Quantum bridge analytics I: a tutorial on formulating and using QUBO models

Glover, Fred; Kochenberger, Gary A.; Hennig, Rick; Du, Yu

doi:10.1007/s10479-022-04634-2

Cited by 96 publications

(45 citation statements)

References 67 publications

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“…The PortOpt problem needs to be first transformed into a Quadratic Unconstrained Binary Optimization (QUBO) [46], an NP-hard problem. The PortOpt with the QUBO representation is then converted to quantum operators so that it can be executed by NISQ computers.…”

Section: Qaoa For Portoptmentioning

confidence: 99%

Optimizing QAOA on Bipotent Architectures

Ji¹,

Koenig²,

Polian³

2023

Preprint

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Vigorous optimization of quantum gates has led to bipotent quantum architectures, where the optimized gates are available for some qubits but not for others. However, such gate-level improvements limit the application of user-side pulse-level optimizations, which have proven effective for quantum circuits with a high level of regularity, such as the ansatz circuit of the Quantum Approximate Optimization Algorithm (QAOA). In this paper, we investigate the trade-off between hardware-level and algorithm-level improvements on bipotent quantum architectures. Our results for various QAOA instances on two quantum computers offered by IBM indicate that the benefits of pulse-level optimizations currently outweigh the improvements due to vigorously optimized monolithic gates. Furthermore, our data indicate that the fidelity of circuit primitives is not always the best indicator for the overall algorithm performance; also their gate type and schedule duration should be taken into account. This effect is particularly pronounced for QAOA on dense portfolio optimization problems, since their transpilation requires many SWAP gates, for which efficient pulse-level optimization exists. Our findings provide practical guidance on optimal qubit selection on bipotent quantum architectures and suggest the need for improvements of those architectures, ultimately making pulse-level optimization available for all gate types.

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Section: Qaoa For Portoptmentioning

confidence: 99%

Optimizing QAOA on Bipotent Architectures

Ji¹,

Koenig²,

Polian³

2023

Preprint

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“…Embedding algorithms [21,22] on the Chimera and Pegasus layouts of current D-Wave hardware are proven to get the objective function value close to the global minimum value. Even if they cannot yet provide a full representation of the original complete graph, the solution they evaluate is locally optimal.…”

Section: Solution Of the Optimization Problem On D-wave Quantum Annealermentioning

confidence: 99%

Gravity Data Inversion by Adiabatic Quantum Computing

Siddi Moreau,

Pisani,

Mameli

et al. 2023

Adv Quantum Tech

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A quantum‐enhanced implementation of the binary inversion method for gravity data acquisition is discussed. The subsurface structure of a single density anomaly with an assigned density contrast is calculated by using a D‐Wave adiabatic quantum computer. In particular, an iterative heuristic based on quantum annealing that recovers a sharp shape of the subsurface anomaly is developed. Such a task is accomplished by collecting partial images obtained by quantum annealing processes for optimal Lagrange penalty coefficients. The results are compared with those obtained according to the same cost function minimized via genetic algorithms by conventional hardware on a realistic 2D dataset. The outcomes of this work are promising as the reconstructed model is obtained in tenths of iterations instead of the hundreds required in conventional methods. Moreover, for the part of the computation that resides in the quantum processing unit, the computational cost of the single quantum annealing descent is constant with respect to the number of degrees of freedom of the subsurface grid. The implemented method is likely to reveal its full potential on forthcoming quantum annealing devices, outperforming existing techniques.

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“…One common model that is suitable for solving combinatorial optimization problems in quantum computers is the quadratic unconstrained binary optimization, or QUBO for short. QUBO can embrace many models in combinatorial optimization; QUBO models were shown to be equivalent to the Ising model, which plays a crucial role in physics and particle interactions [ 19 ].…”

Section: Quantum Routing Optimizationmentioning

confidence: 99%

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

Urgelles

Picazo-Martinez

García‐Roger

et al. 2022

Sensors

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Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.

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Quantum bridge analytics I: a tutorial on formulating and using QUBO models

Cited by 96 publications

References 67 publications

Optimizing QAOA on Bipotent Architectures

Optimizing QAOA on Bipotent Architectures

Gravity Data Inversion by Adiabatic Quantum Computing

Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

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