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Preparing algebraically correlated ground states of quantum many-body systems is an important, yet challenging task for quantum simulation. We introduce a protocol that employs local projective measurements and unitary feedback for…

Quantum Physics · Physics 2026-03-12 Johannes Feldmeier , Yu-Jie Liu , Mikhail D. Lukin , Soonwon Choi

Extracting information efficiently from quantum systems is a major component of quantum information processing tasks. Randomized measurements, or classical shadows, enable predicting many properties of arbitrary quantum states using few…

Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a…

Quantum Physics · Physics 2021-11-29 Robert Klassert , Andreas Baumbach , Mihai A. Petrovici , Martin Gärttner

Measuring global quantum properties-such as the fidelity to complex multipartite states-is both an essential and experimentally challenging task. Classical shadow estimation offers favorable sample complexity, but typically relies on…

Quantum Physics · Physics 2026-02-11 Qingyue Zhang , Dayue Qin , Zhou You , Feng Xu , Jens Eisert , You Zhou

With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short…

Quantum Physics · Physics 2023-09-01 J. Helsen , M. Ioannou , J. Kitzinger , E. Onorati , A. H. Werner , J. Eisert , I. Roth

Quantum algorithms for probing ground-state properties of quantum systems require good initial states. Projection-based methods such as eigenvalue filtering rely on inputs that have a significant overlap with the low-energy subspace, which…

Quantum Physics · Physics 2024-04-10 Danial Motlagh , Modjtaba Shokrian Zini , Juan Miguel Arrazola , Nathan Wiebe

We develop a classical shadow tomography protocol utilizing the randomized measurement scheme based on hybrid quantum circuits, which consist of layers of two-qubit random unitary gates mixed with single-qubit random projective…

Quantum Physics · Physics 2023-09-26 Ahmed A. Akhtar , Hong-Ye Hu , Yi-Zhuang You

We provide a general machine learning methodology that integrates classical shadow representations with unsupervised principal component analysis (PCA) to explore various quantum phase transitions. By sampling spin configurations from…

Quantum Physics · Physics 2026-04-22 Chi-Ting Ho , Daw-Wei Wang

Accurately determining ground-state properties of quantum many-body systems remains one of the major challenges of quantum simulation. In this work, we present a protocol for estimating the ground-state energy using only global time…

Schr\"odinger's equation serves as a fundamental component in characterizing quantum systems, wherein both quantum state tomography and Hamiltonian learning are instrumental in comprehending and interpreting quantum systems. While numerous…

Quantum Physics · Physics 2024-01-25 Zheng An , Jiahui Wu , Muchun Yang , D. L. Zhou , Bei Zeng

In this thesis we present new results relevant to two important problems in quantum information science: the development of a theory of entanglement and the exploration of the use of controlled quantum systems to the simulation of quantum…

Quantum Physics · Physics 2008-10-21 Fernando G. S. L. Brandao

Computing many-body ground state energies and resolving electronic structure calculations are fundamental problems for fields such as quantum chemistry or condensed matter. Several quantum computing algorithms that address these problems…

Quantum Physics · Physics 2023-01-12 Karen J. Morenz Korol , Kenny Choo , Antonio Mezzacapo

The ground state properties of quantum many-body systems are a subject of interest across chemistry, materials science, and physics. Thus, algorithms for finding ground states can have broad impacts. Variational quantum algorithms are one…

Quantum Physics · Physics 2023-09-28 James B. Larsen , Matthew D. Grace , Andrew D. Baczewski , Alicia B. Magann

In quantum many-body systems, measurements can induce qualitative new features, but their simulation is hindered by the exponential complexity involved in sampling the measurement results. We propose to use machine learning to assist the…

Quantum Physics · Physics 2024-12-03 Yuchen Zhu , Molei Tao , Yuebo Jin , Xie Chen

Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages…

Thermal equilibrium states of many-body Hamiltonians are essential for probing quantum chaos, finite-temperature phases of matter, and training quantum machine learning models, yet generating large collections of such states across…

Quantum Physics · Physics 2026-03-20 Jiyu Jiang , Mingrui Jing , Jizhe Lai , Xin Wang , Lei Zhang

The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system.…

Measurement correlations in quantum systems can exhibit non-local behavior, a fundamental aspect of quantum mechanics with applications such as device-independent quantum information processing. However, the explicit construction of local…

Quantum Physics · Physics 2025-04-30 Nick von Selzam , Florian Marquardt

Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always…

Increasingly sophisticated programmable quantum simulators and quantum computers are opening unprecedented opportunities for exploring and exploiting the properties of highly entangled complex quantum systems. The complexity of large…

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