The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-classical computing frameworks and programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qu...
A key open question in quantum computing is whether quantum algorithms can potentially offer a signi...
The simulation of quantum spin chains is a promising candidate for the demonstration of quantum adva...
2 pags.Tensor networks are mathematical structures that efficiently compress the data required to de...
In recent years lots of efforts have been spent in the realization of quantum computers able to repr...
Funding Information: The work of Ar A M, A A T, F N, and M R P was supported by Terra Quantum A G. T...
Tensor network theory and quantum simulation are, respectively, the key classical and quantum comput...
Classical simulation of quantum computation is necessary for studying the numerical behavior of quan...
Hybrid quantum-classical workflows have become standard methods for executing variational algorithms...
Machine learning is a promising application of quantum computing, but challenges remain for implemen...
Machine learning is a promising application of quantum computing, but challenges remain for implemen...
Resource usage of traditional quantum computing simulation techniques scale exponentially fast eith...
Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Físi...
Once developed for quantum theory, tensor networks have been established as a successful machine lea...
In recent times, Variational Quantum Circuits (VQC) have been widely adopted to different tasks in m...
Great advances have been made to quantum computing in recent years. However, an issue keenly felt by...
A key open question in quantum computing is whether quantum algorithms can potentially offer a signi...
The simulation of quantum spin chains is a promising candidate for the demonstration of quantum adva...
2 pags.Tensor networks are mathematical structures that efficiently compress the data required to de...
In recent years lots of efforts have been spent in the realization of quantum computers able to repr...
Funding Information: The work of Ar A M, A A T, F N, and M R P was supported by Terra Quantum A G. T...
Tensor network theory and quantum simulation are, respectively, the key classical and quantum comput...
Classical simulation of quantum computation is necessary for studying the numerical behavior of quan...
Hybrid quantum-classical workflows have become standard methods for executing variational algorithms...
Machine learning is a promising application of quantum computing, but challenges remain for implemen...
Machine learning is a promising application of quantum computing, but challenges remain for implemen...
Resource usage of traditional quantum computing simulation techniques scale exponentially fast eith...
Màster Oficial de Ciència i Tecnologia Quàntiques / Quantum Science and Technology, Facultat de Físi...
Once developed for quantum theory, tensor networks have been established as a successful machine lea...
In recent times, Variational Quantum Circuits (VQC) have been widely adopted to different tasks in m...
Great advances have been made to quantum computing in recent years. However, an issue keenly felt by...
A key open question in quantum computing is whether quantum algorithms can potentially offer a signi...
The simulation of quantum spin chains is a promising candidate for the demonstration of quantum adva...
2 pags.Tensor networks are mathematical structures that efficiently compress the data required to de...