Jun-Yi Wu, Pablo Andrés-Martínez, Tim Forrer, Daniel Simon Mills, Kosuke Matsui, Luciana Henaut, Kentarô Yamamoto, Akihito Soeda, Ross Duncan, Mio Murao
We introduce an embedding-enhanced nonlocal-handling technique to save the entanglement consumption in distributed quantum computing (DQC) between two quantum processing units. The embedding technique is incorporated into the Steiner-tree distribution for DQC over heterogeneous networks.
In noisy intermediate-scale quantum computing, the limited scalability of a single quantum processing unit (QPU) can be extended through distributed quantum computing (DQC), in which one can implement global operations over two QPUs by entanglement-assisted local operations and classical communication. To facilitate this type of DQC in experiments, we need an entanglement-efficient protocol. To this end, we extend the protocol in [Eisert et. al., PRA, 62:052317(2000)] implementing each nonlocal controlled-unitary gate locally with one maximally entangled pair to a packing protocol, which can pack multiple nonlocal controlled-unitary gates locally using one maximally entangled pair. In particular, two types of packing processes are introduced as the building blocks, namely the distributing processes and embedding processes. Each distributing process distributes corresponding gates locally with one entangled pair. The efficiency of entanglement is then enhanced by embedding processes, which merge two non-sequential distributing processes and hence save the entanglement cost. We show that the structure of distributability and embeddability of a quantum circuit can be fully represented by the corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit to be implemented by two parties. These algorithms can determine the required number of local auxiliary qubits in the DQC. We apply these algorithms for bipartite DQC of unitary coupled-cluster circuits and find a significant reduction of entanglement cost through embeddings. This method can determine a constructive upper bound on the entanglement cost for the DQC of quantum circuits.
This paper proposes a novel distributed computing model leveraging quantum entanglement for enhanced computational efficiency and parallel processing capabilities. The core concept centers around constructing a distributed computing network based on entangled quantum states. By exploiting the quantum superposition property inherent in entangled states, this model enables parallel execution of computations, potentially surpassing the limitations of classical distributed systems. This work details the theoretical framework, key components, and operational principles of the proposed model, outlining a path toward realizing high-performance distributed quantum computing. The model's architecture relies on establishing and maintaining entanglement between distributed quantum processing units (QPUs). The efficiency of the model is analyzed through the lens of quantum information theory, specifically focusing on the reduction of communication overhead and the maximization of computational parallelism. This research contributes to the burgeoning field of quantum computing, offering a new approach to distributed computation. ---
Quantum computers face inherent scaling challenges, a fact that necessitates investigation of distributed quantum computing systems, whereby scaling is achieved through interconnection of smaller quantum processing units. However, connecting large numbers of QPUs will eventually result in connectivity constraints at the network level, where the difficulty of entanglement sharing increases with network path lengths. This increases the complexity of the quantum circuit partitioning problem, since the cost of generating entanglement between end nodes varies with network topologies and existing links. We address this challenge using a simple modification to existing partitioning schemes designed for all-to-all connected networks, that efficiently accounts for both of these factors. We investigate the performance in terms of entanglement requirements and optimisation time of various quantum circuits over different network topologies, achieving lower entanglement costs in the majority of cases than state-of-the-art methods. We provide techniques for scaling to large-scale quantum networks employing both network and problem coarsening. We show that coarsened methods can achieve improved solution quality in most cases with significantly lower run-times than direct partitioning methods.
This paper explores a novel approach to multi-task parallel inference leveraging the unique properties of quantum entanglement. The core claim is that utilizing entanglement enables asymmetric information transfer and collaborative processing between distributed computing nodes, thereby overcoming limitations inherent in traditional distributed computing architectures and dramatically increasing efficiency and the ability to handle complex tasks. The proposed mechanism involves constructing a distributed network comprised of multiple quantum processors, each dedicated to processing a subset of tasks. Through carefully designed entanglement operations, data across these processors are entangled, facilitating simultaneous processing by multiple nodes. Quantum measurement and entanglement decoupling techniques are then employed to consolidate the results, ultimately yielding the final outcome. This approach represents a significant departure from classical distributed computing, which relies on bandwidth-constrained and latency-prone communication channels. By exploiting entanglement, the proposed system bypasses these limitations, unlocking the potential for true parallel quantum computation and significantly enhancing the efficiency and complexity handling capabilities of multi-task parallel inference. ---
This paper explores a novel approach to distributed computing leveraging the principles of quantum entanglement. The core claim is that direct information transfer, facilitated by entanglement, can circumvent the limitations of classical communication bottlenecks, ultimately enhancing computational efficiency and security. This research proposes encoding data into entangled states and utilizing their non-local properties to perform parallel computations. We detail a framework where manipulation of specific components within an entangled state impacts the entire system, enabling distributed computation. The potential for significant performance gains and improved security, compared to traditional distributed computing paradigms reliant on classical communication, is thoroughly discussed. The theoretical framework presented here offers a pathway for future research and development in quantum distributed computing systems.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.