@article {2770, title = {Meta Hamiltonian Learning}, year = {2021}, month = {4/9/2021}, abstract = {

Efficient characterization of quantum devices is a significant challenge critical for the development of large scale quantum computers. We consider an experimentally motivated situation, in which we have a decent estimate of the Hamiltonian, and its parameters need to be characterized and fine-tuned frequently to combat drifting experimental variables. We use a machine learning technique known as meta-learning to learn a more efficient optimizer for this task. We consider training with the nearest-neighbor Ising model and study the trained model\&$\#$39;s generalizability to other Hamiltonian models and larger system sizes. We observe that the meta-optimizer outperforms other optimization methods in average loss over test samples. This advantage follows from the meta-optimizer being less likely to get stuck in local minima, which highly skews the distribution of the final loss of the other optimizers. In general, meta-learning decreases the number of calls to the experiment and reduces the needed classical computational resources.

}, url = {https://arxiv.org/abs/2104.04453}, author = {Przemyslaw Bienias and Alireza Seif and Mohammad Hafezi} } @article {2466, title = {Machine learning the thermodynamic arrow of time}, journal = {Nat. Phys.}, year = {2020}, month = {09/21/2020}, pages = {1-9}, abstract = {

The mechanism by which thermodynamics sets the direction of time\&$\#$39;s arrow has long fascinated scientists. Here, we show that a machine learning algorithm can learn to discern the direction of time\&$\#$39;s arrow when provided with a system\&$\#$39;s microscopic trajectory as input. The performance of our algorithm matches fundamental bounds predicted by nonequilibrium statistical mechanics. Examination of the algorithm\&$\#$39;s decision-making process reveals that it discovers the underlying thermodynamic mechanism and the relevant physical observables. Our results indicate that machine learning techniques can be used to study systems out of equilibrium, and ultimately to uncover physical principles.

}, doi = {https://doi.org/10.1038/s41567-020-1018-2}, url = {https://arxiv.org/abs/1909.12380}, author = {Alireza Seif and Mohammad Hafezi and Christopher Jarzynski} } @article {2287, title = {Machine learning assisted readout of trapped-ion qubits}, journal = { J. Phys. B: At. Mol. Opt. Phys.}, volume = {51}, year = {2018}, month = {2018/05/01}, chapter = {174006}, abstract = {

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach allows the incorporation of any number of features of the data with minimal modifications to the underlying network architecture. We experimentally illustrate this approach in the readout of trapped-ion qubits using additional spatial and temporal features in the data. Using this neural network classifier, we efficiently treat qubit readout crosstalk, resulting in a 30\% improvement in detection error over the conventional threshold method. Our approach does not depend on the specific details of the system and can be readily generalized to other quantum computing platforms.

}, doi = {https://doi.org/10.1088/1361-6455/aad62b}, url = {https://arxiv.org/abs/1804.07718}, author = {Alireza Seif and Kevin A. Landsman and Norbert M. Linke and Caroline Figgatt and C. Monroe and Mohammad Hafezi} } @article {2508, title = {Measurement Protocol for the Entanglement Spectrum of Cold Atoms}, journal = {Phys. Rev. X }, volume = {6(4)}, year = {2016}, month = {2016/11/22}, abstract = {

Entanglement, and, in particular the entanglement spectrum, plays a major role in characterizing many-body quantum systems. While there has been a surge of theoretical works on the subject, no experimental measurement has been performed to date because of the lack of an implementable measurement scheme. Here, we propose a measurement protocol to access the entanglement spectrum of many-body states in experiments with cold atoms in optical lattices. Our scheme effectively performs a Ramsey spectroscopy of the entanglement Hamiltonian and is based on the ability to produce several copies of the state under investigation together with the possibility to perform a global swap gate between two copies conditioned on the state of an auxiliary qubit. We show how the required conditional swap gate can be implemented with cold atoms, either by using Rydberg interactions or coupling the atoms to a cavity mode. We illustrate these ideas on a simple (extended) Bose-Hubbard model where such a measurement protocol reveals topological features of the Haldane phase.\ 

}, doi = {https://doi.org/10.1103/PhysRevX.6.041033}, url = {https://arxiv.org/abs/1605.08624}, author = {Hannes Pichler and Guanyu Zhu and Alireza Seif and Peter Zoller and Mohammad Hafezi} }