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In this paper, we study the subset-sum problem by using a quantum heuristic approach similar to the verification circuit of quantum Arthur-Merlin games. Under described certain assumptions, we show that the exact solution of the subset sum…

Quantum Physics · Physics 2017-09-25 Ammar Daskin

Fundamentally, every static program analyser searches for a proof through a combination of heuristics providing candidate solutions and a candidate validation technique. Essentially, the heuristic reduces a second-order problem to a…

Logic in Computer Science · Computer Science 2015-01-20 Cristina David , Daniel Kroening , Matt Lewis

Maslov's class $\overline{\text{K}}$ is an expressive fragment of First-Order Logic known to have decidable satisfiability problem, whose exact complexity, however, has not been established so far. We show that $\overline{\text{K}}$ has the…

Logic in Computer Science · Computer Science 2024-07-19 Oskar Fiuk , Emanuel Kieronski , Vincent Michielini

Although it is believed unlikely that $\NP$-hard problems admit efficient quantum algorithms, it has been shown that a quantum verifier can solve $\NP$-complete problems given a "short" quantum proof; more precisely, $\NP\subseteq…

Quantum Physics · Physics 2011-06-22 Salman Beigi

Based on the tools of limiting variational analysis, we derive a sequential necessary optimality condition for nonsmooth mathematical programs which holds without any additional assumptions. In order to ensure that stationary points in this…

Optimization and Control · Mathematics 2023-06-22 Patrick Mehlitz

A quantum constraint problem is a frustration-free Hamiltonian problem: given a collection of local operators, is there a state that is in the ground state of each operator simultaneously? It has previously been shown that these problems…

Quantum Physics · Physics 2021-07-22 Alex Meiburg

Motivated by problems arising in decentralized control problems and non-cooperative Nash games, we consider a class of strongly monotone Cartesian variational inequality (VI) problems, where the mappings either contain expectations or their…

Optimization and Control · Mathematics 2013-01-10 Farzad Yousefian , Angelia Nedić , Uday V. Shanbhag

Multi-agent reinforcement learning (MARL) is often modeled using the framework of Markov games (also called stochastic games or dynamic games). Most of the existing literature on MARL concentrates on zero-sum Markov games but is not…

Computer Science and Game Theory · Computer Science 2022-12-20 Jayakumar Subramanian , Amit Sinha , Aditya Mahajan

Quantum k-SAT is the problem of deciding whether there is a n-qubit state which is perpendicular to a set of vectors, each of which lies in the Hilbert space of k qubits. Equivalently, the problem is to decide whether a particular type of…

Quantum Physics · Physics 2014-09-19 Sergey Bravyi , Cristopher Moore , Alexander Russell

We present an efficient proof system for Multipoint Arithmetic Circuit Evaluation: for every arithmetic circuit $C(x_1,\ldots,x_n)$ of size $s$ and degree $d$ over a field ${\mathbb F}$, and any inputs $a_1,\ldots,a_K \in {\mathbb F}^n$,…

Computational Complexity · Computer Science 2016-01-20 Ryan Williams

We provide an optimization-based argument for the monotonicity of the multiplicative algorithm (MA) for a class of optimal experimental design problems considered in Yu (2010). Our proof avoids introducing auxiliary variables (or problems)…

Optimization and Control · Mathematics 2025-08-12 Renbo Zhao

We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought;…

Artificial Intelligence · Computer Science 2007-05-23 M. L. Littman , J. Goldsmith , M. Mundhenk

Classical satisfiability (SAT) and quantum satisfiability (QSAT) are complete problems for the complexity classes NP and QMA which are believed to be intractable for classical and quantum computers, respectively. Statistical ensembles of…

Quantum Physics · Physics 2015-10-07 Ionut-Dragos Potirniche , C. R. Laumann , S. L. Sondhi

We study the Active Simple Hypothesis Testing (ASHT) problem, a simpler variant of the Fixed Budget Best Arm Identification problem. In this work, we provide novel game theoretic formulation of the upper bounds of the ASHT problem. This…

Machine Learning · Computer Science 2025-04-29 Sushant Vijayan

We prove that 2-Local Hamiltonian (2-LH) with Low Complexity problem is QCMA-complete by combining the results from the QMA-completeness[4] of 2-LH and QCMA-completeness of 3-LH with Low Complexity[6]. The idea is straightforward. It has…

Computational Complexity · Computer Science 2019-09-10 Ying-hao Chen

We consider the random $k$-SAT problem with $n$ variables, $m=m(n)$ clauses, and clause density $\alpha=\lim_{n\to\infty}m/n$ for $k=2,3$. It is known that if $\alpha$ is small enough, then the random $k$-SAT problem admits a solution with…

Probability · Mathematics 2025-04-17 Andreas Basse-O'Connor , Tobias Lindhardt Overgaard , Mette Skjøtt

Alongside the effort underway to build quantum computers, it is important to better understand which classes of problems they will find easy and which others even they will find intractable. We study random ensembles of the QMA$_1$-complete…

Quantum Physics · Physics 2010-04-29 C. R. Laumann , R. Moessner , A. Scardicchio , S. L. Sondhi

We study the complexity of QMA proof systems with inverse exponentially small promise gap. We show that this class can be exactly characterized by PSPACE, the class of problems solvable with a polynomial amount of memory. As applications we…

Quantum Physics · Physics 2016-01-11 Bill Fefferman , Cedric Lin

This thesis is divided in two parts. The first presents an overview of known results in statistical mechanics of disordered systems and its approach to random combinatorial optimization problems. The second part is a discussion of two…

Statistical Mechanics · Physics 2008-01-21 Fabrizio Altarelli

This work is motivated by the challenges of applying the sample average approximation (SAA) method to multistage stochastic programming with an unknown continuous-state Markov process. While SAA is widely used in static and two-stage…

Optimization and Control · Mathematics 2024-12-30 Hyuk Park , Grani A. Hanasusanto