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Found 22 papers, 9 papers with code
Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization
Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate.
Training Deep Boltzmann Networks with Sparse Ising Machines
These results demonstrate the potential of using Ising machines for traditionally hard-to-train deep generative Boltzmann networks, with further possible improvement in nanodevice-based realizations.
A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
The transistor celebrated its 75${}^\text{th}$ birthday in 2022.
Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.
Nonequilibrium Monte Carlo for unfreezing variables in hard combinatorial optimization
In particular, for 90% of random 4-SAT instances we find solutions that are inaccessible for the best specialized deterministic algorithm known as Survey Propagation (SP) with an order of magnitude improvement in the quality of solutions for the hardest 10% instances.
Exponential suppression of bit or phase flip errors with repetitive error correction
QEC also requires that the errors are local and that performance is maintained over many rounds of error correction, two major outstanding experimental challenges.
Quantum Physics
Power of data in quantum machine learning
These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.
Observation of separated dynamics of charge and spin in the Fermi-Hubbard model
Strongly correlated quantum systems give rise to many exotic physical phenomena, including high-temperature superconductivity.
Quantum Physics
Layerwise learning for quantum neural networks
In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits.
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
A Probability Density Theory for Spin-Glass Systems
In general, evaluating the relevant physical and computational properties of such models is difficult due to critical slowing down near a phase transition.
Self-Supervised Learning of Generative Spin-Glasses with Normalizing Flows
Spin-glasses are universal models that can capture complex behavior of many-body systems at the interface of statistical physics and computer science including discrete optimization, inference in graphical models, and automated reasoning.
Scaling advantage in quantum simulation of geometrically frustrated magnets
By initializing the system in a state with topological obstruction, we observe quantum annealing (QA) relaxation timescales in excess of one microsecond.
Quantum Physics Statistical Mechanics Emerging Technologies
Learning to learn with quantum neural networks via classical neural networks
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.
Quantum-Assisted Genetic Algorithm
Reverse annealing enables the development of genetic algorithms that use quantum fluctuation for mutations and classical mechanisms for the crossovers -- we refer to these as Quantum-Assisted Genetic Algorithms (QAGAs).
Approximate optimization, sampling and spin-glass droplets discovery with tensor networks
We devise a deterministic algorithm to efficiently sample high-quality solutions of certain spin-glass systems that encode hard optimization problems.
Statistical Mechanics Quantum Physics
Efficient population transfer via non-ergodic extended states in quantum spin glass
NEE provide a mechanism for population transfer (PT) between computational states and therefore can serve as a new quantum subroutine for quantum search, quantum parallel tempering and reverse annealing optimization algorithms.
Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics Strongly Correlated Electrons
Universal discriminative quantum neural networks
This circuit learns to simulates the unknown structure of a generalized quantum measurement, or Positive-Operator-Value-Measure (POVM), that is required to optimally distinguish possible distributions of quantum inputs.
Quantum principal component analysis
The usual way to reveal properties of an unknown quantum state, given many copies of a system in that state, is to perform measurements of different observables and to analyze the measurement results statistically.
Quantum Physics
Quantum support vector machine for big data classification
Supervised machine learning is the classification of new data based on already classified training examples.
Quantum algorithms for supervised and unsupervised machine learning
Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces.
Quantum Physics
