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Abhiram Devata

Publications and source records attributed to Abhiram Devata.

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Materials for Quantum Information Science: Roles in the Quantum Evolution 2.0

Quantum information science is entering a second phase, the Quantum Evolution 2.0, in which the challenge has shifted from demonstrating coherent control of individual quantum states to building scalable multi-qubit processors and networks. This transition places materials science at the center of the field. Across superconducting circuits, quantum defects, quantum photonic devices, and emerging materials platforms, including two-dimensional materials and heterostructures, performance is now limited less by device design than by poorly controlled surfaces, buried interfaces, and defects whose atomic identities remain incompletely known. This review surveys the materials challenges of these quantum platforms together with the characterization methods needed to resolve them. For each platform we identify the dominant decoherence mechanisms, the current state of materials understanding, and the most pressing open materials problems. A cross-platform comparison then reveals a shared structure-coherence problem. The implicated material chemistry recurs across platforms, involving light elements in disordered or buried environments, yet no platform can quantitatively connect a specific atomic-scale structure to a measured change in coherence. We close by identifying three needs, mechanistic understanding of decoherence at the atomistic level, high-throughput proxy metrics predictive of device performance, and characterization tools built for quantum materials, whose resolution would advance coherence, scalability, and integration across all platforms.

quant-ph

Programmable and nonvolatile computing with composition tuning in thin film lithium niobate

Matrix-vector multiplications are fundamental operations in artificial intelligence and high-throughput computations, and are executed repeatedly during training and inference. Their high energy cost in electronic processors motivate scalable photonic computing approaches that reduce the energy required per operation. Thin film lithium niobate is a dominant photonic platform due to its large electro-optic effect. However, it lacks nonvolatile index tuning mechanisms, which promise to pave the way for energy-efficient photonic computing. Here, we explore electrochemical lithiation as a route to nonvolatile matrix-vector multiplications in thin film lithium niobate. The lithium niobate phase is stable at room temperature over a 2% Li composition window with an associated composition-dependent refractive index. We computationally demonstrate this as a programmable, low-loss approach to perform matrix-vector multiplications by using composition to control matrix weights. We design Mach-Zehnder interferometers to perform image processing tasks under realistic material loss constraints. We also design microring resonators for iterative weight updates, using gradient descent training to program target matrix operations with matrix-vector multiplication accuracy validated at 1.6% average relative error. These demonstrations show a facile route towards nonvolatile photonic computing in thin film lithium niobate, addressing a critical requirement for energy-efficient photonic matrix operations at scale.

physics.optics