Business Management Meets Quantum Computing
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 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 Integer Programming
Introductory Quantum Integer Programming for the Operations Research/Operations Management community (Cornell, April 2020).
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.
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.
QCG research takes place in five parallel areas:
- Solving practical problems using novel quantum and quantum-inspired algorithms.
- Developing robust and efficient processes of translating a mathematical algorithm into physical instructions executed by the hardware — known as compilers — for quantum computers.
- Understanding and enhancing quantum speedup: How and why speed is increased, and by how much.
- Understanding quantum queue-channel capacities: How much classical information can be securely and reliably sent over a quantum channel in presence of inevitable buffering?
- 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 cancer genomics.
By creatively advancing methods from geometry of numbers, computational integer programming, and algebraic geometry, QCG research has:
- Solved instances of real-world finance problems in seconds that can take hours by classical best-in-class commercial solvers, by developing hybrid quantum-classical algorithms and testing them on the D-Wave and Toshiba's Simulated Bifurcation Machine (SBM).
Research: Graver Bases Via Quantum Annealing With Application to Non-Linear Integer Programs
- 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.
QCG has also developed a systematic computational approach to prepare a polynomial optimization problem for AQC.
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.
MyAmpleLife Blog: The Next Quantum Revolution
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)!
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.
MyAmpleLife Blog: 2020 Tayur Prize