What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Algebra
- Artificial intelligence
His primary areas of study are Quantum computer, Quantum simulator, Quantum, Quantum machine learning and Quantum algorithm.
His research in Quantum computer intersects with topics in Quantum state, Quantum information and Wave function.
In general Quantum information, his work in D-Wave Two is often linked to Benchmarking linking many areas of study.
Nathan Wiebe interconnects Theoretical physics, Hamiltonian, Statistical physics and Atomic orbital in the investigation of issues within Quantum simulator.
His research in Quantum is mostly focused on Qubit.
Nathan Wiebe focuses mostly in the field of Quantum machine learning, narrowing it down to matters related to Quantum circuit and, in some cases, Electronic circuit.
His most cited work include:
- Quantum machine learning (953 citations)
- Elucidating reaction mechanisms on quantum computers (259 citations)
- Quantum Algorithm for Data Fitting (210 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Quantum, Quantum computer, Quantum algorithm, Hamiltonian and Algorithm.
His work carried out in the field of Quantum brings together such families of science as Statistical physics and Computer engineering.
Nathan Wiebe combines subjects such as Quantum information, Quantum technology and Theoretical computer science with his study of Quantum computer.
His Theoretical computer science research includes themes of Quantum sort and Quantum machine learning.
His study in Quantum algorithm is interdisciplinary in nature, drawing from both Function, Optimization problem and Exponential function.
Nathan Wiebe has researched Hamiltonian in several fields, including Quantum system, Quantum dynamics, Linear combination and Mathematical physics.
He most often published in these fields:
- Quantum (69.46%)
- Quantum computer (51.05%)
- Quantum algorithm (30.54%)
What were the highlights of his more recent work (between 2019-2021)?
- Quantum (69.46%)
- Quantum computer (51.05%)
- Hamiltonian (32.22%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Quantum, Quantum computer, Hamiltonian, Computer engineering and Quantum algorithm.
The concepts of his Quantum study are interwoven with issues in Quadratic equation, Statistical physics, Heuristics, Combinatorial optimization and Speedup.
His work deals with themes such as Algorithm, Adiabatic process and Qubit, which intersect with Quantum computer.
The study incorporates disciplines such as Quantum devices, Inverse problem, Quantum dynamics, Model learning and Eigenvalues and eigenvectors in addition to Hamiltonian.
His research investigates the connection with Computer engineering and areas like Ramsey interferometry which intersect with concerns in Parameterized complexity.
Nathan Wiebe has included themes like Term and Electronic circuit in his Quantum circuit study.
Between 2019 and 2021, his most popular works were:
- Circuit-centric quantum classifiers (166 citations)
- Circuit-centric quantum classifiers (166 citations)
- Circuit-centric quantum classifiers (166 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Algebra
- Artificial intelligence
His main research concerns Quantum, Quantum computer, Quantum circuit, Quantum simulator and Scaling.
His Quantum algorithm study in the realm of Quantum connects with subjects such as Noise.
His Quantum computer study integrates concerns from other disciplines, such as Term, Algorithm, Orders of magnitude and Postselection.
His Quantum circuit research is multidisciplinary, relying on both Electronic circuit, Computer engineering and Quantum machine learning.
His Quantum simulator research is multidisciplinary, incorporating elements of Simple, Product, Algebra and Commutator.
His studies deal with areas such as Quantum dynamics, Hamiltonian, Linear combination and Schrödinger equation as well as Scaling.
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