Military Embedded Systems

DARPA to leverage quantum devices to help solve complex optimization problems


March 04, 2019

Mariana Iriarte

Technology Editor

Military Embedded Systems

DARPA to leverage quantum devices to help solve complex optimization problems
DARPA photo

ARLINGTON, Va. The Defense Advanced Research Projects Agency (DARPA) is launching the Optimization with Noisy Intermediate-Scale Quantum devices (ONISQ) program to exploit quantum information processing before fully fault-tolerant quantum computers exist.

The goal of the program aims to pursue a hybrid concept that combines intermediate-sized quantum devices with classical systems to solve a particular set of problems known as combinatorial optimization. ONISQ seeks to demonstrate the quantitative advantage of quantum information processing by leapfrogging the performance of classical-only systems in solving optimization challenges.

“A number of current quantum devices with more than 50 qubits exist, and devices with greater than 100 qubits are anticipated soon,” explains Tatjana Curcic, program manager in DARPA’s Defense Sciences Office. “Qubits’ short lifetime and noise in the system limit how many operations you can do efficiently, but a new quantum optimization algorithm has opened the door for a hybrid quantum/classical approach that could outperform classical systems.”

Solving combinatorial optimization problems – with their mindboggling number of potential combinations – is of significant interest to the military. One potential application is enhancing the military’s complex worldwide logistics system, which includes scheduling, routing, and supply chain management in austere locations that lack the infrastructure on which commercial logistics companies depend. ONISQ solutions could also impact machine-learning, coding theory, electronic fabrication, and protein-folding.

ONISQ researchers will be tasked with developing quantum systems that are scalable to hundreds or thousands of qubits with longer coherence times and improved noise control. Researchers will also be required to efficiently implement a quantum optimization algorithm on noisy intermediate-scale quantum devices, optimizing allocation of quantum and classical resources. Benchmarking will also be part of the program, with researchers making a quantitative comparison of classical and quantum approaches. In addition, the program will identify classes of problems in combinatorial optimization where quantum information processing is likely to have the biggest impact.

“If we’re successful, the outcome of ONISQ will be the first demonstration of a quantum speedup compared to the best classical method for a useful problem,” Curcic says.

A Proposers Day for interested proposers is scheduled for March 19, 2019, at the Executive Conference Center in Arlington, Virginia:


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