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Business Management Meets Quantum Computing

Motivated by operations research and operations management applications, the Quantum Computing Group at the Tepper School aims to turn quantum computing as a service into reality.


Quantum and quantum-inspired algorithms offer dramatically new possibilities to tackle practical problems previously considered intractable. Right now.

Sridhar R. Tayur, Ford Distinguished Research Chair and University Professor of Operations Management, leads the Quantum Computing Group at the Tepper School.

Sridhar Tayur on Quantum Computing

Moonshot: Quantum Computing

A brief history of quantum physics and its application to quantum computing.

Quantum Computing and Integer Optimization: An Overview

An introductory lecture on the use of quantum computing in Non-Linear Integer Optimization.

Quantum Computing Areas of Research

The research of the Quantum Computing Group (QCG) at the Tepper School focuses on the creation of radically different types of algorithms to optimize complex large-scale industrial problems startlingly faster, with the ultimate desired outcome of commercialized algorithms that are easily accessible for practical application.

QCG research takes place in three parallel areas:

  1. Solving practical problems using novel quantum and quantum-inspired algorithms.
  2. Developing robust and efficient processes of translating a mathematical algorithm into physical instructions executed by the hardware — known as compilers — for quantum computers.
  3. Understanding and enhancing quantum speedup: how and why speed is increased, and by how much.

Solving Practical Problems

Our Quantum and Quantum-inspired (classical) algorithms are novel approaches to tackle complex models that arise in areas such as finance, supply chain management, and cancer genomics.

By creatively advancing methods from geometry of numbers, computational integer programming, and algebraic geometry, QCG research has:


  • Developed the Graver-Augmented Multi-Seed algorithm (GAMA), a Quantum-inspired classical algorithm that is two (and three) orders of magnitude faster than commercial best-in-class solvers. GAMA has been applied to solve problems in supply chain management involving integrated production, inventory, and logistics. These work on standard computer hardware and do not require access to digital annealers or quantum hardware.

    Research: GAMA: A Novel Algorithm for Non-Convex Integer Programs

Compiling on Quantum Computers

To solve practical problems on a real quantum computer, we must translate the real-world problem into something that can be understood by the physical hardware — a process known as compiling.

There are two dominant computational models for quantum computing:

  • Circuit (Gate) models, with hardware from Google, IBM, and Rigetti.
  • Adiabatic Quantum Computing (AQC) with hardware from D-Wave.

QCG has developed two novel algorithms for compiling quantum circuits.

ResearchKnuth-Bendix Completion Algorithm and Shuffle Algebras for Compiling NISQ Circuits

QCG has also developed a systematic computational approach to prepare a polynomial optimization problem for AQC.

Research: A Novel Algebraic Geometry Compiling Framework for Adiabatic Quantum Computations

Current QCG research on compiling enhances methods for Gate/circuit chips to account directly for the noise, incorporating models into our algorithms directly and adapts computational methods from Mixed-Integer Linear Programming to create open-source compilers for AQC. 

Understanding Quantum Speedup

Where does quantum speedup really come from? How can we enhance the speedup of quantum (and hybrid) algorithms? This is an exciting and deep area of research.

QCG research has helped provide algorithmic guidelines that enable further speedup in AQC.

Research: Enhancing the Efficiency of Adiabatic Quantum Computations

Research: Homological Description of the Quantum Adiabatic Evolution With a View Toward Quantum Computations

MyAmpleLife Blog: The Next Quantum Revolution