Ph.D. student Brian Doolittle develops quantum software suite

10/11/2021 Jenny Applequist, Coordinated Science Lab

CSL PhD student Brian Doolittle has developed a collection of quantum software packages that help identify and design nonclassical behaviors in quantum systems.

Written by Jenny Applequist, Coordinated Science Lab

Quantum computing with real-world practical applications—will it happen, or remain a tempting mirage, just out of reach? One thing is clear: for quantum computing to have a shot at fulfilling its promise, a lot of work needs to be done. CSL Ph.D. student Brian Doolittle has been working to overcome several significant problems that have arisen in quantum research, including ones related to verification that quantum phenomena are truly occurring inside quantum information systems.

Brian Dooolittle
Brian Doolittle, PhD student

In Doolittle’s work, he considers bits and qubits (quantum bits) as distinct computational resources and is trying to understand the trade-offs between them at a fundamental level. To help reveal the advantages of quantum resources, he is developing software tools that help identify and design nonclassical behaviors in quantum systems.

Notably, he has developed a collection of software packages that are publicly accessible at https://github.com/ChitambarLab, the Github site of his advisor, Electrical & Computer Engineering associate professor Eric Chitambar.

“These packages are transparent in having an open-source codebase with comprehensive documentation,” says Doolittle. “[They are] made reproducible through cross-platform testing, dependency tracking, and semantic versioning.” Two notable examples include SignalingDimension.jl and QBase.jl, both of which he wrote in the Julia programming language. The primary goal of SignalingDimension.jl is to identify lower and upper bounds on the “signaling dimension” of a quantum channel, a quantity that describes how much classical communication is needed to simulate the quantum channel. Channels with high signaling dimension require more classical resources to simulate and therefore can be more powerful for quantum information processing. QBase.jl is a library of quantum information that can be used as a framework for representing quantum systems and their dynamics.

Eric Chitambar
Eric Chitambar, Associate Professor of Electrical and Computer Engineering

Prof. Chitambar explains that one of the broader questions his group addresses is that of verifying quantum phenomena based on classical data. “At a high level, we have some instructions that we give our equipment... and then we get some classical data out... So we want to look at the read-in and read-out data, and somehow infer that there is some quantum process going on in between.” Part of Doolittle’s work, including SignalingDimension.jl, is contributing to that effort. Ultimately, the group’s verification work will also be applied to UIUC’s IQUIST quantum network, now under development.

Chitambar says, “We can verify that actually, underneath the hood, when we turn on these two [quantum network nodes] in different laboratories and they start communicating with one another, that they’re actually sending quantum information back and forth.”

In addition to the software development, Doolittle, who worked as a software engineer in industry before coming to UIUC for grad school, has been pursuing some other research threads. In particular, he recently received funding from the Quantum Information Science and Engineering Network (QISE-NET) to collaborate with an industry partner, Xanadu.

“In this project, I’ll use PennyLane, Xanadu’s platform for hybrid, quantum-classical computing, to perform variational quantum optimization in quantum networks,” he says. “Variational quantum optimization is a simple idea where classical machine learning is used to tune a quantum algorithm to perform a particular task. In our case, we use a quantum computer to simulate a quantum network and use gradient descent to maximize a phenomenon called ‘quantum nonlocality.’ Hybrid optimization techniques are an attractive approach to apply to quantum networks because the quantum computer can efficiently solve one of the key computational bottlenecks: simulating the quantum many-bodied system.” He notes that hybrid quantum-classical techniques show promise as a key design tool for developing quantum network protocols.

Xanadu is a partner in a new $25 million NSF-funded center called HQAN (Hybrid Quantum Architectures and Networks), one of whose goals is to foster relationships between UIUC and quantum industry. “So [Doolittle’s] collaboration... is not only important scientifically, but it’s also important to serving the mission of our center,” says Chitambar. He adds that UIUC is interested in developing relationships with all types of quantum companies, big or small, and is also working with other universities in the Midwest. “We have a lot of exciting science going on here, and we’re not isolated by any means! We’re integrating with other institutions, and this will only lead to, I think, greater innovation and productivity in the end.”


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This story was published October 11, 2021.