10 citations · 23 across the 8 of their papers we have counts for
9 papers
Entanglement of superpositions in zero-energy subspace: insights from stabilizer theory
Konstantin Antipin, Richik Sengupta
Exact zero-energy eigenstates built from entangled dimers have recently been found in a large class of spin Hamiltonians. While the individual states have a simple structure, their…
On the submatrices with the best-bounded inverses
Richik Sengupta, Mikhail Pautov
The following hypothesis was formulated by Goreinov, Tyrtyshnikov, and Zamarashkin in \cite{goreinov1997theory}. If is real matrix with the orthonormal columns $(n>…
High order schemes for solving partial differential equations on a quantum computer
Boris Arseniev, Dmitry Guskov, Richik Sengupta +1
We explore the utilization of higher-order discretization techniques in optimizing the gate count needed for quantum computer based solutions of partial differential equations. To…
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…
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…
On Symmetric Pseudo-Boolean Functions: Factorization, Kernels and Applications
Richik Sengupta, Jacob Biamonte
A symmetric pseudo-Boolean function is a map from Boolean tuples to real numbers which is invariant under input variable interchange. We prove that any such function can be equival…
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…
Progress towards analytically optimal angles in quantum approximate optimisation
D. Rabinovich, R. Sengupta, E. Campos +2
The Quantum Approximate Optimisation Algorithm is a layer, time-variable split operator method executed on a quantum processor and driven to convergence by classical outer loop…