Year 3 Projects

Quantum Approaches to Phylogenetic Tree Reconstruction

Project Motivation: The phylogenetic tree is a fundamental object in genetics research. Thus, inferring the tree is an important problem in domains including medicine (e.g., tracking COVID strains) and agriculture (e.g., crop genetics). However, with the exponential growth of genomic data, accurate construction of phylogenetic trees has become challenging. Existing methods use iterative hill climbing as a heuristic, but are time-consuming and easily get stuck in local optima. Similarly, inferring evolutionary rates on trees faces related computational problems. Thus, there is a need and scope to improve the tree search algorithms. In this project we seek quantum approaches to improve the speed and accuracy of phylogenetic tree construction and evolutionary analysis.

PIs: Nicholas LaRacuente and Matthew Hahn (Indiana University – Bloomington)

Hybrid Quantum Algorithms for Chemical Dynamics and Electronic Structure

Project Motivation: The promise of solving exponentially complex problems efficiently using quantum computing hardware and associated software is a rapidly evolving research frontier. While we are in the early stages of this quantum revolution, there are a diverse set of scientific and technological areas, such as artificial photosynthesis for solar energy harvesting, computational catalysis for nitrogen fixation and CO2 reduction, and quantum structure-based drug design or vaccine development for human health, that can benefit from such developments. However, true progress in such areas can only be achieved by a rigorous study and understanding of the structure and dynamics of complex materials and chemical systems necessitating an accurate treatment of electron correlation effects in conjunction with a rigorous treatment of quantum molecular dynamical effects.

This research team includes theoretical chemists, atomic physicists, and theoretical computer scientists, that have a long history of collaboration through NSF-funded grants. Our team has already contributed to workforce development at the interface of these areas, and we will develop new quantum algorithms and technologies to tackle frontier problems in materials and biological chemistry.

PIs: Srinivasan S. Iyengar, Amr Sabry, and Phil Richerme (Indiana University – Bloomington)

A New Solid-State Quantum Transducer for Distributed Quantum Information Processing

Project Motivation: Quantum transducers are crucial missing components in the roadmaps toward future quantum computing and networking [Mohseni et al., arXiv:2411.10406; IBM Quantum Roadmap]. They bridge local quantum-computer nodes in the microwave domain and remote quantum-network links in the optical domain. So far, the microwave-optical conversion efficiency of most quantum transducers is impractically low [Mirhosseini et al., Nature 588, 599-603 (2020)]. We propose to develop a new quantum transducer based on rare-earth (RE) ions in rare-gas (RG) solids. RE ions contain unfilled 4f electrons that are robust against environmental disturbance and provide a natural spin-photon interface [Zhong et al., Nanophotonics 8, 2003-2015 (2019)]. RG solids, as demonstrated in our prior works [Zhou et al., Nature 605, 46–50 (2022); Nat. Phys. 20, 116–122 (2024)], are the purest solid hosts in nature with minimal electrical and magnetic noises. Merging the two systems in one platform offers an unprecedented opportunity in quantum transduction. In comparison to randomly doped and loosely packed ions in conventional solids, the RE-in-RG platform enables deterministically trapped and densely packed ions at designated locations with <100 nm separation. It will significantly enhance the cooperativity and, hence, microwave-optical conversion efficiency and bandwidth [Wang et al., npj Quantum Inf. 8, 149 (2022)].

PIs: Dafei Jin and Yutian Wen (University of Notre Dame), Tongcang Li and Shengwang Du (Purdue University)

Quantum Sensors Utilizing Long-Range Entanglement Near Quantum Criticality

Project Motivation: In conventional sensing, when multiple electrons reside on lattice sites, the detection limit is bound by the standard quantum limit, where sensitivity is ∝ √N with N representing the number of effective lattice sites. However, our approach involves entangled electrons, which occur naturally close to the phase transition, allowing sensitivity to scale linearly with N – known famously as the Heisenberg limit of detection. Our project aims to create a state near a topological phase transition where coherence length diverges, leading to state of N naturally connected states allowing us to approach the Heisenberg limit in the solid state. Our objective is to leverage the robustness of solid-state platforms to develop ultrasensitive quantum critical sensors for amplitudes and phases of faint optical signals. The solid-state platform is small, and robust for deployment in non-ideal situations where other sensors are non-viable.

PIs: Yuli Lyanda-Geller, Tiancong Zhu, Arnab Banerjee, and Peter Bermel (Purdue University), Gerardo Ortiz (Indiana University – Bloomington)

Generation Of Quantum Light from Two-Dimensional Semiconductors

Project Motivation: Single photon sources are a cornerstone of optical quantum technologies. An open challenge is to efficiently produce quantum states of single and multiple photons in a controlled fashion. Such states have quantum advantages in sensing, quantum-secured communication and computing. Current technology relies on parametric down-conversion and spontaneous emission, but both have distinct disadvantages in brightness and collection efficiency. Here, we propose collective scattering from monolayered semiconductors as an alternative mechanism for the production of quantum light. Collective scattering of light is a powerful tool that can limit loss and has been demonstrated in atomic arrays and semiconductors. We combine this feature with strong Rydberg interactions to convert coherent laser light into a train of single photons.

PIs: Valentin Walther and Yong Chen (Purdue University)

Decomposition-Based Approaches for Practical Quantum Optimization

Project Motivation: Optimization spans science and engineering, driving algorithm and hardware advances, including quantum methods. While combinatorial problems scale exponentially, practical applications necessitate efficient solutions. We focus on decomposition-based algorithms to leverage quantum computing for real-world optimization.

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

TITAN: Quantum Adversarial Attack Detection

Project Motivation: Deep learning models for natural language processing (NLP) have revolutionized how machines understand and interact with human language. With the rapid advances in the AI/ML field, ML models are integral to businesses, from search engines to virtual assistants. However, adversarial attacks threaten model security by imperceptibly altering inputs to fool models [1]. For example, an attacker could manipulate characters in a harmful post, tricking the model into misclassifying it as a safe content [6]. Prior works [2, 3], including our own [4], have found that classical techniques struggle to detect adversarial attacks due to high computational complexity, limited generalization, and susceptibility to adaptive attacks.

PI: Joanna C. S. Santos (University of Notre Dame)

Characterizing Quantum Vacuum for Precision Sensing

Project Motivation: There are many systems that require the smallest possible perturbation in order to make a measurement or characterization. Striving to use existing fields is a requirement not easily attainable (often impossible) in these cases. As the quantum vacuum is present in all cases, using it when possible appears to be a logical direction to pursue. The problem is, however, that although quantum vacuum is reasonably well understood, there are aspects not yet completely developed or characterized. We will provide the basis to further understand quantum vacuum, and put constraints in the capabilities when used as a sensor. The study will be carried out to optimize an experimental system to fully exploit vacuum in the presence of boundary conditions as a spatially resolved, sensitive sensor.

PIs: Ricardo Decca, Ruihua Cheng, and Merrell Johnson (Indiana University – Indianapolis)

Qubits-Based Simulation of Quantum Criticality to Enable Hamiltonian Discovery in Quantum Materials

Project Motivation: The project is motivated by our success in using D-Wave, IBM and QuEra backends to simulate material-responsive Hamitonians such as the Shastry-Sutherland and the triangular lattice clock-model (Kosterlitz-Thouless) Hamiltonians. We were able to successfully observe the correct Ising magnetization plateaus (PRX-Quantum 1, 202320 (2020)) and Transverse field Ising model (TFIM) phases which are validated against DMRG. In CQT Year-1, using quantum quench in D-Wave (Nature Comm 15, 10756 (2024)), accessed coherent spin dynamics where we discovered a new type of quantum coarsening behavior close to the 3D-XY critical point. This result was corroborated by Google and QuEra (both just appeared in Nature). Deduction of new phases – from frustrated Hamiltonians (both defect-free and with defects) is an outstanding problem in quantum material science, and is a hard problem for a 2D or 3D lattice where DMRG and QMC fail. Deducing the universality classes of these Hamiltonians – seen in several real materials – are a near-term application for quantum computers which we wish to exploit. In doing so, we discover new quantum and topological phases with no classical analogues, but also understand effective qubit temperatures, qubit randomness, temporal and spatial coherence and ways to benchmark many-body behavior. Combined to AI, this project is the perfect opportunity to classify defects using quantum backends (AWS, D-Wave, QCi, Infleqtion, etc.) and compare to real materials, ultimately creating a framework to understand ‘dirty’ samples and non-ideal data and discern quantum versus classical effect from the data. As a stretch goal, we propose to implement topological phase transitions (an important application for quantum sensing) using tenets of Restricted Boltzmann Machines. Co-PI Banerjee has more than 7 years of experience using quantum computers and annealers for material Hamiltonians and Carlson is an expert in classical and quantum phase transitions and criticality.

PIs: Arnab Banerjee and Erica Carlson (Purdue University)