70,528 research outputs found
Simulation-assisted learning of open quantum systems
Models for open quantum systems, which play important roles in electron
transport problems and quantum computing, must take into account the
interaction of the quantum system with the surrounding environment. Although
such models can be derived in some special cases, in most practical situations,
the exact models are unknown and have to be calibrated. This paper presents a
learning method to infer parameters in Markovian open quantum systems from
measurement data. One important ingredient in the method is a direct simulation
technique of the quantum master equation, which is designed to preserve the
completely-positive property with guaranteed accuracy. The method is
particularly helpful in the situation where the time intervals between
measurements are large. The approach is validated with error estimates and
numerical experiments
Explainable Representation Learning of Small Quantum States
Unsupervised machine learning models build an internal representation of
their training data without the need for explicit human guidance or feature
engineering. This learned representation provides insights into which features
of the data are relevant for the task at hand. In the context of quantum
physics, training models to describe quantum states without human intervention
offers a promising approach to gaining insight into how machines represent
complex quantum states. The ability to interpret the learned representation may
offer a new perspective on non-trivial features of quantum systems and their
efficient representation. We train a generative model on two-qubit density
matrices generated by a parameterized quantum circuit. In a series of
computational experiments, we investigate the learned representation of the
model and its internal understanding of the data. We observe that the model
learns an interpretable representation which relates the quantum states to
their underlying entanglement characteristics. In particular, our results
demonstrate that the latent representation of the model is directly correlated
with the entanglement measure concurrence. The insights from this study
represent proof of concept towards interpretable machine learning of quantum
states. Our approach offers insight into how machines learn to represent
small-scale quantum systems autonomously
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. An experimental
setup that transduces data from a physical system to a stable quantum memory,
and processes that data using a quantum computer, could have significant
advantages over conventional experiments in which the physical system is
measured and the outcomes are processed using a classical computer. We prove
that, in various tasks, quantum machines can learn from exponentially fewer
experiments than those required in conventional experiments. The exponential
advantage holds in predicting properties of physical systems, performing
quantum principal component analysis on noisy states, and learning approximate
models of physical dynamics. In some tasks, the quantum processing needed to
achieve the exponential advantage can be modest; for example, one can
simultaneously learn about many noncommuting observables by processing only two
copies of the system. Conducting experiments with up to 40 superconducting
qubits and 1300 quantum gates, we demonstrate that a substantial quantum
advantage can be realized using today's relatively noisy quantum processors.
Our results highlight how quantum technology can enable powerful new strategies
to learn about nature.Comment: 6 pages, 17 figures + 46 page appendix; open-source code available at
https://github.com/quantumlib/ReCirq/tree/master/recirq/qml_lf
Provably efficient machine learning for quantum many-body problems
Classical machine learning (ML) provides a potentially powerful approach to
solving challenging quantum many-body problems in physics and chemistry.
However, the advantages of ML over more traditional methods have not been
firmly established. In this work, we prove that classical ML algorithms can
efficiently predict ground state properties of gapped Hamiltonians in finite
spatial dimensions, after learning from data obtained by measuring other
Hamiltonians in the same quantum phase of matter. In contrast, under widely
accepted complexity theory assumptions, classical algorithms that do not learn
from data cannot achieve the same guarantee. We also prove that classical ML
algorithms can efficiently classify a wide range of quantum phases of matter.
Our arguments are based on the concept of a classical shadow, a succinct
classical description of a many-body quantum state that can be constructed in
feasible quantum experiments and be used to predict many properties of the
state. Extensive numerical experiments corroborate our theoretical results in a
variety of scenarios, including Rydberg atom systems, 2D random Heisenberg
models, symmetry-protected topological phases, and topologically ordered
phases.Comment: 10 pages, 12 figures + 57 page appendi
Machine learning detecting Majorana Zero Mode from Zero Bias Peak measurements
Majorana zero modes (MZMs), emerging as exotic quasiparticles that carry
non-Abelian statistics, hold great promise for achieving fault-tolerant
topological quantum computation. A key signature of the presence of MZMs is the
zero-bias peaks (ZBPs) from tunneling differential conductance. However, the
identification of MZMs from ZBPs has faced tremendous challenges, due to the
presence of topological trivial states that generate spurious ZBP signals. In
this work, we introduce a machine-learning framework that can discern MZM from
other signals using ZBP data. Quantum transport simulation from tight-binding
models is used to generate the training data, while persistent cohomology
analysis confirms the feasibility of classification via machine learning. In
particular, even with added data noise, XGBoost classifier reaches
accuracy for 1D tunneling conductance data and for 2D data incorporating
Zeeman splitting. Tests on prior ZBP experiments show that some data are more
likely to originate from MZM than others. Our model offers a quantitative
approach to assess MZMs using ZBP data. Furthermore, our results shed light on
the use of machine learning on exotic quantum systems with
experimental-computational integration
Exploring Large-Scale Entanglement in Quantum Simulation
Entanglement is a distinguishing feature of quantum many-body systems, and
uncovering the entanglement structure for large particle numbers in quantum
simulation experiments is a fundamental challenge in quantum information
science. Here we perform experimental investigations of entanglement based on
the entanglement Hamiltonian, as an effective description of the reduced
density operator for large subsystems. We prepare ground and excited states of
a 1D XXZ Heisenberg chain on a 51-ion programmable quantum simulator and
perform sample-efficient `learning' of the entanglement Hamiltonian for
subsystems of up to 20 lattice sites. Our experiments provide compelling
evidence for a local structure of the entanglement Hamiltonian. This
observation marks the first instance of confirming the fundamental predictions
of quantum field theory by Bisognano and Wichmann, adapted to lattice models
that represent correlated quantum matter. The reduced state takes the form of a
Gibbs ensemble, with a spatially-varying temperature profile as a signature of
entanglement. Our results also show the transition from area to volume-law
scaling of Von Neumann entanglement entropies from ground to excited states. As
we venture towards achieving quantum advantage, we anticipate that our findings
and methods have wide-ranging applicability to revealing and understanding
entanglement in many-body problems with local interactions including higher
spatial dimensions.Comment: 14 pages, 7 figure
Scientific intuition inspired by machine learning-generated hypotheses
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science
Scientific intuition inspired by machine learning-generated hypotheses
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human-interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science
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