no code implementations • 10 Apr 2024 • Hsin-Yuan Huang, John Preskill, Mehdi Soleimanifar

Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science.

no code implementations • 30 Jan 2023 • Laura Lewis, Hsin-Yuan Huang, Viet T. Tran, Sebastian Lehner, Richard Kueng, John Preskill

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics.

1 code implementation • 26 Oct 2022 • Hsin-Yuan Huang, Sitan Chen, John Preskill

We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits.

no code implementations • 28 Apr 2022 • Hsin-Yuan Huang, Steven T. Flammia, John Preskill

When one cannot explore the full state space but all operations are approximately known and noise in Clifford gates is gate-independent, we find an efficient algorithm for learning all operations up to a single unlearnable parameter characterizing the fidelity of the initial state.

1 code implementation • 1 Dec 2021 • Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean

Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.

3 code implementations • 23 Jun 2021 • Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V. Albert, John Preskill

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.

1 code implementation • 7 Jan 2021 • Hsin-Yuan Huang, Richard Kueng, John Preskill

We prove that for any input distribution $\mathcal{D}(x)$, a classical ML model can provide accurate predictions on average by accessing $\mathcal{E}$ a number of times comparable to the optimal quantum ML model.

4 code implementations • 18 Feb 2020 • Hsin-Yuan Huang, Richard Kueng, John Preskill

This description, called a classical shadow, can be used to predict many different properties: order $\log M$ measurements suffice to accurately predict $M$ different functions of the state with high success probability.

1 code implementation • 26 Sep 2018 • Aleksander Kubica, John Preskill

We propose a new cellular automaton (CA), the Sweep Rule, which generalizes Toom's rule to any locally Euclidean lattice.

Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics

no code implementations • 2 Jan 2018 • John Preskill

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future.

Quantum Physics Strongly Correlated Electrons

no code implementations • 17 Nov 2009 • Hui Khoon Ng, Daniel A. Lidar, John Preskill

We study how dynamical decoupling (DD) pulse sequences can improve the reliability of quantum computers.

Quantum Physics Mesoscale and Nanoscale Physics

no code implementations • 11 Oct 2005 • Alexei Kitaev, John Preskill

We formulate a universal characterization of the many-particle quantum entanglement in the ground state of a topologically ordered two-dimensional medium with a mass gap.

High Energy Physics - Theory Strongly Correlated Electrons Quantum Physics

1 code implementation • 1 Mar 2000 • Peter W. Shor, John Preskill

We prove the security of the 1984 protocol of Bennett and Brassard (BB84) for quantum key distribution.

Quantum Physics

no code implementations • 19 Dec 1997 • John Preskill

The discovery of quantum error correction has greatly improved the long-term prospects for quantum computing technology.

Quantum Physics

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