QUBO formulation for the contact map overlap problem

M., de Oliveira, Nicolas; Silva, Ricardo Martins de Abreu; Oliveira, W.

doi:10.1142/s0219749918400075

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…This enhanced connectivity allows for better utilization of the existing qubits, thus fewer vertices are capable of broader calculations. [61]. Simulation results showed that the proposed approach outperformed classical techniques.…”

Section: Related Workmentioning

confidence: 91%

A QUBO Model for the Traveling Salesman Problem with Time Windows for Execution on the D-Wave

Papalitsas¹,

Andronikos²,

Giannakis³

et al. 2019

Preprint

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This work focuses on expressing the TSP with Time Windows (TSPTW for short) as a quadratic unconstrained binary optimization (QUBO) problem. The time windows impose time constraints that a feasible solution must satisfy. These take the form of inequality constraints, which are known to be particularly difficult to articulate within the QUBO framework. This is, we believe, the first time this major obstacle is overcome and the TSPTW is cast in the QUBO formulation. We have every reason to anticipate that this development will lead to the actual execution of small scale TSPTW instances on the D-Wave platform.

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Section: Related Workmentioning

confidence: 91%

A QUBO Model for the Traveling Salesman Problem with Time Windows for Execution on the D-Wave

Papalitsas¹,

Andronikos²,

Giannakis³

et al. 2019

Preprint

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“…Although COPs are usually modeled as constrained binary linear programs, solving their QUBO reformulations via a hardware specially designed for QUBOs with the aforementioned aim can indeed outperform solving their natural formulations via an IP solver. Moreover, QUBO problems naturally arise in many applications such as routing (Irie et al 2019;Papalitsas et al 2019), scheduling (Tran et al 2016;Venturelli, Marchand, and Rojo 2015), finance (Elsokkary et al 2017;Orus, Mugel, and Lizaso 2019), and biology (Li et al 2018;Oliveira, Silva, and Oliveira 2018). These problems are more challenging, thus amenable to these new technologies.…”

Section: Solution Methods For Copsmentioning

confidence: 99%

Digital Annealer for quadratic unconstrained binary optimization: a comparative performance analysis

Şeker¹,

Tanoumand²,

Bodur³

2020

Preprint

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Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization problems formulated as quadratic unconstrained binary optimization (QUBO) models. In this paper, we present the results of an extensive computational study to evaluate the performance of DA in a systematic way in comparison to multiple state-of-the-art solvers for different problem classes. We examine pure QUBO models, as well as QUBO reformulations of three constrained problems, namely quadratic assignment, quadratic cycle partition, and selective graph coloring, with the last two being new applications for DA. For the selective graph coloring problem, we also present a size reduction heuristic that significantly increases the number of eligible instances for DA. Our experimental results show that despite being in its development stage, DA can provide high-quality solutions quickly and in that regard rivals the state of the art, particularly for large instances. Moreover, as opposed to established solvers, within its limit on the number of decision variables, DA's solution times are not affected by the increase in instance size. These findings illustrate that DA has the potential to become a successful technology in tackling combinatorial optimization problems.

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“…Such applications require solving the molecular conformation problem to global optimality [46]. Molecular conformation problem helps predict the native structure of sequence of molecules, and it can be considered as a simplified form of predicting native structures of residues in case of protein folding [39,[47][48][49]. Heuristic solution techniques for the molecular conformation problem are computationally expensive, depend heavily on carefully chosen parameters, and do not guarantee a global optimum [50].…”

Section: Qc For Molecular Conformations In Molecular/product Designmentioning

confidence: 99%

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Ajagekar

Humble

You

2020

Computers & Chemical Engineering

138

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Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency.The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.Quantum computing (QC) is the next frontier in computation and has attracted a lot of attention from the scientific community in recent years. QC provides a novel approach to help solve some of the most complex optimization problems while offering an essential speed advantage over classical methods [10]. This is evident from QC techniques like Shor's algorithm for integer factorization [11], Grover's search algorithm for unstructured databases [12], quantum algorithm for linear system of equations [13], and many more [14]. Quantum adiabatic algorithms too are efficient optimization strategies that quickly search over the solution space [15]. Quantum computers perform computation by inducing quantum speedups whose scaling far exceeds the capability of the most powerful classical computers. QC's major applications can be perceived in areas of optimization, machine learning, cryptography, and quantum chemistry [16]. Despite the contrasting views on QC's viability and performance, there is no doubt that QC holds great promise to open up a new era of computing.QC-based solution approaches are in their earliest stages of development compared to their much more matured classical counterparts. Current quantum machines have very limited functionality in the context of optimization, such that QC hardware and algorithms are inadequate for large-scale optimization problems. Although it has been shown that some optimization problems relevant to energy systems can be solved using quantum computers, their performance deteriorates with increasing size and complexity [17]. A number of technological limitations face commercially available quantum computers, such as relatively small number of qubits with limited connectivity, and lack of quantum memory. Therefore, harnessing the complementary strengths of classical and...

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QUBO formulation for the contact map overlap problem

Cited by 9 publications

References 15 publications

A QUBO Model for the Traveling Salesman Problem with Time Windows for Execution on the D-Wave

A QUBO Model for the Traveling Salesman Problem with Time Windows for Execution on the D-Wave

Digital Annealer for quadratic unconstrained binary optimization: a comparative performance analysis

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

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