In a Merlin–Arthur proof system, the proof verifier (Arthur) accepts valid proofs (from Merlin) with probability 1, and rejects invalid proofs with probability arbitrarily close to 1. The running time of such a system is defined to be the length of Merlin’s proof plus the running time of Arthur. We provide new Merlin–Arthur proof systems for some key problems in fine-grained complexity. In several cases our proof systems have optimal running time. Our main results include: Certifying that a list of Counting the number of Computing the All-Pairs Shortest Distances matrix for an Certifying that an Certifying a Quantified Boolean Formula is true can be done in Merlin–Arthur time
Due to the centrality of these problems in fine-grained complexity, our results have consequences for many other problems of interest. For example, our work implies that certifying there is no Subset Sum solution to
The quantum simulation of quantum chemistry is a promising application of quantum computers. However, for
- Award ID(s):
- 1747426
- NSF-PAR ID:
- 10231543
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Quantum Information
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2056-6387
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract n integers has no 3-SUM solution can be done in Merlin–Arthur time . Previously, Carmosino et al. [ITCS 2016] showed that the problem has a nondeterministic algorithm running in$$\tilde{O}(n)$$ time (that is, there is a proof system with proofs of length$$\tilde{O}(n^{1.5})$$ and a deterministic verifier running in$$\tilde{O}(n^{1.5})$$ time).$$\tilde{O}(n^{1.5})$$ k -cliques with total edge weight equal to zero in ann -node graph can be done in Merlin–Arthur time (where$${\tilde{O}}(n^{\lceil k/2\rceil })$$ ). For odd$$k\ge 3$$ k , this bound can be further improved for sparse graphs: for example, counting the number of zero-weight triangles in anm -edge graph can be done in Merlin–Arthur time . Previous Merlin–Arthur protocols by Williams [CCC’16] and Björklund and Kaski [PODC’16] could only count$${\tilde{O}}(m)$$ k -cliques in unweighted graphs, and had worse running times for smallk .n -node graph can be done in Merlin–Arthur time . Note this is optimal, as the matrix can have$$\tilde{O}(n^2)$$ nonzero entries in general. Previously, Carmosino et al. [ITCS 2016] showed that this problem has an$$\Omega (n^2)$$ nondeterministic time algorithm.$$\tilde{O}(n^{2.94})$$ n -variablek -CNF is unsatisfiable can be done in Merlin–Arthur time . We also observe an algebrization barrier for the previous$$2^{n/2 - n/O(k)}$$ -time Merlin–Arthur protocol of R. Williams [CCC’16] for$$2^{n/2}\cdot \textrm{poly}(n)$$ SAT: in particular, his protocol algebrizes, and we observe there is no algebrizing protocol for$$\#$$ k -UNSAT running in time. Therefore we have to exploit non-algebrizing properties to obtain our new protocol.$$2^{n/2}/n^{\omega (1)}$$ . Previously, the only nontrivial result known along these lines was an Arthur–Merlin–Arthur protocol (where Merlin’s proof depends on some of Arthur’s coins) running in$$2^{4n/5}\cdot \textrm{poly}(n)$$ time.$$2^{2n/3}\cdot \textrm{poly}(n)$$ n integers can be done in Merlin–Arthur time , improving on the previous best protocol by Nederlof [IPL 2017] which took$$2^{n/3}\cdot \textrm{poly}(n)$$ time.$$2^{0.49991n}\cdot \textrm{poly}(n)$$ -
A bstract In this paper we explore
pp →W ± (ℓ ± ν )γ to in the SMEFT expansion. Calculations to this order are necessary to properly capture SMEFT contributions that grow with energy, as the interference between energy-enhanced SMEFT effects at$$ \mathcal{O}\left(1/{\Lambda}^4\right) $$ and the Standard Model is suppressed. We find that there are several dimension eight operators that interfere with the Standard Model and lead to the same energy growth, ~$$ \mathcal{O}\left(1/{\Lambda}^2\right) $$ , as dimension six squared. While energy-enhanced SMEFT contributions are a main focus, our calculation includes the complete set of$$ \mathcal{O}\left({E}^4/{\Lambda}^4\right) $$ SMEFT effects consistent with U(3)5flavor symmetry. Additionally, we include the decay of the$$ \mathcal{O}\left(1/{\Lambda}^4\right) $$ W ± → ℓ ± ν , making the calculation actually . As such, we are able to study the impact of non-resonant SMEFT operators, such as$$ \overline{q}{q}^{\prime}\to {\ell}^{\pm}\nu \gamma $$ $$ \left({L}^{\dagger }{\overline{\sigma}}^{\mu }{\tau}^IL\right)\left({Q}^{\dagger }{\overline{\sigma}}^{\nu }{\tau}^IQ\right) $$ B μν , which contribute to directly and not to$$ \overline{q}{q}^{\prime}\to {\ell}^{\pm}\nu \gamma $$ . We show several distributions to illustrate the shape differences of the different contributions.$$ \overline{q}{q}^{\prime}\to {W}^{\pm}\gamma $$ -
Abstract Approximate integer programming is the following: For a given convex body
, either determine whether$$K \subseteq {\mathbb {R}}^n$$ is empty, or find an integer point in the convex body$$K \cap {\mathbb {Z}}^n$$ which is$$2\cdot (K - c) +c$$ K , scaled by 2 from its center of gravityc . Approximate integer programming can be solved in time while the fastest known methods for exact integer programming run in time$$2^{O(n)}$$ . So far, there are no efficient methods for integer programming known that are based on approximate integer programming. Our main contribution are two such methods, each yielding novel complexity results. First, we show that an integer point$$2^{O(n)} \cdot n^n$$ can be found in time$$x^* \in (K \cap {\mathbb {Z}}^n)$$ , provided that the$$2^{O(n)}$$ remainders of each component for some arbitrarily fixed$$x_i^* \mod \ell $$ of$$\ell \ge 5(n+1)$$ are given. The algorithm is based on a$$x^*$$ cutting-plane technique , iteratively halving the volume of the feasible set. The cutting planes are determined via approximate integer programming. Enumeration of the possible remainders gives a algorithm for general integer programming. This matches the current best bound of an algorithm by Dadush (Integer programming, lattice algorithms, and deterministic, vol. Estimation. Georgia Institute of Technology, Atlanta, 2012) that is considerably more involved. Our algorithm also relies on a new$$2^{O(n)}n^n$$ asymmetric approximate Carathéodory theorem that might be of interest on its own. Our second method concerns integer programming problems in equation-standard form . Such a problem can be reduced to the solution of$$Ax = b, 0 \le x \le u, \, x \in {\mathbb {Z}}^n$$ approximate integer programming problems. This implies, for example that$$\prod _i O(\log u_i +1)$$ knapsack orsubset-sum problems withpolynomial variable range can be solved in time$$0 \le x_i \le p(n)$$ . For these problems, the best running time so far was$$(\log n)^{O(n)}$$ .$$n^n \cdot 2^{O(n)}$$ -
Abstract We continue the program of proving circuit lower bounds via circuit satisfiability algorithms. So far, this program has yielded several concrete results, proving that functions in
and other complexity classes do not have small circuits (in the worst case and/or on average) from various circuit classes$\mathsf {Quasi}\text {-}\mathsf {NP} = \mathsf {NTIME}[n^{(\log n)^{O(1)}}]$ , by showing that$\mathcal { C}$ admits non-trivial satisfiability and/or$\mathcal { C}$ # SAT algorithms which beat exhaustive search by a minor amount. In this paper, we present a new strong lower bound consequence of having a non-trivial# SAT algorithm for a circuit class . Say that a symmetric Boolean function${\mathcal C}$ f (x 1,…,x n ) issparse if it outputs 1 onO (1) values of . We show that for every sparse${\sum }_{i} x_{i}$ f , and for all “typical” , faster$\mathcal { C}$ # SAT algorithms for circuits imply lower bounds against the circuit class$\mathcal { C}$ , which may be$f \circ \mathcal { C}$ stronger than itself. In particular:$\mathcal { C}$ # SAT algorithms forn k -size -circuits running in 2$\mathcal { C}$ n /n k time (for allk ) implyN E X P does not have -circuits of polynomial size.$(f \circ \mathcal { C})$ # SAT algorithms for -size$2^{n^{{\varepsilon }}}$ -circuits running in$\mathcal { C}$ time (for some$2^{n-n^{{\varepsilon }}}$ ε > 0) implyQ u a s i -N P does not have -circuits of polynomial size.$(f \circ \mathcal { C})$ Applying
# SAT algorithms from the literature, one immediate corollary of our results is thatQ u a s i -N P does not haveE M A J ∘A C C 0∘T H R circuits of polynomial size, whereE M A J is the “exact majority” function, improving previous lower bounds againstA C C 0[Williams JACM’14] andA C C 0∘T H R [Williams STOC’14], [Murray-Williams STOC’18]. This is the first nontrivial lower bound against such a circuit class. -
Abstract Given a compact doubling metric measure space
X that supports a 2-Poincaré inequality, we construct a Dirichlet form on that is comparable to the upper gradient energy form on$$N^{1,2}(X)$$ . Our approach is based on the approximation of$$N^{1,2}(X)$$ X by a family of graphs that is doubling and supports a 2-Poincaré inequality (see [20]). We construct a bilinear form on using the Dirichlet form on the graph. We show that the$$N^{1,2}(X)$$ -limit$$\Gamma $$ of this family of bilinear forms (by taking a subsequence) exists and that$$\mathcal {E}$$ is a Dirichlet form on$$\mathcal {E}$$ X . Properties of are established. Moreover, we prove that$$\mathcal {E}$$ has the property of matching boundary values on a domain$$\mathcal {E}$$ . This construction makes it possible to approximate harmonic functions (with respect to the Dirichlet form$$\Omega \subseteq X$$ ) on a domain in$$\mathcal {E}$$ X with a prescribed Lipschitz boundary data via a numerical scheme dictated by the approximating Dirichlet forms, which are discrete objects.