Compressed Quadratization of Higher Order Binary Optimization Problems
- URL: http://arxiv.org/abs/2001.00658v1
- Date: Thu, 2 Jan 2020 22:48:50 GMT
- Title: Compressed Quadratization of Higher Order Binary Optimization Problems
- Authors: Avradip Mandal, Arnab Roy, Sarvagya Upadhyay and Hayato
Ushijima-Mwesigwa
- Abstract summary: Recent hardware advances in quantum and quantum-inspired annealers promise substantial speedup for solving NP-hard optimization problems.
In this work, we prove that in Ising space the degree reduction of one term requires the introduction of two variables.
For sparse higher-order Ising problems, this results in a more compact representation of the resultant QUBO problem.
- Score: 5.833700634137687
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent hardware advances in quantum and quantum-inspired annealers promise
substantial speedup for solving NP-hard combinatorial optimization problems
compared to general-purpose computers. These special-purpose hardware are built
for solving hard instances of Quadratic Unconstrained Binary Optimization
(QUBO) problems. In terms of number of variables and precision of these
hardware are usually resource-constrained and they work either in Ising space
{-1,1} or in Boolean space {0,1}. Many naturally occurring problem instances
are higher-order in nature. The known method to reduce the degree of a
higher-order optimization problem uses Rosenberg's polynomial. The method works
in Boolean space by reducing the degree of one term by introducing one extra
variable. In this work, we prove that in Ising space the degree reduction of
one term requires the introduction of two variables. Our proposed method of
degree reduction works directly in Ising space, as opposed to converting an
Ising polynomial to Boolean space and applying previously known Rosenberg's
polynomial. For sparse higher-order Ising problems, this results in a more
compact representation of the resultant QUBO problem, which is crucial for
utilizing resource-constrained QUBO solvers.
Related papers
- An encoding of argumentation problems using quadratic unconstrained binary optimization [1.104960878651584]
We develop a way to encode several NP-Complete problems in Abstract Argumentation to Quadratic Unconstrained Binary Optimization problems.
With the QUBO formulation, exploiting new computing architectures, such as Quantum and Digital Annealers, is possible.
We performed tests to prove the correctness and applicability of classical problems in Argumentation and enforcement of argument sets.
arXiv Detail & Related papers (2024-09-09T11:29:46Z) - Sum-of-Squares inspired Quantum Metaheuristic for Polynomial Optimization with the Hadamard Test and Approximate Amplitude Constraints [76.53316706600717]
Recently proposed quantum algorithm arXiv:2206.14999 is based on semidefinite programming (SDP)
We generalize the SDP-inspired quantum algorithm to sum-of-squares.
Our results show that our algorithm is suitable for large problems and approximate the best known classicals.
arXiv Detail & Related papers (2024-08-14T19:04:13Z) - Symmetric Tensor Networks for Generative Modeling and Constrained
Combinatorial Optimization [72.41480594026815]
Constrained optimization problems abound in industry, from portfolio optimization to logistics.
One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space.
In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
arXiv Detail & Related papers (2022-11-16T18:59:54Z) - Sparse Polynomial Optimization: Theory and Practice [5.27013884159732]
Book presents several efforts to tackle this challenge with important scientific implications.
It provides alternative optimization schemes that scale well in terms of computational complexity.
We present sparsity-exploiting hierarchies of relaxations, for either unconstrained or constrained problems.
arXiv Detail & Related papers (2022-08-23T18:56:05Z) - Penalty Weights in QUBO Formulations: Permutation Problems [0.0]
optimisation algorithms designed to work on quantum computers have been of research interest in recent years.
Many of these solver can only optimise problems that are in binary and quadratic form.
There are many optimisation problems that are naturally represented as permutations.
We propose new static methods of calculating penalty weights which lead to more promising results.
arXiv Detail & Related papers (2022-06-20T22:00:38Z) - Comparing the Digital Annealer with Classical Evolutionary Algorithm [0.0]
Examples of solvers that use specialised hardware are IBM's Quantum System One and D-wave's Quantum Annealer (QA) and Fujitsu's Digital Annealer (DA)
arXiv Detail & Related papers (2022-05-26T19:04:20Z) - Constrained mixers for the quantum approximate optimization algorithm [55.41644538483948]
We present a framework for constructing mixing operators that restrict the evolution to a subspace of the full Hilbert space.
We generalize the "XY"-mixer designed to preserve the subspace of "one-hot" states to the general case of subspaces given by a number of computational basis states.
Our analysis also leads to valid Trotterizations for "XY"-mixer with fewer CX gates than is known to date.
arXiv Detail & Related papers (2022-03-11T17:19:26Z) - An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm
for First-order and Zeroth-order Optimization [111.24899593052851]
Conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community.
ARCS is the first zeroth-order conditional gradient sliding type algorithms solving convex problems in zeroth-order optimization.
In first-order optimization, the convergence results of ARCS substantially outperform previous algorithms in terms of the number of gradient query oracle.
arXiv Detail & Related papers (2021-09-18T07:08:11Z) - Computational Overhead of Locality Reduction in Binary Optimization
Problems [0.0]
We discuss the effects of locality reduction needed for the majority of solvers that can only accommodate 2-local (quadratic) cost functions.
Using a parallel tempering Monte Carlo solver on Microsoft Azure Quantum, we show that post reduction to a corresponding 2-local representation the problems become considerably harder to solve.
arXiv Detail & Related papers (2020-12-17T15:49:55Z) - Gradient Free Minimax Optimization: Variance Reduction and Faster
Convergence [120.9336529957224]
In this paper, we denote the non-strongly setting on the magnitude of a gradient-free minimax optimization problem.
We show that a novel zeroth-order variance reduced descent algorithm achieves the best known query complexity.
arXiv Detail & Related papers (2020-06-16T17:55:46Z) - Hardness of Random Optimization Problems for Boolean Circuits,
Low-Degree Polynomials, and Langevin Dynamics [78.46689176407936]
We show that families of algorithms fail to produce nearly optimal solutions with high probability.
For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory.
arXiv Detail & Related papers (2020-04-25T05:45:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.