Quantum Computing

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Quantum Computing and Optimization Lab (QCOL) to join I-DISC as a new Research Group.

Quantum Computing has the potential to solve problems faster than classical supercomputers. This capability can change our world by enabling new methods to decrypt data, faster molecular analysis to design better drug treatments and to solve complex optimization problems rapidly.

The Quantum Computing Research Group is formed by the Quantum Computing and Optimization Lab (QCOL), established in 2019 by the Industrial and System Engineering Department of Lehigh University. Its aim was to accelerate the development of quantum computing optimization methodology. Mathematical optimization has critical applications in many disciplines, including physics, biology, engineering, economics, business, and finance. Quantum speed-up has the potential to lead to transformative, revolutionary advances and fundamentally impact society.
 

Research Areas

  • Quantum Interior Point Methods for solving Linear Programming, Semi-definite Programming, and Second-order Cone Optimization problems
  • Quantum methods to solve Combinatorial optimization problem such as Stable Set Problem in Graph theory
  • Quantum Errors and Their Models

The QCOL’s research focus is on developing novel QC methodologies to solve complex optimization problems. 
Current research topics include:

 

Quantum Interior Point Methods

Making use of Quantum Linear Solvers and Block Encodings to achieve a quantum speedup over classical runtimes by solving the Newton system more efficiently.

 

Noise in NISQ Devices

Building up a description of physical errors in quantum computers from both individual gate and integrated quantum circuit aspects. 

 

Solving Combinatorial Problems in NISQ devices

Development and study of implementable QUBO formulations of combinatorial problems using QAOA algorithms. 

Quantum Computing and Optimization Lab (QCOL)  is supported by a recently awarded $2,128,658 research grant from the Defense Advanced Research Projects Agency (DARPA).
Read the article about this award. 
 

Research Group Members

This lab is led by four faculty members:
The Ph.D. students working in this lab
Collaborating partners