Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Zhu, Chenghong"'
Symmetry plays a crucial role in quantum physics, dictating the behavior and dynamics of physical systems. In this paper, We develop a hypothesis-testing framework for quantum dynamics symmetry using a limited number of queries to the unknown unitary
Externí odkaz:
http://arxiv.org/abs/2411.14292
Establishing a fully functional quantum internet relies on the efficient allocation of multipartite entangled states, which enables advanced quantum communication protocols, secure multipartite quantum key distribution, and distributed quantum comput
Externí odkaz:
http://arxiv.org/abs/2409.08173
The conflict between trainability and expressibility is a key challenge in variational quantum computing and quantum machine learning. Resolving this conflict necessitates designing specific quantum neural networks (QNN) tailored for specific problem
Externí odkaz:
http://arxiv.org/abs/2406.11805
Magic states are essential for achieving universal quantum computation. This study introduces a reversible framework for the manipulation of magic states in odd dimensions, delineating a necessary and sufficient condition for the exact transformation
Externí odkaz:
http://arxiv.org/abs/2405.17356
The unique features of entanglement and non-locality in quantum systems, where there are pairs of bipartite states perfectly distinguishable by general entangled measurements yet indistinguishable by local operations and classical communication, hold
Externí odkaz:
http://arxiv.org/abs/2402.18446
We introduce a reversible theory of exact entanglement manipulation by establishing a necessary and sufficient condition for state transfer under trace-preserving transformations that completely preserve the positivity of partial transpose (PPT). Und
Externí odkaz:
http://arxiv.org/abs/2312.04456
Publikováno v:
Phys. Rev. Lett. 133, 010202 (2024)
In the realm of fault-tolerant quantum computing, stabilizer operations play a pivotal role, characterized by their remarkable efficiency in classical simulation. This efficiency sets them apart from non-stabilizer operations within the quantum compu
Externí odkaz:
http://arxiv.org/abs/2310.11323
Quantum neural networks (QNNs) have been a promising framework in pursuing near-term quantum advantage in various fields, where many applications can be viewed as learning a quantum state that encodes useful data. As a quantum analog of probability d
Externí odkaz:
http://arxiv.org/abs/2309.14980
The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum chemistry. However, quantum sys
Externí odkaz:
http://arxiv.org/abs/2305.04148
Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground state preparation scheme with shallow variational warm-start to tackle
Externí odkaz:
http://arxiv.org/abs/2303.11204