arXiv subjects

Jasvith Raj Basani

Publications and source records attributed to Jasvith Raj Basani.

10 recordsLinked to original sources

arXiv2026

Bright Telecom Spin-Photon Interface in Silicon Photonics

Silicon is an attractive host for scalable quantum photonics, but the absence of bright telecom-band emitters with optically addressable spin states has limited its use for spin-photon interfaces. Here we demonstrate the Al1-center, an aluminum--carbon defect in silicon, as a bright waveguide-integrated single-photon emitter with a ground-state spin. Using isotopically purified silicon-on-insulator nanophotonic devices, we isolate individual Al1-centers and observe high-purity single-photon emission with $g^{(2)}(0)=0.04$ without background subtraction. Time-resolved photoluminescence spectroscopy reveals a fast excited-state lifetime of 135 ns, nearly an order of magnitude shorter than the benchmark provided by the well-studied T-center. Resonant photoluminescence excitation measurements further resolve the zero-phonon transition and reveal a narrow homogeneous linewidth reaching 47 MHz, threefold narrower than the T-center under comparable temperature. Through magneto-optical spectroscopy, we resolve the spin-dependent transitions of the bound-exciton manifold and achieve spin-selective optical pumping, fulfilling the prerequisite for quantum state initialization and readout. These results establish the Al1-center as a bright telecom-band spin-photon interface in silicon photonics and introduce a promising platform for integrated quantum networks.

quant-ph↗

arXiv2026

Hardware-Efficient Universal Linear Transformations for Optical Modes in the Synthetic Time Dimension

Recent progress in photonic information processing has spurred strong demand in scalable and reconfigurable photonic circuitry. Conventional spatially-meshed multi-port interferometers require a number of components growing quadratically with the system size, posing a fundamental scaling challenge ahead. Here, we introduce a hardware-efficient synthetic time-domain photonic processor that achieves at least an exponential reduction in hardware component count for implementing arbitrary linear transformations. The processor's dynamic connectivity allows systematic pruning, minimizing optical loss while preserving all-to-all connectivity. We benchmark our architecture on the task of boosted Bell state measurements -- a protocol essential for linear optical quantum computation, and show that it exceeds thresholds for universal cluster-state quantum computation under realistic hardware constraints. We link the device performance to the geometry of multi-photon transport, showing that localization effects from redundant, imperfect hardware may enhance robustness to coherent errors. Our design establishes a practical pathway toward near-term, scalable, and reconfigurable photonic processors in the synthetic time dimension.

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arXiv2026

Electrical Control of Optically Active Single Spin Qubits in ZnSe

Electrons bound to shallow donors in ZnSe quantum wells are promising candidates for optically addressable spin qubits and single-photon sources. However, their optical coherence and indistinguishability are often limited by spectral broadening arising from charge fluctuations in the local environment. Here, we report electrical control of single donor qubits in ZnSe quantum wells. The applied field induces a DC Stark shift that tunes the emission energy over a range exceeding 30 times the inhomogeneous linewidth, effectively compensating for emitter-to-emitter variations. Concurrently, the field stabilizes trap occupancy, yielding a twofold reduction in optical linewidth and the suppression of spectral wandering. A statistical model based on trap dynamics qualitatively reproduces these observations and elucidates the mechanism of field-assisted charge noise suppression. Our results identify electrical control as a versatile pathway to significantly improve optical and spin addressability.

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arXiv2025

Inverse-Designed Photonic Crystal Cavities with Controllable Far-Field Numerical Aperture

Photonic crystal cavities confine light to subwavelength volumes, enabling strong light-matter interactions for applications in low-power photonics, opto-electronics, nonlinear optics, and quantum information. These applications demand cavities that combine high quality factors, low mode volumes, and high coupling efficiencies. However, optimizing across these metrics requires exploring a large design space, motivating the use of inverse design strategies. Previous inverse design efforts targeted high quality factors and low mode volumes, sacrificing the coupling efficiency or lacking the ability to precisely control the far-field radiation pattern. In this work, we present an inverse design framework that simultaneously optimizes cavity quality factor and far-field numerical aperture, both specified as design targets. Using this method, we design L3 photonic crystal cavities, with different far-field numerical apertures, in the visible wavelength and fabricate them in silicon nitride. Photoluminescence measurements confirm experimental control of the far-field numerical aperture and reveal a 28-fold and 3.9-fold improvement in the coupling efficiency and quality factor respectively when compared to the standard L3 cavity. Disorder analysis further shows that the designs retain significant performance despite nanofabrication imperfections. Our work demonstrates a versatile inverse design framework for multi-objective optimization of photonic crystal cavities to attain high quality factors and coupling efficiency.

physics.optics↗

arXiv2024

Universal Logical Quantum Photonic Neural Network Processor via Cavity-Assisted Interactions

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 propose an architecture to prepare and perform logical quantum operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Central to our approach is the optical nonlinearity, which is realized through strong light-matter interaction 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 executing tasks that are key to logical quantum information processing. The network is able to deterministically prepare a wide array of multimode multi-photon states, including essential resource states. We also show that the architecture is capable of encoding and performing logical operations on bosonic error-correcting codes. Additionally, by adapting components of our architecture, error-correcting circuits can be built to protect bosonic codes. 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.

quant-ph↗

arXiv2024

Towards the Information-Theoretic Limit of Programmable Photonics

The scalability of many programmable photonic circuits is limited by the $2π$ tuning range needed for the constituent phase shifters. To address this problem, we introduce the concept of a phase-efficient circuit architecture, where the average phase shift is $\ll 2π$. We derive a universal information-theoretic limit to the phase-shift efficiency of universal multiport interferometers, and propose a "3-MZI" architecture that approaches this limit to within a factor of $2\times$, approximately a $10\times$ reduction in average phase shift over the prior art, where the average phase shift scales inversely with system size as $O(1/\sqrt{N})$. For non-unitary circuits, we show that the 3-MZI saturates the theoretical bound for Gaussian-distributed target matrices. Using this architecture, we show optical neural network training with all phase shifters constrained to $\lesssim 0.2$ radians without loss of accuracy.

physics.optics↗

arXiv2023

Enhanced Photon Routing Beyond the Blockade Limit Via Linear Optics

Directing indistinguishable photons from one input port into separate output ports is a fundamental operation in quantum information processing. The simplest scheme for achieving routing beyond random chance uses the photon blockade effect of a two-level emitter. But this approach is limited by a time-energy uncertainty relation. We show that a linear optical unitary transformation applied after the atom enables splitting efficiencies that exceed this time-energy limit. We show that the linear optical unitary improves the splitting efficiency from 67\% to 82\% for unentangled photon inputs, and from 77\% to 90\% for entangled photon inputs. We then optimize the temporal mode profile of the entangled photon wavefunction to attain the optimal splitting efficiency of 92\%, a significant improvement over previous limits derived using a two-level atom alone. These results provide a path towards optimizing single photon nonlinearities and engineering programmable and robust photon-photon interactions for practical, high-fidelity quantum operations.

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arXiv2022

A Self-Similar Sine-Cosine Fractal Architecture for Multiport Interferometers

Multiport interferometers based on integrated beamsplitter meshes have recently captured interest as a platform for many emerging technologies. In this paper, we present a novel architecture for multiport interferometers based on the Sine-Cosine fractal decomposition of a unitary matrix. Our architecture is unique in that it is self-similar, enabling the construction of modular multi-chiplet devices. Due to this modularity, our design enjoys improved resilience to hardware imperfections as compared to conventional multiport interferometers. Additionally, the structure of our circuit enables systematic truncation, which is key in reducing the hardware footprint of the chip as well as compute time in training optical neural networks, while maintaining full connectivity. Numerical simulations show that truncation of these meshes gives robust performance even under large fabrication errors. This design is a step forward in the construction of large-scale programmable photonics, removing a major hurdle in scaling up to practical machine learning and quantum computing applications.

physics.optics↗

arXiv2022

All-Photonic Artificial Neural Network Processor Via Non-linear Optics

Optics and photonics has recently captured interest as a platform to accelerate linear matrix processing, that has been deemed as a bottleneck in traditional digital electronic architectures. In this paper, we propose an all-photonic artificial neural network processor wherein information is encoded in the amplitudes of frequency modes that act as neurons. The weights among connected layers are encoded in the amplitude of controlled frequency modes that act as pumps. Interaction among these modes for information processing is enabled by non-linear optical processes. Both the matrix multiplication and element-wise activation functions are performed through coherent processes, enabling the direct representation of negative and complex numbers without the use of detectors or digital electronics. Via numerical simulations, we show that our design achieves a performance commensurate with present-day state-of-the-art computational networks on image-classification benchmarks. Our architecture is unique in providing a completely unitary, reversible mode of computation. Additionally, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power.

physics.optics↗

arXiv2020

Continuous-Variable Deep Quantum Neural Networks for Flexible Learning of Structured Classical Information

Quantum computation using optical modes has been well-established in its ability to construct deep neural networks. These networks have been shown to be flexible both architecturally as well as in terms of the type of data being processed. We leverage this property of the Continuous-Variable (CV) model to construct stacked single mode networks that are shown to learn structured classical information, while placing no restrictions on the size of the network, and at the same time maintaining it's complexity. The hallmark of the CV model is its ability to forge non-linear functions using a set of gates that allows it to remain completely unitary. The proposed model exemplifies that the appropriate photonic hardware can be integrated with present day optical communication systems to meet our information processing requirements. In this paper, using the Strawberry Fields software library on the MNIST dataset of hand-written digits, we demonstrate the adaptability of the network to learn classical information to fidelities of greater than 99.98%

quant-ph↗