Jane Doe
Graduate Research Student
Exploring the frontiers of quantum computing and machine learning at Stanford University.
Exploring the frontiers of quantum computing and machine learning at Stanford University.
I'm a Ph.D. candidate in Computer Science at Stanford University, specializing in quantum computing and its applications in machine learning. My research focuses on developing novel quantum algorithms that can outperform classical approaches for optimization problems.
Before joining Stanford, I completed my Master's in Physics at MIT, where I worked on quantum error correction techniques. My undergraduate studies were at UC Berkeley, where I double-majored in Computer Science and Physics.
Ph.D. in Computer Science (2020-Present)
Advisor: Prof. John Smith
M.Sc. in Physics (2018-2020)
Thesis: "Quantum Error Correction in Noisy Intermediate-Scale Quantum Devices"
B.Sc. in Computer Science & Physics (2014-2018)
Summa Cum Laude, Phi Beta Kappa
Developing hybrid quantum-classical algorithms for machine learning tasks that show potential quantum advantage. Our approach combines parameterized quantum circuits with classical neural networks.
Investigating quantum algorithms for combinatorial optimization problems with applications in logistics and finance. Our work focuses on reducing the quantum resources required for practical implementations.
Developing novel error mitigation techniques for noisy intermediate-scale quantum (NISQ) devices. Our methods significantly improve the reliability of quantum computations without requiring full error correction.
Exploring protocols for quantum communication and distributed quantum computing. Our research includes developing efficient entanglement distribution schemes and quantum network coding.
J. Doe, M. Smith, A. Johnson
We present a novel hybrid quantum-classical algorithm that demonstrates a polynomial speedup for certain optimization problems compared to purely classical approaches. The method combines variational quantum circuits with classical neural networks.
J. Doe, R. Brown, L. Chen
We introduce a family of error mitigation protocols that significantly improve the reliability of quantum computations on noisy devices. Our techniques require minimal overhead and are compatible with existing quantum hardware.
J. Doe, T. Wilson, K. Lee
This work demonstrates practical quantum algorithms for financial portfolio optimization problems. We show that even with noisy quantum processors, our methods can provide better solutions than classical approaches for certain problem instances.
J. Doe, A. Garcia, P. Kumar
We develop efficient protocols for entanglement distribution in quantum networks with limited resources. Our approach significantly reduces the time required to establish entangled links between distant nodes.
I'm always interested in discussing research collaborations, speaking engagements, or answering questions about my work. Feel free to reach out!
Gates Computer Science Building
Room 342, Stanford University
