What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Algorithm
Rupak Biswas focuses on Quantum, Grid, Quantum computer, Distributed computing and Supercomputer.
His Quantum study incorporates themes from Embedding, Deep learning, Artificial intelligence and Speedup.
His Grid study integrates concerns from other disciplines, such as Algorithm, Load balancing, Polygon mesh and Computational science.
The concepts of his Quantum computer study are interwoven with issues in Optimization problem, Quantum algorithm, Computer engineering and Qubit.
His research in Distributed computing intersects with topics in Job scheduler, Scheduling and Grid computing.
His Supercomputer research is multidisciplinary, relying on both Virtualization, Computation and Benchmark.
His most cited work include:
- Quantum supremacy using a programmable superconducting processor (1598 citations)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor" (967 citations)
- Parallel, adaptive finite element methods for conservation laws (344 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Parallel computing, Distributed computing, Grid, Load balancing and Computational fluid dynamics.
His work deals with themes such as Polygon mesh and Programming paradigm, which intersect with Parallel computing.
Many of his research projects under Distributed computing are closely connected to Resource allocation with Resource allocation, tying the diverse disciplines of science together.
His Grid research also works with subjects such as
- Computational science, which have a strong connection to Supercomputer,
- Aerodynamics which connect with Noise and Flow.
His work carried out in the field of Computational problem brings together such families of science as Virtual machine and Quantum computer, Quantum.
His study explores the link between Quantum and topics such as Algorithm that cross with problems in Set.
He most often published in these fields:
- Parallel computing (40.23%)
- Distributed computing (24.71%)
- Grid (20.69%)
What were the highlights of his more recent work (between 2012-2020)?
- Quantum (13.79%)
- Quantum computer (11.49%)
- Theoretical computer science (6.90%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Quantum, Quantum computer, Theoretical computer science, Benchmark and Speedup.
His Quantum study combines topics from a wide range of disciplines, such as Algorithm, Deep learning, Artificial intelligence and Computer engineering.
His Quantum computer research incorporates elements of Qubit, Supercomputer, Quantum algorithm and Computational problem.
His Supercomputer course of study focuses on Computational science and Quantum machine learning.
Rupak Biswas interconnects Scalability, Distributed memory, Programming paradigm and Conjugate gradient method in the investigation of issues within Theoretical computer science.
His Speedup research is included under the broader classification of Parallel computing.
Between 2012 and 2020, his most popular works were:
- Quantum supremacy using a programmable superconducting processor (1598 citations)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor" (967 citations)
- From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz (149 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Algorithm
His main research concerns Quantum, Quantum computer, Qubit, Quantum algorithm and Computer engineering.
His Quantum research incorporates themes from Embedding and Speedup.
He has included themes like Supercomputer, Hamiltonian and Operator in his Quantum computer study.
His Supercomputer research is multidisciplinary, incorporating elements of Quantum state, Computational science and Quantum machine learning.
The Qubit study combines topics in areas such as Simulated annealing and Machine learning.
His study in Quantum algorithm is interdisciplinary in nature, drawing from both Optimization problem, Ansatz, Algebra and Constrained optimization.
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