Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2002.10106

High-quality photonic entanglement based on a silicon chip

Dorian Oser·Sébastien Tanzilli·Florent Mazeas·Carlos Alonso-Ramos·Xavier Le Roux·Grégory Sauder·Xin Hua·Olivier Alibart·Laurent Vivien·Éric Cassan·Laurent Labonté
View original source ↗Read source document ↗Export metadata ↓

Abstract

The fruitful association of quantum and integrated photonics holds the promise to produce, manipulate, and detect quantum states of light using compact and scalable systems. Integrating all the building-blocks necessary to produce high-quality photonic entanglement in the telecom wavelength range out of a single chip remains a major challenge, mainly due to the limited performance of on-chip light rejection filters. We report a stand-alone, telecom-compliant, device that integrates, on a single substrate, a nonlinear photon-pair generator and a passive pump rejection filter. Using standard channel-grid fiber demultiplexers, we demonstrate the first entanglement quantification of such a integrated circuit, showing the highest raw quantum interference visibility for energy-time entangled photons over two telecom-wavelength bands. Genuinely pure maximally entangled states can therefore be generated thanks to the high-level of noise suppression obtained with the pump filter. These results will certainly further promote the development of more advanced and scalable photonic-integrated quantum systems compliant with telecommunication standards.

Explore related subjects

quant-phphysics.optics

Keep this discovery

BibTeXRIS

Dorian Oser, Sébastien Tanzilli, Florent Mazeas, Carlos Alonso-Ramos, Xavier Le Roux, Grégory Sauder, Xin Hua, Olivier Alibart, Laurent Vivien, Éric Cassan, Laurent Labonté. 2020-02-24. High-quality photonic entanglement based on a silicon chip. https://doi.org/10.1038/s41534-020-0263-7

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↗