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Problemy Techniki Uzbrojenia

HARDWARE FOR QUANTUM COMPUTATIONS

Tomasz Kuczerski, Włodzimierz MIKKE

Published Dec 30, 2021• 0 citations
Publisher

Abstract

An exemplary hardware platform for simulation of some elementary quantum computations is presented in the paper. Basic cards of Sinara system with software and platform for experiments in domain of quantum physics and computations were described. Some exemplary applications of the hardware platform and instruction for starting up basic quantum algorithms are presented.

DOI
10.5604/01.3001.0015.6776

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An exemplary hardware platform for simulation of some elementary quantum computations is presented in the paper. Basic cards of Sinara system with software and platform for experiments in domain of quantum physics and computations were described. Some exemplary applications of the hardware platform and instruction for starting up basic quantum algorithms are presented.
A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending with means of accessing quantum devices. As part of the roadmap for the development of the high-tech field of quantum computing in the period from 2020 to 2024, a set of software solutions for quantum computing devices was developed. This software package includes a set of quantum algorithms for solving prototypes of applied tasks, monitoring and benchmarking tools for quantum processors, error suppression and correction methods, tools for compiling and optimizing quantum circuits, as well as interfaces for remote cloud access. This review presents the key results achieved, among which it is necessary to mention the execution of quantum algorithms using a cloud-based quantum computing platform.
Quantum hardware platforms are getting increasingly sophisticated in their ability to simulate condensed matter, including but not limited to strongly-correlated, topological, and non-equilibrium phenomena. This review surveys recent progress in quantum-hardware-based simulations of condensed matter, primarily emphasizing gate-based digital quantum computer simulation, with analog experiments discussed as complementary benchmarks. We first review major hardware platforms, including superconducting qubits, trapped-ions, ultracold atoms, Rydberg arrays, photonic systems, and moire quantum materials. We then introduce the basic ingredients of digital quantum simulation. Building on this foundation, we discuss representative applications to condensed-matter physics, spanning ground-state problems, strongly correlated matter, topological phases, non-equilibrium dynamics, open-system physics, and high-energy-physics-inspired simulations. Finally, we summarize key methodological tools used in state-of-the-art quantum-simulation workflows. We emphasize that present noisy quantum simulations serve not only as near-term demonstrations, but also as prototypes for the encodings, diagnostic protocols and error-control strategies required for future fault-tolerant quantum simulation.
During the study, software techniques for optimizing the quantum computer model were considered. The main goal of techniques that increase the efficiency of modeling is to reduce the amount of data that must be processed when modeling quantum gates. The most suitable algorithm for implementing a hardware accelerator was considered and modeled in the experimental part. Algorithms of graph theory or decision diagrams are not very suitable for this task, since their implementation will require the implementation of tensor matrix products, which is more convenient to do on a software simulator. It is also worth noting the increasingly growing interest in modeling quantum computing on special hardware, such as video cards or supercomputers. Therefore, the technique presented in the work is relevant, because it combines software solutions in the field of optimization over the past few years and recently studied methods for modeling quantum computing.
Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study. Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains. In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.