Adaptive quantum variational algorithms are particularly promising for simulating strongly correlated systems on near-term quantum hardware, but they are not yet viable due, in large part, to the severe coherence time limitations on current devices. In this work, we introduce an algorithm called TETRIS-ADAPT-VQE, which iteratively builds up variational ans\"atze a few operators at a time in a way dictated by the problem being simulated. This algorithm is a modified version of the ADAPT-VQE algorithm in which the one-operator-at-a-time rule is lifted to allow for the addition of multiple operators with disjoint supports in each iteration. TETRIS-ADAPT-VQE results in denser but significantly shallower circuits, without increasing the number o...
We proposed a general quantum-computing-based algorithm that harnesses the exponential power of nois...
Quantum variational circuits have gained significant attention due to their applications in the quan...
The quantum approximate optimisation algorithm was proposed as a heuristic method for solving combin...
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy inter...
Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near...
Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstra...
In order to answer the problem of Quantum Phase Estimation Algorithm been not suitable for NISQ devi...
The variational quantum eigensolver (VQE) is a promising algorithm for demonstrating quantum advanta...
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast prob...
For a large number of tasks, quantum computing demonstrates the potential for exponential accelerati...
Variational methods offer a highly promising route to exploiting quantum computers for chemistry tas...
We perform a systematic study of preparing ground states of correlated multi-orbital impurity models...
The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems ...
Variational quantum algorithms involve training parameterized quantum circuits using a classical co-...
Many classical optimization problems can be mapped to finding the ground states of diagonal Ising Ha...
We proposed a general quantum-computing-based algorithm that harnesses the exponential power of nois...
Quantum variational circuits have gained significant attention due to their applications in the quan...
The quantum approximate optimisation algorithm was proposed as a heuristic method for solving combin...
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy inter...
Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near...
Variational Quantum Algorithms (VQA) have been identified as a promising candidate for the demonstra...
In order to answer the problem of Quantum Phase Estimation Algorithm been not suitable for NISQ devi...
The variational quantum eigensolver (VQE) is a promising algorithm for demonstrating quantum advanta...
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast prob...
For a large number of tasks, quantum computing demonstrates the potential for exponential accelerati...
Variational methods offer a highly promising route to exploiting quantum computers for chemistry tas...
We perform a systematic study of preparing ground states of correlated multi-orbital impurity models...
The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems ...
Variational quantum algorithms involve training parameterized quantum circuits using a classical co-...
Many classical optimization problems can be mapped to finding the ground states of diagonal Ising Ha...
We proposed a general quantum-computing-based algorithm that harnesses the exponential power of nois...
Quantum variational circuits have gained significant attention due to their applications in the quan...
The quantum approximate optimisation algorithm was proposed as a heuristic method for solving combin...