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Due to the limited qubit number of quantum devices, distributed quantum computing is considered a promising pathway to overcome this …

Quantum neural networks combine quantum computing with advanced data-driven methods, offering promising applications in quantum machine …

Imaginary-time evolution is fundamental to analyzing quantum many-body systems, yet classical simulation requires exponentially growing …

Unknown unitary inversion is a fundamental primitive in quantum computing and physics. Although recent work has demonstrated that …

Quantum neural networks have emerged as promising quantum machine learning models, leveraging the properties of quantum systems and …

Quantum hardware development is progressing rapidly with substantial advancements achieved across leading platforms, including …

Quantum state purification, a process that aims to recover a state closer to a system’s principal eigenstate from multiple copies …

Identifying symmetries in quantum dynamics, such as identity or time-reversal invariance, is a crucial challenge with profound …

Efficient estimation of nonlinear functions of quantum states is crucial for various key tasks in quantum computing, such as …

The conflict between trainability and expressibility is a key challenge in variational quantum computing and quantum machine learning. …