Quantum imaginary time evolution steered by reinforcement learning
The quantum imaginary time evolution is a powerful algorithm for preparing the ground and
thermal states on near-term quantum devices. However, algorithmic errors induced by …
thermal states on near-term quantum devices. However, algorithmic errors induced by …
Measurement-based deterministic imaginary time evolution
Y Mao, M Chaudhary, M Kondappan, J Shi… - Physical Review Letters, 2023 - APS
We introduce a method to perform imaginary time evolution in a controllable quantum
system using measurements and conditional unitary operations. By performing a sequence …
system using measurements and conditional unitary operations. By performing a sequence …
Quantum approximate optimization algorithm with adaptive bias fields
The quantum approximate optimization algorithm (QAOA) transforms a simple many-qubit
wave function into one that encodes a solution to a difficult classical optimization problem. It …
wave function into one that encodes a solution to a difficult classical optimization problem. It …
SpinQ Gemini: a desktop quantum computing platform for education and research
SpinQ Gemini is a commercial desktop quantum computing platform designed and
manufactured by SpinQ Technology. It is an integrated hardware-software system. The first …
manufactured by SpinQ Technology. It is an integrated hardware-software system. The first …
Simulating noisy variational quantum eigensolver with local noise models
The variational quantum eigensolver (VQE) is a promising algorithm to demonstrate
quantum advantage on near‐term noisy‐intermediate‐scale quantum (NISQ) computers …
quantum advantage on near‐term noisy‐intermediate‐scale quantum (NISQ) computers …
Mitigating algorithmic errors in quantum optimization through energy extrapolation
Quantum optimization algorithms offer a promising route to finding the ground states of
target Hamiltonians on near-term quantum devices. Nonetheless, it remains necessary to …
target Hamiltonians on near-term quantum devices. Nonetheless, it remains necessary to …
Enhancing variational quantum state diagonalization using reinforcement learning techniques
A Kundu, P Bedełek, M Ostaszewski… - New Journal of …, 2024 - iopscience.iop.org
The variational quantum algorithms are crucial for the application of NISQ computers. Such
algorithms require short quantum circuits, which are more amenable to implementation on …
algorithms require short quantum circuits, which are more amenable to implementation on …
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms
A Kundu - arXiv preprint arXiv:2402.13754, 2024 - arxiv.org
A significant hurdle in the noisy intermediate-scale quantum (NISQ) era is identifying
functional quantum circuits. These circuits must also adhere to the constraints imposed by …
functional quantum circuits. These circuits must also adhere to the constraints imposed by …
Predicting properties of quantum systems by regression on a quantum computer
A Kardashin, Y Balkybek, K Antipin… - arXiv preprint arXiv …, 2024 - arxiv.org
Quantum computers can be considered as a natural means for performing machine learning
tasks for labeled data which are inherently quantum. Many quantum machine learning …
tasks for labeled data which are inherently quantum. Many quantum machine learning …
Double-bracket quantum algorithms for diagonalization
M Gluza - Quantum, 2024 - quantum-journal.org
This work proposes double-bracket iterations as a framework for obtaining diagonalizing
quantum circuits. Their implementation on a quantum computer consists of interlacing …
quantum circuits. Their implementation on a quantum computer consists of interlacing …