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We present an efficient tensor-network-based approach for simulating large-scale quantum circuits, demonstrated using Quantum Support Vector Machines (QSVMs). Our method effectively reduces exponential runtime growth to near-quadratic…

Quantum layout synthesis (QLS) is a critical step in quantum program compilation for superconducting quantum computers, involving the insertion of SWAP gates to satisfy hardware connectivity constraints. While previous works have introduced…

Quantum Physics · Physics 2025-03-06 Shuohao Ping , Wan-Hsuan Lin , Daniel Bochen Tan , Jason Cong

Developing space- and time-efficient logical magic state preparation protocols will likely be an essential step towards building a large-scale fault-tolerant quantum computer. Motivated by this need, we introduce a scalable method for…

Quantum Physics · Physics 2026-05-26 Samyak Surti , Lucas Daguerre , Isaac H. Kim

Quantum signal processing (QSP) is a methodology for constructing polynomial transformations of a linear operator encoded in a unitary. Applied to an encoding of a state $\rho$, QSP enables the evaluation of nonlinear functions of the form…

Quantum Physics · Physics 2025-08-28 John M. Martyn , Zane M. Rossi , Kevin Z. Cheng , Yuan Liu , Isaac L. Chuang

Quantum state tomography (QST) is an indispensable tool for characterizing many-body quantum systems. However, due to the exponential scaling of the cost of the protocol with system size, many approaches have been developed for quantum…

The AKLT state is the ground state of an isotropic quantum Heisenberg spin-$1$ model. It exhibits an excitation gap and an exponentially decaying correlation function, with fractionalized excitations at its boundaries. So far, the…

Quantum Physics · Physics 2023-10-18 Tianqi Chen , Ruizhe Shen , Ching Hua Lee , Bo Yang

A new method of preparing the pseudo-pure state of a spin system for quantum computation in liquid nuclear magnetic resonance (NMR) was put forward and demonstrated experimentally. Applying appropriately connected line-selective pulses…

Quantum Physics · Physics 2019-08-17 Xinhua Peng , Xiwen Zhu , Ximing Fang , Mang Feng , Keli Gao , Xiaodong Yang , Maili Liu

Accurate state preparation is a critical bottleneck in many quantum algorithms, particularly those for ground state energy estimation. Even in fault-tolerant quantum computing, preparing a quantum state with sufficient overlap to the…

Quantum Physics · Physics 2025-10-07 Gwonhak Lee , Minhyeok Kang , Jungsoo Hong , Stepan Fomichev , Joonsuk Huh

Remote state preparation (RSP) allows one party to remotely prepare a known quantum state on another party's qubit using entanglement. This can be used in quantum networks to perform applications such as blind quantum computing or…

We experimentally demonstrate that loop state-preparation-and-measurement (SPAM) tomography is capable of detecting correlated errors in a two-qubit system. We prepare photon pairs in a state that approximates a Werner state, which may or…

Quantum Physics · Physics 2018-07-11 M. E. Feldman , G. K. Juul , S. J. Van Enk , M. Beck

As one of the most prominent subfields of quantum communication research, remote state preparation (RSP) plays a crucial role in quantum networks. Here we present a deterministic remote state preparation scheme to prepare an arbitrary…

Quantum Physics · Physics 2023-02-21 Deepak Singh , Sanjeev Kumar , Bikash K. Behera

We propose and investigate a new method of quantum process tomography (QPT) which we call projected least squares (PLS). In short, PLS consists of first computing the least-squares estimator of the Choi matrix of an unknown channel, and…

Quantum Physics · Physics 2022-11-02 Trystan Surawy-Stepney , Jonas Kahn , Richard Kueng , Madalin Guta

Quantum state tomography (QST) is a crucial tool for characterizing quantum states. However, QST becomes impractical for reconstructing multi-qubit density matrices since data sets and computational costs grow exponentially with qubit…

Quantum Physics · Physics 2026-04-02 Aniket Patel , Akshay Gaikwad , Tangyou Huang , Anton Frisk Kockum , Tahereh Abad

This paper addresses the challenge of preparing mixed quantum states -- both arbitrary states in general and highly sparse ones in particular -- an area that has received far less attention than the preparation of pure states. We present…

Quantum Physics · Physics 2026-05-19 Bo-Hung Chen , Dah-Wei Chiou , Jie-Hong Roland Jiang

Finding the ground state of complex quantum systems remains a central challenge in many-body physics, quantum chemistry, and combinatorial optimization, due to the exponential growth of the Hilbert-space dimension and the entangled…

Quantum Physics · Physics 2026-04-07 Alireza Ataei , Olle Eriksson , Vahid Azimi Mousolou

Quantum state preparation is a key step in all digital quantum simulation algorithms. Here we propose methods to initialize on a gate-based quantum computer a general class of quantum spin wave functions, the so-called Valence-Bond-Solid…

Quantum Physics · Physics 2023-04-13 Bruno Murta , Pedro M. Q. Cruz , J. Fernández-Rossier

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific…

Quantum Physics · Physics 2023-05-10 Tom Rindell , Berat Yenilen , Niklas Halonen , Arttu Pönni , Ilkka Tittonen , Matti Raasakka

Contemporary quantum computers have relatively high levels of noise, making it difficult to use them to perform useful calculations, even with a large number of qubits. Quantum error correction is expected to eventually enable…

The $n$-qubit $k$-weight Dicke states $|D^n_k\rangle$, defined as the uniform superposition of all computational basis states with exactly $k$ qubits in state $|1\rangle$, form a basis of the symmetric subspace and represent an important…

Quantum Physics · Physics 2025-05-22 Pei Yuan , Shengyu Zhang

Approximate inference techniques are the cornerstone of probabilistic methods based on Gaussian process priors. Despite this, most work approximately optimizes standard divergence measures such as the Kullback-Leibler (KL) divergence, which…

Machine Learning · Computer Science 2020-11-06 Rui Zhang , Christian J. Walder , Edwin V. Bonilla , Marian-Andrei Rizoiu , Lexing Xie
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