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Adiabatic quantum computing

Elisabeth Lobe, Tobias Stollenwerk, Anke Tröltzsch

Published Jan 1, 2015• 6 citations
OpenAlex

Abstract

In the recent years, the field of adiabatic quantum computing has gained importance due to the advances in the realisation of such machines, especially by the company D-Wave Systems. These machines are suited to solve discrete optimisation problems which are typically very hard to solve on a classical computer. Due to the quantum nature of the device it is assumed that there is a substantial speedup compared to classical HPC facilities. We explain the basic principles of adiabatic quantum computing as well as its advantages and limitations. By using a quantum computer simulation software we present how discrete optimisation problems, like map colouring and the maximal Clique problem, can be solved. In addition, we investigate the applicability of adiabatic quantum computing to DLR specific problems.

Related papers

Discrete optimisation problems on an adiabatic quantum computer

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In the recent years the field of adiabatic quantum computing has gained importance due to the advances in the realisation of such machines, especially by the company D-Wave Systems. In contrast to a quantum computer in the conventional sense, an adiabatic quantum computer can solve a discrete optimisation problem by encoding the objective function into a quantum mechanical system. By carefully evolving the system in time from an initial state into the lowest energy state, the optimisation problem is solved. Due to the quantum nature of the device it is assumed that there is a substantial speedup compared to classical HPC facilities. The D-Wave machines are capable of finding the minimum of a subclass of the Ising model, a quadratic unconstrained binary optimisation problem. In this paper, we present a way of mapping more general problems to the subclass of Ising problems in order to make them solvable on an adiabatic quantum computer. As an example, we choose the maximum clique problem where one needs to find the maximal complete subgraph in an undirected graph. The corresponding decision problem is NP-complete. It has applications in social media or computational chemistry. Moreover, the solution of the maximum clique problem can be used to increase the number of logical variables which can be realised on a D-Wave machine. In addition, we investigate the applicability of adiabatic quantum computing to the scheduling optimisation of satellite missions performed by the German Aerospace Center.

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Quantum Computation has thus far proven a promising new research field, with results that vastly surpass their classical counterparts, but programming a quantum computer is in its infant stage, at best. D-Wave have successfully implemented a computing machine based on adiabatic evolution which natively solves Ising problems by construction. In this paper we present additional solutions to known computational problems by reducing them to mathematical expressions. They will be passed to the quantum computer through Blackbox, a mechanism that maps objective functions to the native Ising form the D-Wave machine recognizes.

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The exploitation of quantum mechanical principles seems to provide a decisive advantage over classical computation technologies. In the recent years there has been big progress in realizing different quantum computer architectures, especially by the Canadian company D-Wave Systems, which developed the first commercially available adiabatic quantum annealer. By adiabatic evolution of quantum mechanical systems it can solve NP-hard discrete optimization problems probabilistically. Due to the restrictions of the hardware architecture the directly mappable problems are limited to quadratic unconstrained binary optimization (QUBO) problems over a so called Chimera interaction graph. After briefly introducing the physical and quantum computational background including the diffculties in determining the run time and success probability of the D-Wave machine, the presentation will focus on the mapping of more general problems onto their specific problem structure. In addition to the reductions to QUBOs the main part here is the minor embedding with the distribution of the weight of the original node onto several hardware nodes.

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Adiabatic Quantum Computing (AQC) is a relatively new subject in the world of quantum computing, let alone Physics. Inspiration for this project has come from recent controversy around D-Wave Systems in British Columbia, Canada, who claim to have built a working AQC which is now commercially available and hope to be distributing a 1024 qubit chip by the end of 2008. Their 16 qubit chip was demonstrated online for the Supercomputing 2007 conference within which a few small problems were solved; although the explanations that journalists and critics received were minimal and very little was divulged in the question and answer session. This 'unconvincing' demonstration has caused physicists and computer scientists to hit back at D-Wave. The aim of this project is to give an introduction to the historic advances in classical and quantum computing and to explore the methods of AQC. Through numerical simulations an algorithm for the Max Independent Set problem is empirically obtained.

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Abstract Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP -hard optimisation problems that are common in industrial areas such as logistics and finance. Newcomers to the field of quantum computing who are interested in using this technology to solve optimisation problems do not have an easily accessible source of information on the current capabilities of quantum computers and algorithms. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques and their practical application, focusing on their near-term potential for noisy intermediate scale quantum devices. The paper starts by drawing parallels between classical and quantum optimisation problems, highlighting their conceptual similarities and differences. Two main paradigms for quantum hardware are then discussed: analogue and gate-based quantum computers. While analog devices such as quantum annealers are effective for some optimisation problems, they have limitations and cannot be used for universal quantum computation. In contrast, gate-based quantum computers offer the potential for universal quantum computation, but they face challenges with hardware limitations and accurate gate implementation. The paper provides a detailed mathematical discussion with references to key works in the field, as well as a more practical discussion with relevant examples. The most popular techniques for quantum optimisation on gate-based quantum computers, the quantum approximate optimisation algorithm and the quantum alternating operator ansatz framework, are discussed in detail. However, it is still unclear whether these techniques will yield quantum advantage, even with advancements in hardware and noise reduction. The paper concludes with a discussion of the challenges facing quantum optimisation techniques and the need for further research and development to identify new, effective methods for achieving quantum advantage.

arXiv:2305.07323•Journal of Physics A Mathematical and Theoretical•Oct 6, 2023•37 citations
In the recent years, the field of adiabatic quantum computing has gained importance due to the advances in the realisation of such machines, especially by the company D-Wave Systems. These machines are suited to solve discrete optimisation problems which are typically very hard to solve on a classical computer. Due to the quantum nature of the device it is assumed that there is a substantial speedup compared to classical HPC facilities. We explain the basic principles of adiabatic quantum computing as well as its advantages and limitations. By using a quantum computer simulation software we present how discrete optimisation problems, like map colouring and the maximal Clique problem, can be solved. In addition, we investigate the applicability of adiabatic quantum computing to DLR specific problems.
In the recent years the field of adiabatic quantum computing has gained importance due to the advances in the realisation of such machines, especially by the company D-Wave Systems. In contrast to a quantum computer in the conventional sense, an adiabatic quantum computer can solve a discrete optimisation problem by encoding the objective function into a quantum mechanical system. By carefully evolving the system in time from an initial state into the lowest energy state, the optimisation problem is solved. Due to the quantum nature of the device it is assumed that there is a substantial speedup compared to classical HPC facilities. The D-Wave machines are capable of finding the minimum of a subclass of the Ising model, a quadratic unconstrained binary optimisation problem. In this paper, we present a way of mapping more general problems to the subclass of Ising problems in order to make them solvable on an adiabatic quantum computer. As an example, we choose the maximum clique problem where one needs to find the maximal complete subgraph in an undirected graph. The corresponding decision problem is NP-complete. It has applications in social media or computational chemistry. Moreover, the solution of the maximum clique problem can be used to increase the number of logical variables which can be realised on a D-Wave machine. In addition, we investigate the applicability of adiabatic quantum computing to the scheduling optimisation of satellite missions performed by the German Aerospace Center.
Quantum physics has the potential to revolutionize computer science by building what is called a quantum computer. Quantum computers can perform tasks that are impossible with Turing-machine based classical computers (e.g., generating random sequences of bits) and can outperform classical computers (to different degrees) in a variety of classical computation problems. Several different models of quantum computing exist. Based on the Adiabatic Theorem in physics, Adiabatic Quantum Computing is a novel computing framework that is drastically different from the more traditional quantum logic gate model. Traditionally, quantum computing has only been tested on devices with a very small number of qubits, severely limiting their uses in practice. In recent years however, advances in engineering and physics have allowed the construction for (relatively) large machines called quantum annealers that do what is known as a specific type of adiabatic quantum computation called quantum annealing. Solving classical computation problems with the quantum annealing framework is not an easy task and there are many obstacles to overcome if we want to achieve any kind of speedups over classical algorithms. In this thesis, we present quantum annealing solutions to several classical problems including the Hamiltonian Cycle Problem, Mixed Dominating Set Problem, Graph Isomorphism Problem, Densest k-Subgraph Problem and the Maximum Weighted Independent Set Problem. Experimental results of these quantum solutions on DWave quantum annealers are also reported. We also look at some of the bottlenecks in the quantum annealing framework and propose several novel approach to mitigate their effects on the efficiency of the quantum solutions.
Quantum Computation has thus far proven a promising new research field, with results that vastly surpass their classical counterparts, but programming a quantum computer is in its infant stage, at best. D-Wave have successfully implemented a computing machine based on adiabatic evolution which natively solves Ising problems by construction. In this paper we present additional solutions to known computational problems by reducing them to mathematical expressions. They will be passed to the quantum computer through Blackbox, a mechanism that maps objective functions to the native Ising form the D-Wave machine recognizes.
The exploitation of quantum mechanical principles seems to provide a decisive advantage over classical computation technologies. In the recent years there has been big progress in realizing different quantum computer architectures, especially by the Canadian company D-Wave Systems, which developed the first commercially available adiabatic quantum annealer. By adiabatic evolution of quantum mechanical systems it can solve NP-hard discrete optimization problems probabilistically. Due to the restrictions of the hardware architecture the directly mappable problems are limited to quadratic unconstrained binary optimization (QUBO) problems over a so called Chimera interaction graph. After briefly introducing the physical and quantum computational background including the diffculties in determining the run time and success probability of the D-Wave machine, the presentation will focus on the mapping of more general problems onto their specific problem structure. In addition to the reductions to QUBOs the main part here is the minor embedding with the distribution of the weight of the original node onto several hardware nodes.
Adiabatic Quantum Computing (AQC) is a relatively new subject in the world of quantum computing, let alone Physics. Inspiration for this project has come from recent controversy around D-Wave Systems in British Columbia, Canada, who claim to have built a working AQC which is now commercially available and hope to be distributing a 1024 qubit chip by the end of 2008. Their 16 qubit chip was demonstrated online for the Supercomputing 2007 conference within which a few small problems were solved; although the explanations that journalists and critics received were minimal and very little was divulged in the question and answer session. This 'unconvincing' demonstration has caused physicists and computer scientists to hit back at D-Wave. The aim of this project is to give an introduction to the historic advances in classical and quantum computing and to explore the methods of AQC. Through numerical simulations an algorithm for the Max Independent Set problem is empirically obtained.
Abstract Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP -hard optimisation problems that are common in industrial areas such as logistics and finance. Newcomers to the field of quantum computing who are interested in using this technology to solve optimisation problems do not have an easily accessible source of information on the current capabilities of quantum computers and algorithms. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques and their practical application, focusing on their near-term potential for noisy intermediate scale quantum devices. The paper starts by drawing parallels between classical and quantum optimisation problems, highlighting their conceptual similarities and differences. Two main paradigms for quantum hardware are then discussed: analogue and gate-based quantum computers. While analog devices such as quantum annealers are effective for some optimisation problems, they have limitations and cannot be used for universal quantum computation. In contrast, gate-based quantum computers offer the potential for universal quantum computation, but they face challenges with hardware limitations and accurate gate implementation. The paper provides a detailed mathematical discussion with references to key works in the field, as well as a more practical discussion with relevant examples. The most popular techniques for quantum optimisation on gate-based quantum computers, the quantum approximate optimisation algorithm and the quantum alternating operator ansatz framework, are discussed in detail. However, it is still unclear whether these techniques will yield quantum advantage, even with advancements in hardware and noise reduction. The paper concludes with a discussion of the challenges facing quantum optimisation techniques and the need for further research and development to identify new, effective methods for achieving quantum advantage.