutral Atom Quantum Computers
The authors propose a standard cell approach to efficiently design and compile large-scale quantum circuits, particularly for neutral atom quantum computers. Their method leverages the regular structure of quantum circuits and qubit layouts to enable fast, scalable, and resource-efficient routing and compilation.
Optimization Model for Designing Minimum-Cost Multiple-Control Toffoli Quantum Circuits
This paper introduces a new optimization model and symmetry-breaking constraints to efficiently design minimum-cost quantum circuits using Multiple-Control Toffoli (MCT) gates.
Unitary k-designs: Distinguishing Random Number-Conserving Quantum Circuits from Haar Ensembles
While random number-conserving quantum circuits are useful for various quantum information processing tasks, their finite moments cannot be distinguished from those of the Haar ensemble on the entire group of number-conserving unitaries, except for very high-order moments.
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