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Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …
application of machine learning methods in quantum sciences. We cover the use of deep …
Quantum metrology assisted by machine learning
Quantum metrology aims to measure physical quantities based on fundamental quantum
principles, enhancing measurement precision through resources like quantum …
principles, enhancing measurement precision through resources like quantum …
Quantum metrology and sensing with many-body systems
The main power of quantum sensors is achieved when the probe is composed of several
particles. In this situation, quantum features such as entanglement contribute in enhancing …
particles. In this situation, quantum features such as entanglement contribute in enhancing …
Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor
Classifying many-body quantum states with distinct properties and phases of matter is one of
the most fundamental tasks in quantum many-body physics. However, due to the …
the most fundamental tasks in quantum many-body physics. However, due to the …
Unveiling quantum phase transitions from traps in variational quantum algorithms
Understanding quantum phase transitions in physical systems is fundamental to
characterize their behaviour at small temperatures. Achieving this requires both accessing …
characterize their behaviour at small temperatures. Achieving this requires both accessing …
Positive-definite parametrization of mixed quantum states with deep neural networks
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network
architecture that can encode positive semi-definite density operators of exponential rank …
architecture that can encode positive semi-definite density operators of exponential rank …
Quantum phase transition detection via quantum support vector machine
Unveiling quantum phase transitions (QPTs) is important for characterising physical systems
at low temperatures. However, the detection of these transitions is encumbered by …
at low temperatures. However, the detection of these transitions is encumbered by …
Quantum transport in open spin chains using neural-network quantum states
In this work we study the treatment of asymmetric open quantum systems with neural
networks based on the restricted Boltzmann machine. In particular, we are interested in the …
networks based on the restricted Boltzmann machine. In particular, we are interested in the …
Deep recurrent networks predicting the gap evolution in adiabatic quantum computing
In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a
function of the parameter varied during the adiabatic sweep is crucial in order to optimize the …
function of the parameter varied during the adiabatic sweep is crucial in order to optimize the …
[PDF][PDF] Deep recurrent networks predicting the gap evolution in adiabatic quantum computing
F Marquardt - quantum-journal.org
In adiabatic quantum computing finding the dependence of the gap of the Hamiltonian as a
function of the parameter varied during the adiabatic sweep is crucial in order to optimize the …
function of the parameter varied during the adiabatic sweep is crucial in order to optimize the …