In this work, we evaluate an evolutionary algorithm (EA) to optimize a given circuit in such a way that it reduces the required communication when executed in the Distributed Quantum Computing (DQC) paradigm. We evaluate our approach for a state preparation task using Grover circuits and show that it is able to reduce the required global gates by more than 89% while still achieving high fidelity as well as the ability to extract the correct solution to the given problem. We also apply the approach to reduce circuit depth and number of CX gates. Additionally, we run experiments in which a circuit is optimized for a given network topology after each qubit has been assigned to specific nodes in the network. In these experiments, the algorithm is able to reduce the communication cost (i.e., number of hops between QPUs) by up to 19%.
In this work, we evaluate an evolutionary algorithm (EA) to optimize a given circuit in such a way that it reduces the required communication when executed in the Distributed Quantum Computing (DQC) paradigm. We evaluate our approach for a state preparation task using Grover circuits and show that it is able to reduce the required global gates by more than 89% while still achieving high fidelity as well as the ability to extract the correct solution to the given problem. We also apply the approach to reduce circuit depth and number of CX gates. Additionally, we run experiments in which a circuit is optimized for a given network topology after each qubit has been assigned to specific nodes in the network. In these experiments, the algorithm is able to reduce the communication cost (i.e., number of hops between QPUs) by up to 19%.
The emerging paradigm of distributed quantum computing promises a potential solution to scaling quantum computing to currently unfeasible dimensions. While this approach itself is still in its infancy, and many obstacles must still be overcome before its physical implementation, challenges from the software and algorithmic side must also be identified and addressed. For instance, this paradigm shift requires a new form of compiler that considers the network constraints in general as well as phenomena arising due to the nature of quantum communication. In distributed quantum computing, large circuits are divided into smaller subcircuits such that they can be executed individually and simultaneously on multiple QPUs that are connected through quantum channels. As quantum communication, for example, in the form of teleportation, is expensive, it must be used sparingly. We address the problem of assigning qubits to QPUs to minimize communication costs in two different ways. First by applying time-aware algorithms that take into account the changing connectivity of a given circuit as well as the underlying network topology. We define the optimization problem, use simulated annealing and an evolutionary algorithm and compare the results to graph partitioning and sequential qubit assignment baselines. In another approach, we propose an evolutionary-based quantum circuit optimization algorithm that adjusts the circuit itself rather than the schedule to reduce the overall communication cost. We evaluate the techniques against random circuits and different network topologies. Both evolutionary algorithms outperform the baseline in terms of communication cost reduction. We give an outlook on how the approaches can be integrated into a compilation framework for distributed quantum computing.
By connecting multiple quantum computers (QCs) through classical and quantum channels, a quantum communication network can be formed. This gives rise to new applications such as blind quantum computing, distributed quantum computing, and quantum key distribution. In distributed quantum computing, QCs collectively perform a quantum computation. As each device only executes a sub-circuit with fewer qubits than required by the complete circuit, a number of small QCs can be used in combination to execute a large quantum circuit that a single QC could not solve on its own. However, communication between QCs may still occur. Depending on the connectivity of the circuit, qubits must be teleported to different QCs in the network, adding overhead to the actual computation; thus, it is crucial to minimize the number of teleportations. In this paper, we propose an evolutionary algorithm for this problem. More specifically, the algorithm assigns qubits to QCs in the network for each time step of the circuit such that the overall teleportation cost is minimized. Moreover, network-specific constraints such as the capacity of each QC in the network can be taken into account. We run experiments on random as well as benchmarking circuits and give an outline on how this method can be adjusted to be incorporated into more realistic network settings as well as in compilers for distributed quantum computing. Our results show that an evolutionary algorithm is well suited for this problem when compared to the graph partitioning approach as it delivers better results while simultaneously allows the easy integration and consideration of various problem-specific constraints.
In a quantum internet, quantum processing units (QPUs) with varying architectures and capabilities may be connected through quantum communication channels, enabling new applications such as distributed quantum computing (DQC), a paradigm in which multiple QPUs execute a single circuit. However, remote operations between QPUs are expensive as they require the creation and distribution of entanglement throughout the network. It is therefore crucial to assign qubits to QPUs and partition circuits in such a way that the overall communication between QPUs is minimized. In this paper, we apply and evaluate simulated annealing and an evolutionary algorithm for this problem. We consider quantum networks with 25 nodes arranged in different topologies and QPUs with varying qubit capacities. The circuits evaluated contain 50 and 100 qubits. We show that the different metaheuristics all significantly outperform the baselines by drastically reducing the communication cost by over 40%.
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.
Distributed Quantum Computing (DQC) has the potential to solve industrial large-scale problems by connecting multiple small quantum processors together to form a larger computing system. Concerning the emerging distributed paradigm, a pivotal challenge lies in crafting specialized network topologies to establish efficient connections among quantum processors while minimizing communication costs. In this paper, we propose a novel DQC topology generation algorithm (DQC-TG) to create optimal and near-optimal network topologies for homogeneous and heterogeneous quantum computers, respectively. Furthermore, for specific quantum circuits requiring diverse communication demands between each pair of qubits, we extend the original algorithm into DQC- TG- Plus to design network topologies tailored for these circuits to further enhance the performance. We perform extensive simulations to evaluate the superiority of the generated network topology designs by our algorithms to baselines.
To overcome the physical limitations of scaling monolithic quantum computers, distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network. However, this approach introduces a critical challenge, namely the high cost of quantum communication between remote QPUs incurred by quantum state teleportation and quantum gate teleportation. To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs. Static graph partitioning methods are fundamentally ill-equipped for this task as they ignore execution dynamics and underlying network topology, while metaheuristics require substantial computational runtime. In this work, we propose a heuristic based on beam search to solve the circuit partitioning problem. Our time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead. The time and space complexities of the proposed algorithm scale quadratically with the number of qubits and linearly with circuit depth, offering a significant computational speedup over common metaheuristics. We demonstrate that our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies, providing an efficient compilation tool for near-term distributed quantum hardware.