Goal seeking Quadratic Unconstrained Binary Optimization
Results in Control and Optimization, 2022 The Quadratic Unconstrained Binary Optimization (QUBO) modeling and solution framework is a requirement for quantum and digital annealers. However optimality for QUBO problems of any practical size is extremely difficult to achieve.
Amit Verma, Mark Lewis
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Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization [PDF]
Scientific Reports, 2022 Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their ...
Hiroki Oshiyama, Masayuki Ohzeki
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Analyzing Quadratic Unconstrained Binary Optimization Problems via Multicommodity Flows [PDF]
Discrete Applied Mathematics, 2009 Quadratic Unconstrained Binary Optimization (QUBO) problems concern the minimization of quadratic polynomials in n {0, 1}-valued variables. These problems are NP-complete, but prior work has identified a sequence of polynomial-time computable lower bounds on the minimum value, denoted by C(2), C(3), C(4),….
Robert Kleinberg
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Sampling electronic structure quadratic unconstrained binary optimization problems (QUBOs) with Ocean and Mukai solvers. [PDF]
PLoS ONE, 2022 The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count.
Alexander Teplukhin +4 more
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Parallelizable search-space decomposition for large-scale combinatorial optimization problems using quadratic unconstrained binary optimization [PDF]
Scientific ReportsCombinatorial optimization problems are crucial in industry. However, many COPs are NP-hard, causing the search space to grow exponentially with problem size and rendering large-scale instances computationally intractable.
Eiji Kawase +3 more
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Quadratic unconstrained binary optimization and constraint programming approaches for lattice-based cyclic peptide docking [PDF]
Scientific ReportsThe peptide-protein docking problem is an important problem in structural biology that facilitates rational and efficient drug design. In this work, we explore modeling and solving this problem with the quantum-amenable quadratic unconstrained binary ...
J. Kyle Brubaker +6 more
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A smaller and more efficient one-hot encoding for QUBO [PDF]
Scientific ReportsOne-hot constraints are widely used for quadratic unconstrained binary optimization (QUBO) in quantum and simulated annealing to solve optimization problems. They correspond to exactly-one constraints for Boolean satisfiability problems (SAT), which have
Shinji Takasugi +3 more
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Q-LEAP: Millisecond Hyperdimensional Optimization for Full-Spectrum Optical Metamaterials. [PDF]
Adv Sci (Weinh)Q‐LEAP integrates physics‐informed residual machine learning with factorization‐machine‐encoded quantum annealing to design full‐spectrum optical metamaterials. It explores a 2108 design space and, in a single 2.56 ms annealing step, reaches 85.83% of the theoretical FoM limit, enabling selective 5‐8 µm emission with 3–5 and 8–14 µm suppression and ∼40×
Guo Z, Wu Y, Guo Y, Ju S.
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RisksIn this paper, we consider the inclusion of the solvency capital requirement (SCR) into portfolio optimization by the use of a quadratic proxy model. The Solvency II directive requires insurance companies to calculate their SCR based on the complete loss
Ivica Turkalj +8 more
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Parity Quantum Optimization: Compiler [PDF]
Quantum, 2023 We introduce parity quantum optimization with the aim of solving optimization problems consisting of arbitrary $k$-body interactions and side conditions using planar quantum chip architectures.
Kilian Ender +4 more
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