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Quantum supremacy using a programmable superconducting processor
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
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. Classical algorithms for solving such problems typically take
Environment-assisted quantum transport
Transport phenomena at the nanoscale are of interest due to the presence of both quantum and classical behavior. In this work, we demonstrate that quantum transport efficiency can be enhanced by a
Environment-assisted quantum walks in photosynthetic energy transfer.
A theoretical framework for studying the role of quantum interference effects in energy transfer dynamics of molecular arrays interacting with a thermal bath within the Lindblad formalism is developed and an effective interplay between the free Hamiltonian evolution and the thermal fluctuations in the environment is demonstrated.
Quantum support vector machine for big feature and big data classification
This work shows that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples, and an exponential speedup is obtained.
Quantum principal component analysis
Characterizing an unknown quantum state typically relies on analysing the outcome of a large set of measurements. Certain quantum-processing tasks are now shown to be realizable using only
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
The application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) is demonstrated and an approximation ratio is obtained that is independent of problem size and for the first time, that performance increases with circuit depth.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.
Power of data in quantum machine learning
This work shows that some problems that are classically hard to compute can be easily predicted by classical machines learning from data and proposes a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.
Towards quantum chemistry on a quantum computer.
The application of the latest photonic quantum computer technology to calculate properties of the smallest molecular system: the hydrogen molecule in a minimal basis is reported and the complete energy spectrum is calculated to 20 bits of precision.