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Found 27 papers, 15 papers with code
Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network
Quantum error-correction is a prerequisite for reliable quantum computation.
Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift
Here, we introduce an artificial neural network that incorporates homeostatic features.
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.
A quantum algorithm for training wide and deep classical neural networks
Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for quantum machine learning.
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.
Learnability and Complexity of Quantum Samples
Given a quantum circuit, a quantum computer can sample the output distribution exponentially faster in the number of bits than classical computers.
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
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.
Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm
In this work, we derive the non-Markovian dynamics between TLS and qubits during a SWAP-like two-qubit gate and the associated average gate fidelity for frequency-tunable Transmon qubits.
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).
Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation
Noisy Intermediate-Scale Quantum (NISQ) computers aim to perform computational tasks beyond the capabilities of the most powerful classical computers, thereby achieving "Quantum Supremacy", a major milestone in quantum computing.
Quantum Physics Computational Complexity Computational Physics
For Fixed Control Parameters the Quantum Approximate Optimization Algorithm's Objective Function Value Concentrates for Typical Instances
For higher depth circuits the numerics also show concentration and we argue for this using the Law of Large Numbers.
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.
Barren plateaus in quantum neural network training landscapes
Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits.
Classification with Quantum Neural Networks on Near Term Processors
We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.
Quantum Physics
Characterizing Quantum Supremacy in Near-Term Devices
We study the task of sampling from the output distributions of (pseudo-)random quantum circuits, a natural task for benchmarking quantum computers.
Quantum Physics
Totally Corrective Boosting with Cardinality Penalization
If actual quantum optimization were to be used with this algorithm in the future, we would expect equivalent or superior results at much smaller time and energy costs during training.
Probabilistic Label Relation Graphs with Ising Models
In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e. g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear).
Bayesian Sampling Using Stochastic Gradient Thermostats
To remedy this problem, we show that one can leverage a small number of additional variables in order to stabilize momentum fluctuations induced by the unknown noise.
Construction of non-convex polynomial loss functions for training a binary classifier with quantum annealing
Quantum annealing is a heuristic quantum algorithm which exploits quantum resources to minimize an objective function embedded as the energy levels of a programmable physical system.
Training a Binary Classifier with the Quantum Adiabatic Algorithm
To bring it into a format that allows the application of adiabatic quantum computing (AQC), we first show that the bit-precision with which the weights need to be represented only grows logarithmically with the ratio of the number of training examples to the number of weak classifiers.
Quantum Physics
