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Tridiagonal matrix decomposition for Hamiltonian simulation on a quantum computer
Boris Arseniev, Dmitry Guskov, Richik Sengupta +2
The construction of quantum circuits to simulate Hamiltonian evolution is central to many quantum algorithms. State-of-the-art circuits are based on oracles whose implementation is…
Mitigating Quantum Gate Errors for Variational Eigensolvers Using Hardware-Inspired Zero-Noise Extrapolation
Alexey Uvarov, Daniil Rabinovich, Olga Lakhmanskaya +3
Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. Practical implementations of these algorithms, despite offering certain le…
On Translation-Invariant Matrix Product States and advances in MPS representations of the -state
Petr Klimov, Richik Sengupta, Jacob Biamonte
This work is devoted to the study Translation-Invariant (TI) Matrix Product State (MPS) representations of quantum states with periodic boundary conditions (PBC). We pursue two dir…
The ZX-Calculus is Canonical in the Heisenberg Picture for Stabilizer Quantum Mechanics
J Biamonte, A Nasrallah
In 2008 Coecke and Duncan proposed the graphical ZX-calculus rewrite system which came to formalize reasoning with quantum circuits, measurements and quantum states. The ZX-calculu…
Robustness of Variational Quantum Algorithms against stochastic parameter perturbation
Daniil Rabinovich, Ernesto Campos, Soumik Adhikary +3
Variational quantum algorithms are tailored to perform within the constraints of current quantum devices, yet they are limited by performance-degrading errors. In this study, we co…
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…