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Business Management Meets Quantum Technologies

Motivated by supply chain, finance, technology, and health care applications, the Quantum Technologies Group at the Tepper School aims to turn quantum computing as a service into industrial reality, help design practical quantum communication networks, and develop quantum-inspired hardware.

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 Technologies 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 Integer Programming

Introductory Quantum Integer Programming for the Operations Research/Operations Management community (Cornell, April 2020). 

Five Starter Pieces: Quantum Information Science Via Semi-definite Program

A brief introduction to five fundamental problems in Quantum Information Science that are especially well-suited to be formulated as Semi-definite Programs. (INFORMS, November 2022). 

Quantum Areas of Research

The research of the Quantum Technologies Group (QTG) 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.

We are also exploring unconventional hardware, studying practical issues in quantum communications, and analyzing other quantum technologies (such as sensing) by framing fundamental problems in quantum information science as semidefinite programs.

QTG research takes place in five 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.
  4. Understanding quantum queue-channel capacities: How much classical information can be securely and reliably sent over a quantum channel in presence of inevitable buffering?
  5. Hardware: How well can Photonic Ising Machines (PIM) solve benchmark combinatorial problems such as Max-Cut and Number Partitioning Problem?

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 health care.

By creatively advancing methods from geometry of numbers, computational integer programming, and algebraic geometry, QTG 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.

Discovery of Altered Cancer Pathways

Current research is testing hybrid quantum-classical and Graver-Augmented Multi-Seed algorithm in the area of cancer genomics, to identify altered driver pathways in Gliobalstoma Multiforme and Acute MyeLoid Leukemia, using data from The Cancer Genome Atlas.

Quantum Machine Learning for Image Classification

Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a quantum machine learning tool that can accurately classify images as belonging to normal or infected individuals.


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.

QTG has developed two novel algorithms for compiling quantum circuits.

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

QTG 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 QTG 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. 

ResearchInteger Programming Techniques for Minor-Embedding in Quantum Annealers 

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.

QTG 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 


Quantum Communications

Quantum queue-channels arise naturally in the context of buffering in quantum networks. We study the important practical case of symmetric Generalized Amplitude Damping, and extend our results to Unital qubit queue-channels: We show that the maximum classical capacity can be achieved without entanglement (in encoding and decoding)!

Research: Queue-Channel Capacities With Generalized Amplitude Damping 

Research: Unital Qubit Queue-channels: Classical Capacity and Product Decoding

My Ample Life Blog: Buffering of Flying Qubits


Photonic Ising Machines (PIMs) offer alternatives to quantum annealing and simulated annealing. An NP-hard problem is cast as a quadratic unconstrained binary optimization (QUBO) where the final spin configuration in the Ising model is adiabatically arrived at as a solution to a Hamiltonian, given a known set of interactions between spins.

The temporal multiplexed Ising machine uses the bistable response of an electro-optic modulator to mimic the spin up and down states and solves the Max-Cut problem on par with Gurobi for up to 1000 spins.

The spatial photonic Ising machine easily partitions an array of 2^14 integers, vastly outperforming both Gurobi and the state-of-the-art D-Wave annealer.

Research: Optimization With Photonic Wave-Based Annealers

MyAmpleLife Blog: 2020 Tayur Prize