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An NSF Industry/University Cooperative Research Center (IUCRC)

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Year 2 Projects

Quantum-Natural Language Processing (NLP) and Machine Learning (ML)

Motivation: Our goal is to establish a local infrastructure and a group of colleagues and graduate students focusing on research in the Quantum-NLP and ML domain. We aim at preparing and running experiments testing quantum approaches to NLP at two levels: a.) algorithms and methods that improve current shortcomings of purely neural methods at the level of semantics and common-sense reasoning of NLP, and b.) identify ways to improve the training and testing cycles of ML in optimization-based learning. At the qualitative level, current NLP methods fail to provide. At the ML and technical level, neural approaches on classical hardware are too costly and time-consuming. Quantum solutions have the potential to improve the situation significantly.

PIs: Prof. Damir Cavar (Indiana University) and Prof. Larry Moss (Indiana University)

Benchmarking Hybrid Quantum Computing

Motivation: It is clear that to be useful for real-world sized problems that quantum computing needs to become much more “hybrid”, involving closer coupling of classical and quantum computation. To do this requires understanding what is the nature of the cross-system information/command flow, what are the metrics that one ought to use to measure such, how have current hybrid codes performed against such metrics, and what computational models and language features ought to look like that would most optimize such codes. As with the first year, the goal is to be problem solution focused and both technology and algorithm agnostic.

PIs: Prof. Peter Kogge (University of Notre Dame) and Prof. Amr Sabry (Indiana University)

Topological insulator quantum dots for long-wavelength quantum technologies

Motivation: Epitaxial III-V quantum dots provide the ability to generate single photons at telecom wavelengths enabling quantum-encrypted communication with lower hardware overhead compared to parametric down conversion. III-V dots are however limited in terms of frequency band. We are motivated to develop material platforms where emission of single photons in the far-infrared (FIR) to mid-infrared (MIR) can be achieved. This ability enables transformative quantum technologies for communications beyond 1.55μm and for chemical sensing with high-sensitivity. Topological insulators (TI) that have an inverted bulk energy gap and gapless surface modes can enable this.

PI: Prof. Badih Assaf (University of Notre Dame)

Entangled organic molecules for quantum light

Motivation: To use lifetime-limited organic molecules to generate many-body entangled states of light, and to characterize the multi-photon and multi-mode quantum light for usefulness in quantum sensing, computation, and communication.

PI: Prof. Jonathan Hood (Purdue University)

Prototypical quantum processors based on electron-on-solid-neon (eNe) qubits

Motivation: We recently realized a new solid-state qubit platform by trapping single electrons on an ultrapure solid neon surface in a vacuum and manipulating the electron’s charge (motional) states by microwave photons in an on-chip superconducting resonator [X. Zhou … D. Jin, Nature 605, 46–50 (2022); Nature Physics 20, 116–122 (2024)]. The measured relaxation and coherence times of the electron-on-solid-neon (eNe) qubits have reached 0.1ms, and the single-qubit gate fidelity has reached 99.97%, outperforming all the traditional semiconductor and superconductor charge qubits and rivaling state-of-the-art transmon and fluxonium qubits. In our latest experiments, we have also enhanced the electron-photon coupling strength from ~3MHz to ~30MHz that promises the realization of high-fidelity two-qubit gates soon. With the CQT funding opportunity, especially the related industry and government interest, we want to seek expedited and unique routes to scale up this qubit platform by demonstrating prototypical quantum processors, before the single-qubit performance has been fully optimized or a high-fidelity two-qubit gate has been achieved.

PIs: Prof. Dafei Jin (University of Notre Dame) and Prof. Tongcang Li (Purdue University)

Decomposition-based approaches to enable practical quantum computing for optimization

Motivation: Optimization applications appear in various fields of science and engineering. Addressing these problems efficiently has motivated the development of advanced algorithms and hardware, some leveraging quantum phenomena. Combinatorial problems can require worst-case exponentially growing resources, but practical applications abound, and efficient methods to tackle them are actively sought. We aim to apply advanced algorithmic approaches based on decomposition methods to efficiently use quantum computers in solving practical optimization problems.

PIs: Alex Pothen (Purdue University), Arnab Banerjee (Purdue University), and David Bernal (Purdue University)

Quantum-Enabled Software Exploit Synthesis to Detect Object Injection Vulnerabilities

Motivation: Modern programs often use object-oriented design, where the basic system components are objects and classes. Objects in a program can be hijacked by a hacker to conduct object injection attacks. The hijacking occurs when benign features handling external objects (e.g., from a socket) are misused. When the program invokes one of the hijacked object’s methods, it leads to a sequence of method calls that results in a malicious behavior.  Log4Shell is an example of a severe object injection vulnerability that compromised the security of millions of software systems. Object injection vulnerabilities are difficult to detect because they exhibit control and data flows similar to those of benign code execution. Although fuzzing was used to detect object injection attacks, its long execution time and time budget limits mean it misses latent vulnerabilities. Existing static analyzers cannot identify object hijacking scenarios because the objects can be instances of classes that are unused, but loadable at runtime, i.e., not observable at static (compile) time by the analyzer. We propose to detect object injection vulnerabilities by synthesizing and injecting malicious objects (exploits) to verify whether it triggers harmful behavior in the program.

PI: Prof. Joanna Cecilia da Silva Santos (University of Notre Dame)