🤖 AI Summary
This work addresses the efficient distribution of graph states—a class of pictorially representable multipartite entangled states—in quantum networks. We propose a novel distribution framework based on subgraph complementation, modeling graph-state generation as a sequence of local graph operations. First, we establish theoretical connections between subgraph complementation and fundamental graph-theoretic and entanglement measures: minimum rank, Schmidt rank, and entanglement degree. Second, leveraging local unitary transformations, parallel quantum gate scheduling, and realistic noise modeling, our approach achieves linear resource scaling in qubit count, classical communication bits, and EPR pair consumption. Compared to state-of-the-art methods, it delivers quadratic-to-exponential speedups in completion time; for dense graph states, it simultaneously attains quadratic time complexity reduction and exponential parallelism gain. Noise simulations confirm improved distribution fidelity.
📝 Abstract
Quantum networks are important for quantum communication, enabling tasks such as quantum teleportation, quantum key distribution, quantum sensing, and quantum error correction, often utilizing graph states, a specific class of multipartite entangled states that can be represented by graphs. We propose a novel approach for distributing graph states across a quantum network. We show that the distribution of graph states can be characterized by a system of subgraph complementations, which we also relate to the minimum rank of the underlying graph and the degree of entanglement quantified by the Schmidt-rank of the quantum state. We analyze resource usage for our algorithm and show that it improves on the number of qubits, bits for classical communication, and EPR pairs utilized, as compared to prior work. In fact, the number of local operations and resource consumption for our approach scales linearly in the number of vertices. This produces a quadratic improvement in completion time for several classes of graph states represented by dense graphs, which translates into an exponential improvement by allowing parallelization of gate operations. This leads to improved fidelities in the presence of noisy operations, as we show through simulation in the presence of noisy operations. We classify common classes of graph states, along with their optimal distribution time using subgraph complementations. We find a sequence of subgraph complementation operations to distribute an arbitrary graph state which we conjecture is close to the optimal sequence, and establish upper bounds on distribution time along with providing approximate greedy algorithms.
