Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Shouvanik Chakrabarti"'
Autor:
Enrico Fontana, Dylan Herman, Shouvanik Chakrabarti, Niraj Kumar, Romina Yalovetzky, Jamie Heredge, Shree Hari Sureshbabu, Marco Pistoia
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024)
Abstract Variational quantum algorithms, a popular heuristic for near-term quantum computers, utilize parameterized quantum circuits which naturally express Lie groups. It has been postulated that many properties of variational quantum algorithms can
Externí odkaz:
https://doaj.org/article/17c5158ee3ad48f2b763408f160d3c03
Autor:
Zichang He, Ruslan Shaydulin, Shouvanik Chakrabarti, Dylan Herman, Changhao Li, Yue Sun, Marco Pistoia
Publikováno v:
npj Quantum Information, Vol 9, Iss 1, Pp 1-11 (2023)
Abstract Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as execu
Externí odkaz:
https://doaj.org/article/0b5b1bec92c048c48bc618d261714eb2
Autor:
Dylan Herman, Ruslan Shaydulin, Yue Sun, Shouvanik Chakrabarti, Shaohan Hu, Pierre Minssen, Arthur Rattew, Romina Yalovetzky, Marco Pistoia
Publikováno v:
Communications Physics, Vol 6, Iss 1, Pp 1-17 (2023)
Abstract Constrained optimization problems are ubiquitous in science and industry. Quantum algorithms have shown promise in solving optimization problems, yet none of the current algorithms can effectively handle arbitrary constraints. We introduce a
Externí odkaz:
https://doaj.org/article/5d1a96a0671b42f1bb6b6d80240a850d
Autor:
Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, Marco Pistoia
Publikováno v:
Quantum, Vol 8, p 1231 (2024)
Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challe
Externí odkaz:
https://doaj.org/article/1fa6ad77859848a5b8b7b9f577525d51
Autor:
Ruben S. Andrist, Martin J. A. Schuetz, Pierre Minssen, Romina Yalovetzky, Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar, Grant Salton, Ruslan Shaydulin, Yue Sun, Marco Pistoia, Helmut G. Katzgraber
Publikováno v:
Physical Review Research, Vol 5, Iss 4, p 043277 (2023)
Rydberg atom arrays are among the leading contenders for the demonstration of quantum speedups. Motivated by recent experiments with up to 289 qubits [Ebadi et al., Science 376, 1209 (2022)0036-807510.1126/science.abo6587], we study the maximum-indep
Externí odkaz:
https://doaj.org/article/529bac3458824f419db1407d76013bf2
Autor:
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Publikováno v:
Quantum, Vol 7, p 1191 (2023)
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where deep reinforcement learning offers a powerful framework for real markets.
Externí odkaz:
https://doaj.org/article/0861d733a03b4f2caef97337df93225f
Autor:
Shouvanik Chakrabarti, Rajiv Krishnakumar, Guglielmo Mazzola, Nikitas Stamatopoulos, Stefan Woerner, William J. Zeng
Publikováno v:
Quantum, Vol 5, p 463 (2021)
We give an upper bound on the resources required for valuable quantum advantage in pricing derivatives. To do so, we give the first complete resource estimates for useful quantum derivative pricing, using autocallable and Target Accrual Redemption Fo
Externí odkaz:
https://doaj.org/article/dfb092388b6f47b8994a20234f743159
Publikováno v:
Quantum, Vol 4, p 221 (2020)
While recent work suggests that quantum computers can speed up the solution of semidefinite programs, little is known about the quantum complexity of more general convex optimization. We present a quantum algorithm that can optimize a convex function
Externí odkaz:
https://doaj.org/article/a67b21f6e1b144f399b198c6f1b486a1
Autor:
Dylan Herman, Ruslan Shaydulin, Yue Sun, Shouvanik Chakrabarti, Shaohan Hu, Pierre Minssen, Arthur Rattew, Romina Yalovetzky, Marco Pistoia
Portfolio optimization is an important problem in mathematical finance, and a promising target for quantum optimization algorithms. The use cases solved daily in financial institutions are subject to many constraints that arise from business objectiv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::173847ac192bf73b8620d501b4cfe257
Publikováno v:
2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b50db2695db0191a36f7c1ad9de0c36
https://doi.org/10.1109/iccad51958.2021.9643519
https://doi.org/10.1109/iccad51958.2021.9643519