New Algorithms Beat Existing Methods For Quantum Circuits

Mark Webster, Stergios Koutsioumpas, and Dan E Browne University College London have developed new algorithms that reduce the number of two-qubit gates needed for quantum circuits. The team benchmarked optimal, A*, and greedy algorithms against existing methods, and show that their approach results in circuits with lower two-qubit gate count. Their approach surpasses previous results achieved using reinforcement learning, even discovering a circuit for the 23-qubit Golay code with a lower two-qubit gate count than previously known. The algorithms are available as an open-source Python package for use by the classical and quantum computing community.

Optimal Synthesis via Graph Isomorphism for Small Circuits

The Golay code, an important component in quantum error correction, now benefits from a newly discovered circuit requiring fewer two-qubit gates than previously achieved. Researchers developed algorithms that outperformed existing reinforcement learning approaches, identifying a more efficient circuit for the 23-qubit Golay code, a significant step toward practical quantum computation. This improvement demonstrates the potential of graph isomorphism in optimizing quantum circuit design, a technique previously unexplored for this specific application.