4,588 research outputs found
Perfect zero knowledge for quantum multiprover interactive proofs
In this work we consider the interplay between multiprover interactive
proofs, quantum entanglement, and zero knowledge proofs - notions that are
central pillars of complexity theory, quantum information and cryptography. In
particular, we study the relationship between the complexity class MIP, the
set of languages decidable by multiprover interactive proofs with quantumly
entangled provers, and the class PZKMIP, which is the set of languages
decidable by MIP protocols that furthermore possess the perfect zero
knowledge property.
Our main result is that the two classes are equal, i.e., MIP
PZKMIP. This result provides a quantum analogue of the celebrated result of
Ben-Or, Goldwasser, Kilian, and Wigderson (STOC 1988) who show that MIP
PZKMIP (in other words, all classical multiprover interactive protocols can be
made zero knowledge). We prove our result by showing that every MIP
protocol can be efficiently transformed into an equivalent zero knowledge
MIP protocol in a manner that preserves the completeness-soundness gap.
Combining our transformation with previous results by Slofstra (Forum of
Mathematics, Pi 2019) and Fitzsimons, Ji, Vidick and Yuen (STOC 2019), we
obtain the corollary that all co-recursively enumerable languages (which
include undecidable problems as well as all decidable problems) have zero
knowledge MIP protocols with vanishing promise gap
Universal Intelligence: A Definition of Machine Intelligence
A fundamental problem in artificial intelligence is that nobody really knows
what intelligence is. The problem is especially acute when we need to consider
artificial systems which are significantly different to humans. In this paper
we approach this problem in the following way: We take a number of well known
informal definitions of human intelligence that have been given by experts, and
extract their essential features. These are then mathematically formalised to
produce a general measure of intelligence for arbitrary machines. We believe
that this equation formally captures the concept of machine intelligence in the
broadest reasonable sense. We then show how this formal definition is related
to the theory of universal optimal learning agents. Finally, we survey the many
other tests and definitions of intelligence that have been proposed for
machines.Comment: 50 gentle page
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
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