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Scientific Reports

Scalable distributed gate-model quantum computers

László Gyöngyösi, Sándor Imre

Published Feb 26, 2021• 82 citations
Publisher

Abstract

A scalable model for a distributed quantum computation is a challenging problem due to the complexity of the problem space provided by the diversity of possible quantum systems, from small-scale quantum devices to large-scale quantum computers. Here, we define a model of scalable distributed gate-model quantum computation in near-term quantum systems of the NISQ (noisy intermediate scale quantum) technology era. We prove that the proposed architecture can maximize an objective function of a computational problem in a distributed manner. We study the impacts of decoherence on distributed objective function evaluation.

DOI
10.1038/s41598-020-76728-5

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Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.
Quantum computing has transitioned from a theoretical curiosity to an emerging technological paradigm with demonstrable computational advantages in specific problem domains. This paper presents a comprehensive review of current advances in quantum computing hardware, software, and near-term applications within the Noisy Intermediate-Scale Quantum (NISQ) era. We examine the architectural evolution of leading quantum platforms, including superconducting, trapped-ion, and photonic systems, analyzing their qubit counts, gate fidelities, and coherence times. The paper evaluates key quantum algorithms Shor's factoring algorithm [1], Grover's search algorithm [2], the Variational Quantum Eigensolver (VQE) [3], and the Quantum Approximate Optimization Algorithm (QAOA) [4] in the context of their practical applicability to real-world problems. We further discuss the milestone of quantum computational advantage demonstrated by Google's Sycamore processor [5] and IBM's ambitious hardware roadmap toward fault-tolerant quantum computing [6]. Our analysis reveals that while universal fault-tolerant quantum computing remains a long-term objective, NISQ-era devices are already yielding valuable results in quantum chemistry simulation, combinatorial optimization, and quantum machine learning. We conclude by identifying the critical challenges—error correction, scalability, and algorithm design that must be addressed to realize the full potential of quantum computing.
Noisy quantum computers can in principle perform reliable quantum computations, but truly scalable systems require noise levels lower than are presently achieved. Still, moderate-complexity computations can be performed. This review discusses what is possible in this ``noisy intermediate scale'' quantum (NISQ) era. Topic areas include the simulation of many-body physics and chemistry, combinatorial optimization, and machine learning. It is evident that the NISQ era has produced new paradigms for programming that will be built upon as quantum computers are further perfected.
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Abstract Superconducting gate-model quantum computer architectures provide an implementable model for practical quantum computations in the NISQ (noisy intermediate scale quantum) technology era. Due to hardware restrictions and decoherence, generating the physical layout of the quantum circuits of a gate-model quantum computer is a challenge. Here, we define a method for layout generation with a decoherence dynamics estimation in superconducting gate-model quantum computers. We propose an algorithm for the optimal placement of the quantum computational blocks of gate-model quantum circuits. We study the effects of capacitance interference on the distribution of the Gaussian noise in the Josephson energy.