4.7k citations · 9.3k across the 70 of their papers we have counts for
5 papers · 2 filters
Tensor networks in machine learning
Richik Sengupta, Soumik Adhikary, Ivan Oseledets +1
A tensor network is a type of decomposition used to express and approximate large arrays of data. A given data-set, quantum state or higher dimensional multi-linear map is factored…
Ion native variational ansatz for quantum approximate optimization
Daniil Rabinovich, Soumik Adhikary, Ernesto Campos +6
Variational quantum algorithms involve training parameterized quantum circuits using a classical co-processor. An important variational algorithm, designed for combinatorial optimi…
Quantum-machine-learning channel discrimination
Andrey Kardashin, Anna Vlasova, Anastasiia Pervishko +2
In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measur…
On Circuit Depth Scaling For Quantum Approximate Optimization
V. Akshay, H. Philathong, E. Campos +4
Variational quantum algorithms are the centerpiece of modern quantum programming. These algorithms involve training parameterized quantum circuits using a classical co-processor, a…
Experimental quantum adversarial learning with programmable superconducting qubits
Wenhui Ren, Weikang Li, Shibo Xu +21
Quantum computing promises to enhance machine learning and artificial intelligence. Different quantum algorithms have been proposed to improve a wide spectrum of machine learning t…