Optimizing Teleportation Cost in Distributed Quantum Circuits

Zomorodi‐Moghadam, Mariam; Houshmand, Mahboobeh; Houshmand, Monireh

doi:10.1007/s10773-017-3618-x

Cited by 35 publications

(72 citation statements)

References 39 publications

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“…In our previous study [14], with two given quantum subsystems, an algorithm with an exponential complexity was proposed to optimize the number of qubit teleportations required for the communication between these two subsystems. Reducing the problem of quantum circuit partitioning to the graph partitioning model has been proposed in [39].…”

Section: Related Workmentioning

confidence: 99%

“…The authors have considered the case where different quantum gates have a common control or common target and then for each case they have built a hyperedge connecting common qubits and non-common qubits which meet at one end. But the authors have not considered any optimization like moving gates back and forth for making them close to each other as the approach presented in [14]. They have not also taken into account the entire search space of different partitioning and different partition for executing global gates and hence cannot produce optimal solutions.…”

Section: Related Workmentioning

confidence: 99%

“…In [14], an exact solution has been given and an algorithm has been proposed to minimize the number of teleportations in a two-partite distributed quantum circuit. The approach calculates the minimal number of teleportations for each configuration of global gates, where each configuration has a unique placement for global gates being in either Partition A or B.…”

Section: Problem Definitionmentioning

confidence: 99%

“…In order to overcome this problem, in this paper, a heuristic approach based on genetic algorithms is proposed which attempts to find a configuration of global gates with an optimized teleportation cost. Each chromosome, i.e., the individuals in the GA population of potential solutions, shows a configuration whose fitness is computed by MIN-TELEPORTATION function in [14] and then by the evolution in the generations, the configuration with the optimized cost is found. The details of the proposed approach are given in the following sections.…”

Section: Problem Definitionmentioning

confidence: 99%

“…Fitness function and selection strategy The fitness function for this genetic algorithm is computed as the minimal number of required teleportations for a given configuration of global gates, represented by chromosomes, of a distributed quantum system. This value is computed by the MIN-TELEPORTATION function in [14]. In the MIN-TELEPORTATION function, when a qubit of a global gate is teleported to the other partition, the whole circuit is tracked and as much as possible number of gates that can be executed without the need of teleporting this qubit back are executed.…”

Section: The Outline Of the Algorithmmentioning

confidence: 99%

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An Evolutionary Approach to Optimizing Teleportation Cost in Distributed Quantum Computation

et al. 2020

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Distributed quantum computing has been well-known for many years as a system composed of a number of small-capacity quantum circuits. Limitations in the capacity of monolithic quantum computing systems can be overcome by using distributed quantum systems which communicate with each other through known communication links. In our previous study, an algorithm with an exponential complexity was proposed to optimize the number of qubit teleportations required for the communications between two partitions of a distributed quantum circuit. In this work, a genetic algorithm is used to solve the optimization problem in a more efficient way. The results are compared with the previous study and we show that our approach works almost the same with a remarkable speed-up. Moreover, the comparison of the proposed approach based on GA with a random search over the search space verifies the effectiveness of GA.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

Section: The Outline Of the Algorithmmentioning

confidence: 99%

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An Evolutionary Approach to Optimizing Teleportation Cost in Distributed Quantum Computation

et al. 2020

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Imperfect Distributed Quantum Phase Estimation

Neumann

Houte

Attema

2020

Lecture Notes in Computer Science

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In the near-term, the number of qubits in quantum computers will be limited to a few hundreds. Therefore, problems are often too large and complex to be run on quantum devices. By distributing quantum algorithms over different devices, larger problem instances can be run. This distributing however, often requires operations between two qubits of different devices. Using shared entangled states and classical communication, these operations between different devices can still be performed. In the ideal case of perfect fidelity, distributed quantum computing is a solution to achieving scalable quantum computers with a larger number of qubits. In this work we consider the effects on the output fidelity of a quantum algorithm when using noisy shared entangled states. We consider the quantum phase estimation algorithm and present two distribution schemes for the algorithm. We give the resource requirements for both and show that using less noisy shared entangled states results in a higher overall fidelity.

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