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We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample…

Quantum Physics · Physics 2024-07-08 Shyam Narayanan

Learning the Hamiltonian governing a quantum system is a central task in quantum metrology, sensing, and device characterization. Existing Heisenberg-limited Hamiltonian learning protocols either require multi-qubit operations that are…

Quantum Physics · Physics 2026-01-16 Shrigyan Brahmachari , Shuchen Zhu , Iman Marvian , Yu Tong

The efficient validation of quantum devices is critical for emerging technological applications. In a wide class of use-cases the precise engineering of a Hamiltonian is required both for the implementation of gate-based quantum information…

Quantum Physics · Physics 2019-11-20 Agnes Valenti , Evert van Nieuwenburg , Sebastian Huber , Eliska Greplova

We study the problem of learning an unknown quantum many-body Hamiltonian $H$ from black-box queries to its time evolution $e^{-\mathrm{i} H t}$. Prior proposals for solving this task either impose some assumptions on $H$, such as its…

Quantum Physics · Physics 2025-06-27 Andrew Zhao

We study the problem of certifying local Hamiltonians from real-time access to their dynamics. Given oracle access to $e^{-itH}$ for an unknown $k$-local Hamiltonian $H$ and a fully specified target Hamiltonian $H_0$, the goal is to decide…

Quantum Physics · Physics 2025-12-11 Junseo Lee , Myeongjin Shin

We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\mathrm{i} Ht}$ for an unknown local Hamiltonian $H = \sum_{a = 1}^m \lambda_a E_a$ on $n$ qubits, the goal is to recover $H$.…

Quantum Physics · Physics 2026-05-11 Ainesh Bakshi , Allen Liu , Ankur Moitra , Ewin Tang

We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and $k$-wise uniformity of probability distributions. Closeness testing is the problem of distinguishing whether two $n$-dimensional…

Quantum Physics · Physics 2024-06-27 Jingquan Luo , Qisheng Wang , Lvzhou Li

We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves…

Recovering an unknown Hamiltonian from measurements is an increasingly important task for certification of noisy quantum devices and simulators. Recent works have succeeded in recovering the Hamiltonian of an isolated quantum system with…

Quantum Physics · Physics 2019-01-24 Eyal Bairey , Itai Arad , Netanel H. Lindner

Recent advances have demonstrated that $\mathcal{O}(\log M)$ measurements suffice to predict $M$ properties of arbitrarily large quantum many-body systems. However, these remarkable findings assume that the properties to be predicted are…

Quantum Physics · Physics 2024-10-22 Jerry Huang , Laura Lewis , Hsin-Yuan Huang , John Preskill

We ask whether the knowledge of a single eigenstate of a local Hamiltonian is sufficient to uniquely determine the Hamiltonian. We present evidence that the answer is "yes" for generic local Hamiltonians, given either the ground state or an…

Quantum Physics · Physics 2019-07-10 Xiao-Liang Qi , Daniel Ranard

Expectation value estimation is ubiquitous in quantum algorithms. The expectation value of a Hamiltonian, which is essential in various practical applications, is often estimated by measuring a large number of Pauli strings on quantum…

We examine the problem of determining if a 2-local Hamiltonian is stoquastic by local basis changes. We analyze this problem for two-qubit Hamiltonians, presenting some basic tools and giving a concrete example where using unitaries beyond…

Quantum Physics · Physics 2019-05-08 Joel Klassen , Barbara M. Terhal

We examine the problem of determining whether a multi-qubit two-local Hamiltonian can be made stoquastic by single-qubit unitary transformations. We prove that when such a Hamiltonian contains one-local terms, then this task can be NP-hard.…

Quantum Physics · Physics 2020-04-07 Joel Klassen , Milad Marvian , Stephen Piddock , Marios Ioannou , Itay Hen , Barbara Terhal

Characterizing quantum many-body systems is a fundamental problem across physics, chemistry, and materials science. While significant progress has been made, many existing Hamiltonian learning protocols demand digital quantum control over…

Quantum Physics · Physics 2025-10-10 Sitan Chen , Jordan Cotler , Hsin-Yuan Huang

We give a classical $1/(qk+1)$-approximation for the maximum eigenvalue of a $k$-sparse fermionic Hamiltonian with strictly $q$-local terms, as well as a $1/(4k+1)$-approximation when the Hamiltonian has both $2$-local and $4$-local terms.…

Quantum Physics · Physics 2023-09-15 Daniel Hothem , Ojas Parekh , Kevin Thompson

Hybrid quantum systems with different particle species are fundamental in quantum materials and quantum information science. In this work, we establish a rigorous theoretical framework proving that, given access to an unknown spin-boson…

Quantum Physics · Physics 2025-05-01 Lixing Zhang , Ze-Xun Lin , Prineha Narang , Di Luo

We study the problem of efficiently learning an unknown $n$-qubit unitary channel in diamond distance given query access. We present a general framework showing that if Pauli operators remain low-complexity under conjugation by a unitary,…

Quantum Physics · Physics 2026-04-07 Sabee Grewal , Daniel Liang

Implementing the time evolution under a desired target Hamiltonian is critical for various applications in quantum science. Due to the exponential increase in the number of parameters with system size and experimental imperfections, this…

Quantum Physics · Physics 2025-12-11 Pascal Baßler , Markus Heinrich , Martin Kliesch

We give the first almost optimal polynomial-time proper learning algorithm of Boolean sparse multivariate polynomial under the uniform distribution. For $s$-sparse polynomial over $n$ variables and $\epsilon=1/s^\beta$, $\beta>1$, our…

Machine Learning · Computer Science 2022-02-08 Nader H. Bshouty