Search Results for author: M. S. Kim

Found 9 papers, 5 papers with code

Generalization with quantum geometry for learning unitaries

1 code implementation23 Mar 2023 Tobias Haug, M. S. Kim

Generalization is the ability of quantum machine learning models to make accurate predictions on new data by learning from training data.

Out-of-Distribution Generalization Quantum Machine Learning

Faster variational quantum algorithms with quantum kernel-based surrogate models

no code implementations2 Nov 2022 Alistair W. R. Smith, A. J. Paige, M. S. Kim

We present a new optimization method for small-to-intermediate scale variational algorithms on noisy near-term quantum processors which uses a Gaussian process surrogate model equipped with a classically-evaluated quantum kernel.

Quantum machine learning of large datasets using randomized measurements

1 code implementation2 Aug 2021 Tobias Haug, Chris N. Self, M. S. Kim

Further, we efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth.

Benchmarking BIG-bench Machine Learning +1

Capacity and quantum geometry of parametrized quantum circuits

1 code implementation2 Feb 2021 Tobias Haug, Kishor Bharti, M. S. Kim

We identify a transition in the quantum geometry of the parameter space, which leads to a decay of the quantum natural gradient for deep circuits.

Single-shot discrimination of coherent states beyond the standard quantum limit

no code implementations1 Feb 2021 G. S. Thekkadath, S. Sempere-Llagostera, B. A. Bell, R. B. Patel, M. S. Kim, I. A. Walmsley

The discrimination of coherent states is a key task in optical communication and quantum key distribution protocols.

Quantum Physics Optics

Quantum rotations of nanoparticles

no code implementations1 Feb 2021 Benjamin A. Stickler, Klaus Hornberger, M. S. Kim

Rotations of microscale rigid bodies exhibit pronounced quantum phenomena that do not exist for their center-of-mass motion.

Quantum Physics

Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks

no code implementations16 Sep 2020 Alistair W. R. Smith, Johnnie Gray, M. S. Kim

Once our neural network has been trained it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data.

Meta-Learning Quantum State Tomography

Simulating quantum many-body dynamics on a current digital quantum computer

1 code implementation14 Jun 2019 Adam Smith, M. S. Kim, Frank Pollmann, Johannes Knolle

Universal quantum computers are potentially an ideal setting for simulating many-body quantum dynamics that is out of reach for classical digital computers.

Quantum Physics Mesoscale and Nanoscale Physics Strongly Correlated Electrons

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