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Found 16 papers, 10 papers with code
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.
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
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Even more efficient quantum computations of chemistry through tensor hypercontraction
We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e. g., molecular) orbitals.
Quantum Physics Chemical 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
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 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.
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.
OpenFermion: The Electronic Structure Package for Quantum Computers
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term and fault-tolerant quantum devices.
Quantum Physics Chemical Physics Computational 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
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.
Exploiting locality in quantum computation for quantum chemistry
Accurate prediction of chemical and material properties from first principles quantum chemistry is a challenging task on traditional computers.
Quantum Physics Chemical Physics
Bayesian Network Structure Learning Using Quantum Annealing
The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.
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.
