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Comparison of container-based platforms for quantum computing simulation

Ginés Carrascal, Guillermo Botella, Alberto Antonio Del Barrio

Published Jan 1, 2020• 0 citations
OpenAlex

Abstract

Quantum Computing allows for substantial speed-ups e.g. for integer factorization or database search, compared to conventional computation. However, since real quantum computers are an emerging technology, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This paper proposes a containerized environment to simulate quantum circuits, making this environment quick and easy to share within research teams. Also test the environment implementing an arithmetic circuit (Vedral’s adder) and compares the performance of two of the current more used environments: IBM QISKit and Google Cirq.

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Quantum Computing allows for substantial speed-ups e.g. for integer factorization or database search, compared to conventional computation. However, since real quantum computers are an emerging technology, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This paper proposes a containerized environment to simulate quantum circuits, making this environment quick and easy to share within research teams. Also test the environment implementing an arithmetic circuit (Vedral’s adder) and compares the performance of two of the current more used environments: IBM QISKit and Google Cirq.
Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups, e.g., for integer factorization or database search. However, since physical realizations of quantum computers are in their infancy, a significant amount of research in this domain still relies on simulations of quantum computations on conventional machines. This causes a significant complexity which current state-of-the-art simulators try to tackle with a rather straight forward array-based representation and by applying massive hardware power. There also exist solutions based on decision diagrams (i.e., graph-based approaches) that try to tackle the exponential complexity by exploiting redundancies in quantum states and operations. However, these existing approaches do not fully exploit redundancies that are actually present. In this paper, we revisit the basics of quantum computation, investigate how corresponding quantum states and quantum operations can be represented even more compactly, and, eventually, simulated in a more efficient fashion. This leads to a new graph-based simulation approach which outperforms state-of-the-art simulators (array-based as well as graph-based). Experimental evaluations show that the proposed solution is capable of simulating quantum computations for more qubits than before, and in significantly less run-time (several magnitudes faster compared to previously proposed simulators). An implementation of the proposed simulator is publicly available online at http://iic.jku.at/eda/research/quantum_simulation.
Recent advances in the development of the first generation of quantum computing devices have provided researchers with computational platforms to explore new ideas and reformulate conventional computational codes suitable for a quantum computer. Developers can now implement these reformulations on both quantum simulators and hardware platforms through a cloud computing software environment. For example, the IBM Q Experience provides the direct access to their quantum simulators and quantum computing hardware platforms. However these current access options may not be an optimal environment for developers needing to download and modify the source codes and libraries. This paper focuses on the construction of a Docker container environment with Qiskit source codes and libraries running on a local cloud computing system that can directly access the IBM Q Experience. This prototype container based system allows single user and small project groups to do rapid prototype development, testing and implementation of extreme capability algorithms with more agility and flexibility than can be provided through the IBM Q Experience website. This prototype environment also provides an excellent teaching environment for labs and project assignments within graduate courses in cloud computing and quantum computing. The paper also discusses computer security challenges for expanding this prototype container system to larger groups of quantum computing researchers.
The term Quantum Computing(QC) has emerged with lots of promises specifically with respect to the hard and complex computational problems in recent years.Quantum computing already shown its supreme capability and achieved several milestones by generating solutions in various domains such as material science, bio-science, finance etc.A systematic survey is conducted over the a few but well-practiced quantum platforms for quantum application and research in this paper.The characteristics, the technique of the simulation at various level of the quantum platform like IBM Qiskit, Google Cirq etc. are discussed with respect to current scenario of advantages and disadvantages.This paper also tried to explain the applicability as well as the present day challenges of different quantum tools.
Quantum computing has emerged as a transformative technological advancement with the potential to challenge the foundations of classical cryptography. Traditional cryptographic algorithms, such as RSA, rely heavily on the computational hardness of problems like integer factorization—a problem quantum algorithm like Shor's can theoretically solve efficiently. This research investigates the practical capabilities and performance limitations of three prominent quantum simulation frameworks: Qiskit, Cirq, and QuTiP. The study aims to analyze and compare their efficiency in simulating the decryption process for encrypted data of varying bit lengths—specifically 8 -bit, 16 -bit, 32 -bit, 64 -bit, 128 -bit, and 512 -bit keys. By simulating classical decryption schemes through quantum circuits, we assess the computational resources, execution time, and overall performance of each platform. This benchmarking helps to highlight how effectively these simulators can model cryptographic tasks and where they currently fall short in terms of scalability and execution time. The results offer valuable insights into the practicality of deploying quantum computing for real-world cryptographic operations and outline the current limitations imposed by simulation-based approaches, especially as key sizes grow in complexity. Ultimately, this research contributes to a deeper understanding of how existing quantum tools perform under cryptographic workloads and what improvements are necessary to make quantum-assisted cryptanalysis viable at scale.
The simulation of quantum circuits using the tensor network method is very computationally demanding and requires significant High Performance Computing (HPC) resources to find an efficient contraction order and to perform the contraction of the large tensor networks. In addition, the researchers want a workflow that is easy to customize, reproduce and migrate to different HPC systems. In this paper, we discuss the issues associated with the deployment of the QuantEX quantum computing simulation software within containers on different HPC systems. Also, we compare the performance of the containerized software with the software running on bare metal.
The simulation of quantum circuits using the tensor network method is very computationally demanding and requires significant High Performance Computing (HPC) resources to find an efficient contraction order and to perform the contraction of the large tensor networks. In addition, the researchers want a workflow that is easy to customize, reproduce and migrate to different HPC systems. In this paper, we discuss the issues associated with the deployment of the QuantEX quantum computing simulation software within containers on different HPC systems. Also, we compare the performance of the containerized software with the software running on “bare metal”.