University of Technology Sydney

Generation of Tunable Three-Photon Entanglement in Cubic Nonlinear Coupled Waveguides

Miguel Bacaoco, Max Galettis, James Huang +2

We theoretically investigate the generation of three-photon states with spatial entanglement in cubic nonlinear coupled waveguides using third-order spontaneous parametric down-con…

quant-ph2024★ 1 cited

Short-time simulation of quantum dynamics by Pauli measurements

Paul K. Faehrmann, Jens Eisert, Maria Kieferova +1

Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here, we propose leveraging the power of measurements to simulate short-time quantum…

Hamiltonian characterisation of multi-time processes with classical memory

Kaumudibikash Goswami, Abhinash Kumar Roy, Varun Srivastava +4

A central problem in open quantum systems is the characterization of non-Markovian processes, where an environment retains the memory of its interaction with the system. A key dist…

quant-ph2024★ 39 cited

Quantum Optics Applications of Hexagonal Boron Nitride Defects

Aslı Çakan, Chanaprom Cholsuk, Angus Gale +6

Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas…

Noise Transfer Approach to GKP Quantum Circuits

Timothy C Ralph, Matthew Winnel, S Nibedita Swain +1

The choice between the Schroedinger and Heisenberg pictures can significantly impact the computational resources needed to solve a problem, even though they are equivalent formulat…

Riemannian-geometric generalizations of quantum fidelities and Bures-Wasserstein distance

A. Afham, Chris Ferrie

We introduce a family of fidelities, termed generalized fidelity, which are based on the Riemannian geometry of the Bures-Wasserstein manifold. We show that this family of fideliti…

Information geometry of bosonic Gaussian thermal states

Zixin Huang, Mark M. Wilde

Bosonic Gaussian thermal states form a fundamental class of states in quantum information science. This paper explores the information geometry of these states, focusing on charact…

Achieving computational gains with quantum error-correction primitives: Generation of long-range entanglement enhanced by error detection

Haoran Liao, Gavin S. Hartnett, Ashish Kakkar +5

The resource overhead required to achieve net computational benefits from quantum error correction (QEC) limits its utility while current systems remain constrained in size, despit…

quant-ph2024★ 25 cited

Deterministic and reconfigurable graph state generation with a single solid-state quantum emitter

H. Huet, P. R. Ramesh, S. C. Wein +11

Measurement-based quantum computing offers a promising route towards scalable, universal photonic quantum computation. This approach relies on the deterministic and efficient gener…

On the Trainability and Classical Simulability of Learning Matrix Product States Variationally

Afrad Basheer, Yuan Feng, Christopher Ferrie +2

We prove that using global observables to train the matrix product state ansatz results in the vanishing of all partial derivatives, also known as barren plateaus, while using loca…

Quantum Register Machine: Efficient Implementation of Quantum Recursive Programs

Zhicheng Zhang, Mingsheng Ying

Quantum recursive programming has been recently introduced for describing sophisticated and complicated quantum algorithms in a compact and elegant way. However, implementation of…

A Graph-Theoretic Framework for Free-Parafermion Solvability

Ryan L. Mann, Samuel J. Elman, David R. Wood +1

We present a graph-theoretic characterisation of when a quantum spin model admits an exact solution via a mapping to free parafermions. Our characterisation is based on the concept…

Tight Quantum Depth Lower Bound for Solving Systems of Linear Equations

Qisheng Wang, Zhicheng Zhang

Since Harrow, Hassidim, and Lloyd (2009) showed that a system of linear equations with $N$ variables and condition number $κ$ can be solved on a quantum computer in $\operatorname{…

Superconducting qubits in the millions: the potential and limitations of modularity

S. N. Saadatmand, Tyler L. Wilson, Mark J. Hodson +11

The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, wh…

quant-ph2024★ 7 cited

Quantum algorithms for matrix geometric means

Nana Liu, Qisheng Wang, Mark M. Wilde +1

Matrix geometric means between two positive definite matrices can be defined from distinct perspectives - as solutions to certain nonlinear systems of equations, as points along ge…

Demonstration of Lossy Linear Transformations and Two-Photon Interference on a Photonic Chip

Kai Wang, Simon J. U. White, Alexander Szameit +2

Studying quantum correlations in the presence of loss is of critical importance for the physical modeling of real quantum systems. Here, we demonstrate the control of spatial corre…

Local Test for Unitarily Invariant Properties of Bipartite Quantum States

Kean Chen, Qisheng Wang, Zhicheng Zhang

We study the power of local test for bipartite quantum states. Our central result is that, for properties of bipartite pure states, unitary invariance on one part implies an \texti…

quant-ph2024★ 1 cited

Optimal Scheduling of Graph States via Path Decompositions

Samuel J. Elman, Jason Gavriel, Ryan L. Mann

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graph…

quant-ph2024★ 13 cited

Exact quantum sensing limits for bosonic dephasing channels

Zixin Huang, Ludovico Lami, Mark M. Wilde

Dephasing is a prominent noise mechanism that afflicts quantum information carriers, and it is one of the main challenges towards realizing useful quantum computation, communicatio…

quant-ph2024★ 16 cited

A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE)

Nishikanta Mohanty, Bikash K. Behera, Christopher Ferrie +1

The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets.…