Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1508.04358

Silicon-Nitride Platform for Narrowband Entangled Photon Generation

Sven Ramelow·Alessandro Farsi·Stéphane Clemmen·Daniel Orquiza·Kevin Luke·Michal Lipson·Alexander L. Gaeta
View original source ↗Read source document ↗Export metadata ↓

Abstract

CMOS-compatible photonic chips are highly desirable for real-world quantum optics devices due to their scalability, robustness, and integration with electronics. Despite impressive advances using Silicon nanostructures, challenges remain in reducing their linear and nonlinear losses and in creating narrowband photons necessary for interfacing with quantum memories. Here we demonstrate the potential of the silicon nitride (Si3N4) platform by realizing an ultracompact, bright, entangled photon-pair source with selectable photon bandwidths down to 30 MHz, which is unprecedented for an integrated source. Leveraging Si3N4's moderate thermal expansion, simple temperature control of the chip enables precise wavelength stabilization and tunability without active control. Single-mode photon pairs at 1550 nm are generated at rates exceeding 107 s-1 with mW's of pump power and are used to produce time-bin entanglement. Moreover, Si3N4 allows for operation from the visible to the mid-IR, which make it highly promising for a wide range of integrated quantum photonics applications.

Explore related subjects

quant-phphysics.optics

Keep this discovery

BibTeXRIS

Sven Ramelow, Alessandro Farsi, Stéphane Clemmen, Daniel Orquiza, Kevin Luke, Michal Lipson, Alexander L. Gaeta. 2015-08-18. Silicon-Nitride Platform for Narrowband Entangled Photon Generation. https://arxiv.org/abs/1508.04358

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph↗
arXiv2026

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph↗
arXiv2026

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph↗