no code implementations • 5 Sep 2024 • Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li

Previously, Huang, Chen, and Preskill proved a surprising result that even if $E$ is arbitrary, this task can be solved in time roughly $n^{O(\log(1/\epsilon))}$, where $\epsilon$ is the target prediction error.

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 • 18 Jan 2024 • Hsin-Yuan Huang, Yunchao Liu, Michael Broughton, Isaac Kim, Anurag Anshu, Zeph Landau, Jarrod R. McClean

Despite fundamental interests in learning quantum circuits, the existence of a computationally efficient algorithm for learning shallow quantum circuits remains an open question.

no code implementations • 30 Oct 2023 • Haimeng Zhao, Laura Lewis, Ishaan Kannan, Yihui Quek, Hsin-Yuan Huang, Matthias C. Caro

While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers.

no code implementations • 23 Sep 2023 • Senrui Chen, Changhun Oh, Sisi Zhou, Hsin-Yuan Huang, Liang Jiang

In this work, we consider learning algorithms without entanglement to be those that only utilize states, measurements, and operations that are separable between the main system of interest and an ancillary system.

no code implementations • 22 Mar 2023 • Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph, Matthias C. Caro, Patrick J. Coles, Hsin-Yuan Huang, Zoë Holmes

Quantum process learning is emerging as an important tool to study quantum systems.

no code implementations • 16 Mar 2023 • M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, Patrick J. Coles

At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics.

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 • 12 Dec 2022 • Katherine Van Kirk, Jordan Cotler, Hsin-Yuan Huang, Mikhail D. Lukin

Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science.

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 • 13 Oct 2022 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li

The recent proliferation of NISQ devices has made it imperative to understand their computational power.

no code implementations • 6 Oct 2022 • Hsin-Yuan Huang, Yu tong, Di Fang, Yuan Su

In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$ experiments.

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.

no code implementations • 21 Apr 2022 • Joe Gibbs, Zoë Holmes, Matthias C. Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T. Sornborger, Patrick J. Coles

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated.

no code implementations • 21 Apr 2022 • Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles, Zoë Holmes

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 different distribution to the training distribution.

no code implementations • 1 Dec 2021 • Jordan Cotler, Hsin-Yuan Huang, Jarrod R. McClean

In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs.

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.

no code implementations • 10 Nov 2021 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li

We prove that given the ability to make entangled measurements on at most $k$ replicas of an $n$-qubit state $\rho$ simultaneously, there is a property of $\rho$ which requires at least order $2^n$ measurements to learn.

no code implementations • 10 Nov 2021 • Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li

We study the power of quantum memory for learning properties of quantum systems and dynamics, which is of great importance in physics and chemistry.

no code implementations • 9 Nov 2021 • Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, Patrick J. Coles

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i. e., generalizing).

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.

no code implementations • 20 Jan 2021 • Hsin-Yuan Huang, Lei Zhang

In this paper, we continue to consider the generalized Liouville system: $$ \Delta_g u_i+\sum_{j=1}^n a_{ij}\rho_j\left(\frac{h_j e^{u_j}}{\int h_j e^{u_j}}- {1} \right)=0\quad\text{in \,}M,\quad i\in I=\{1,\cdots, n\}, $$ where $(M, g)$ is a Riemann surface $M$ with volume $1$, $h_1,.., h_n$ are positive smooth functions and $\rho_j\in \mathbb R^+$($j\in I$).

Analysis of PDEs Mathematical Physics Mathematical Physics 35J60, 35J55

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.

1 code implementation • 3 Nov 2020 • Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod R. McClean

These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.

no code implementations • 26 Aug 2020 • Chi-Fang Chen, Hsin-Yuan Huang, Richard Kueng, Joel A. Tropp

qDRIFT achieves a gate count that does not explicitly depend on the number of terms in the Hamiltonian, which contrasts with Suzuki formulas.

Quantum Physics Probability

4 code implementations • 6 Mar 2020 • Michael Broughton, Guillaume Verdon, Trevor McCourt, Antonio J. Martinez, Jae Hyeon Yoo, Sergei V. Isakov, Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao, Roeland Wiersema, Hsin-Yuan Huang, Jarrod R. McClean, Ryan Babbush, Sergio Boixo, Dave Bacon, Alan K. Ho, Hartmut Neven, Masoud Mohseni

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.

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.

no code implementations • 23 Aug 2019 • Hsin-Yuan Huang, Richard Kueng

Predicting features of complex, large-scale quantum systems is essential to the characterization and engineering of quantum architectures.

1 code implementation • ICLR 2019 • Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question.

Ranked #1 on Question Answering on QuAC

3 code implementations • ICLR 2018 • Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, Weizhu Chen

This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.

Ranked #26 on Question Answering on SQuAD1.1 dev

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