13 research outputs found
Hamiltonian inference from dynamical excitations in confined quantum magnets
Quantum-disordered models provide a versatile platform to explore the
emergence of quantum excitations in many-body systems. The engineering of spin
models at the atomic scale with scanning tunneling microscopy and the local
imaging of excitations with electrically driven spin resonance has risen as a
powerful strategy to image spin excitations in finite quantum spin systems.
Here, focusing on lattices as realized by Ti in MgO, we show that
dynamical spin excitations provide a robust strategy to infer the nature of the
underlying Hamiltonian. We show that finite-size interference of the dynamical
many-body spin excitations of a generalized long-range Heisenberg model allows
the underlying spin couplings to be inferred. We show that the spatial
distribution of local spin excitations in Ti islands and ladders directly
correlates with the underlying ground state in the thermodynamic limit. Using a
supervised learning algorithm, we demonstrate that the different parameters of
the Hamiltonian can be extracted by providing the spatially and
frequency-dependent local excitations that can be directly measured by
electrically driven spin resonance with scanning tunneling microscopy. Our
results put forward local dynamical excitations in confined quantum spin models
as versatile witnesses of the underlying ground state, providing an
experimentally robust strategy for Hamiltonian inference in complex real spin
models.Comment: 11 pages, 10 figure
Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
Determining Hamiltonian parameters from noisy experimental measurements is a
key task for the control of experimental quantum systems. An experimental
platform that recently emerged, and where knowledge of Hamiltonian parameters
is crucial to fine-tune the system, is that of quantum dot-based Kitaev chains.
In this work, we demonstrate an adversarial machine learning algorithm to
determine the parameters of a quantum dot-based Kitaev chain. We train a
convolutional conditional generative adversarial neural network (Conv-cGAN)
with simulated differential conductance data and use the model to predict the
parameters at which Majorana bound states are predicted to appear. In
particular, the Conv-cGAN model facilitates a rapid, numerically efficient
exploration of the phase diagram describing the transition between elastic
co-tunneling and crossed Andreev reflection regimes. We verify the theoretical
predictions of the model by applying it to experimentally measured conductance
obtained from a minimal Kitaev chain consisting of two spin-polarized quantum
dots coupled by a superconductor-semiconductor hybrid. Our model accurately
predicts, with an average success probability of \%, whether the
measurement was taken in the elastic co-tunneling or crossed Andreev
reflection-dominated regime. Our work constitutes a stepping stone towards
fast, reliable parameter prediction for tuning quantum-dot systems into
distinct Hamiltonian regimes. Ultimately, our results yield a strategy to
support Kitaev chain tuning that is scalable to longer chains
Real-time adaptive estimation of decoherence timescales for a single qubit
Characterizing the time over which quantum coherence survives is critical for any implementation of quantum bits, memories, and sensors. The usual method for determining a quantum system's decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in postprocessing. Here we present an adaptive multiparameter Bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales (T1, T2∗ - , and T2) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. A further speedup of a factor approximately 2 can be realized by performing our optimization with respect to sensitivity as opposed to variance
Out-of-distribution generalization for learning quantum dynamics
Generalization bounds are a critical tool to assess the training data
requirements of Quantum Machine Learning (QML). Recent work has established
guarantees for in-distribution generalization of quantum neural networks
(QNNs), where training and testing data are assumed to be drawn from the same
data distribution. However, there are currently no results on
out-of-distribution generalization in QML, where we require a trained model to
perform well even on data drawn from a distribution different from the training
distribution. In this work, we prove out-of-distribution generalization for the
task of learning an unknown unitary using a QNN and for a broad class of
training and testing distributions. In particular, we show that one can learn
the action of a unitary on entangled states using only product state training
data. We numerically illustrate this by showing that the evolution of a
Heisenberg spin chain can be learned using only product training states. Since
product states can be prepared using only single-qubit gates, this advances the
prospects of learning quantum dynamics using near term quantum computers and
quantum experiments, and further opens up new methods for both the classical
and quantum compilation of quantum circuits.Comment: 7 pages (main body) + 14 pages (references and appendix); 4+1 figure
An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits
Efforts to scale-up quantum computation have reached a point where the
principal limiting factor is not the number of qubits, but the entangling gate
infidelity. However, a highly detailed system characterization required to
understand the underlying errors is an arduous process and impractical with
increasing chip size. Open-loop optimal control techniques allow for the
improvement of gates but are limited by the models they are based on. To
rectify the situation, we provide a new integrated open-source tool-set for
Control, Calibration and Characterization (), capable of open-loop pulse
optimization, model-free calibration, model fitting and refinement. We present
a methodology to combine these tools to find a quantitatively accurate system
model, high-fidelity gates and an approximate error budget, all based on a
high-performance, feature-rich simulator. We illustrate our methods using
fixed-frequency superconducting qubits for which we learn model parameters to
an accuracy of and derive a coherence limited cross-resonance (CR) gate
that achieves fidelity without need for calibration.Comment: Source code available at http://q-optimize.org; added reference
Inferring interpretable dynamical generators of local quantum observables from projective measurements through machine learning
To characterize the dynamical behavior of many-body quantum systems, one is usually interested in the evolution of so-called order parameters rather than in characterizing the full quantum state. In many situations, these quantities coincide with the expectation value of local observables, such as the magnetization or the particle density. In experiment, however, these expectation values can only be obtained with a finite degree of accuracy due to the effects of the projection noise. Here, we utilize a machine-learning approach to infer the dynamical generator governing the evolution of local observables in a many-body system from noisy data. To benchmark our method, we consider a variant of the quantum Ising model and generate synthetic experimental data, containing the results of N projective measurements at M sampling points in time, using the time-evolving block-decimation algorithm. As we show, across a wide range of parameters the dynamical generator of local observables can be approximated by a Markovian quantum master equation. Our method is not only useful for extracting effective dynamical generators from many-body systems but may also be applied for inferring decoherence mechanisms of quantum simulation and computing platforms
Real-time adaptive estimation of decoherence timescales for a single qubit
Characterizing the time over which quantum coherence survives is critical for any implementation of quantum bits, memories, and sensors. The usual method for determining a quantum system’s decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in postprocessing. Here we present an adaptive multiparameter Bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales (T1, T
∗2, and T2) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. This approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. A further speedup of a factor approximately 2 can be realized by performing our optimization with respect to sensitivity as opposed to varianc