Event
Ph.D. Research Proposal Exam: Jasvith Raj Basani
Friday, November 15, 2024
10:00 a.m.
Energy Research Facility (IREAP), Room ERF 1207
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: Ph.D. Research Proposal Exam
Name: Jasvith Raj Basani
Committee:
Professor Edo Waks (Chair)
Professor Saikat Guha
Professor Julius Goldhar
Date/time: Friday, 15th November 2024 at 10AM
Location: Energy Research Facility (IREAP), Room ERF 1207
Title: Quantum Information Processing with Programmable Photonics and Cavity QED Systems
Abstract:
Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over the bosonic degree of freedom using native photonic hardware remains a challenge. Here, we will discuss an architecture to prepare and perform logical quantum operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Fundamentally, the quantum photonic neural network processor performs two types of transformations on information encoded in the complex probability amplitudes of multimode multi-photon states – linear intermodulation among the basis elements, and elementwise non-linear activations.
By encoding information over serialized time-bins, linear unitary transformations can be realized in a hardware efficient manner, using a device we term as the ‘generalized Green Machine’. By programming the coupling among the modes, arbitrary unitary transformations decomposed using the Sine-Cosine Fractal can be implemented by the generalized Green Machine. The nonlinear transformation is realized through strong light-matter interaction of optical modes with a three-level Lambda atomic system. The dynamics of this interaction are confined to the single-mode subspace, enabling the construction of high-fidelity quantum gates. This nonlinearity functions as a photon-number selective phase gate, which facilitates the construction of a universal gate set and serves as the element-wise activation function in our neural network architecture.
Through numerical simulations, we demonstrate the versatility of our approach by constructing an end-to-end architecture for logical quantum computation. First, the network is able to realize a universal gate set for physical qubits encoded in the dual rail basis. We show that the network can be trained to prepare a wide array of multimode multi-photon resource states, which are essential for quantum algorithms.The universality of the network can also be exploited to process encoded quantum information, giving it increased robustness against decoherence channels. Both encoding and universal logical operations on bosonic error-correcting codes can be implemented to high fidelity. By adapting the optical nonlinearity built into our device, error correcting circuits can be built to detect and correct errors in these bosonic codes. Finally, we discuss the experimental implementation of learning quantum processes on a photonic processor. The proposed architecture paves the way for near-term quantum photonic processors that enable error-corrected quantum computation, and can be achieved using present-day integrated photonic hardware.
