Our study demonstrates successful error mitigation of indistinguishably-related noise in a quantum photonic processor through the application of the zero-noise extrapolation (ZNE) technique. By measuring observable values at different error levels, we were able to extrapolate toward a noise-free regime. We examined the impact of partial distinguishability of photons in a two-qubit processor implementing the variational quantum eigensolver for a Schwinger Hamiltonian. Our findings highlight the effectiveness of the extrapolation technique in mitigating indistinguishably-related noise and improving the accuracy of the Hamiltonian eigenvalue estimation.
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http://dx.doi.org/10.1364/OL.532017 | DOI Listing |
J Phys Chem C Nanomater Interfaces
January 2025
Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC - Universidad de Zaragoza, Plaza San Francisco s/n, Zaragoza 50009, Spain.
A strategy toward the realization of a quantum spin processor involves the coupling of spin qubits and qudits to photons within superconducting resonators. To enable the realization of such hybrid architecture, here we first explore the design of a chip with multiple lumped-element LC superconducting resonators optimized for their coupling to distinct transitions of a vanadyl porphyrin electronuclear qudit. The controlled integration of the vanadyl qudit onto the superconducting device, both in terms of number and orientation, is then attained using the formation of nanosheets of a 2D framework built on the vanadyl qudit as a node.
View Article and Find Full Text PDFNat Photonics
October 2024
Institut national de la recherche scientifique, Centre Énergie Matériaux Télécommunications, Varennes, Quebec Canada.
Quantum walks on photonic platforms represent a physics-rich framework for quantum measurements, simulations and universal computing. Dynamic reconfigurability of photonic circuitry is key to controlling the walk and retrieving its full operation potential. Universal quantum processing schemes based on time-bin encoding in gated fibre loops have been proposed but not demonstrated yet, mainly due to gate inefficiencies.
View Article and Find Full Text PDFNat Commun
January 2025
TUM School of Natural Sciences, Department of Physics and Munich Center for Quantum Science and Technology (MCQST), Technical University of Munich, James-Franck-Str. 1, Garching, Germany.
Small registers of spin qubits in silicon can exhibit hour-long coherence times and exceeded error-correction thresholds. However, their connection to larger quantum processors is an outstanding challenge. To this end, spin qubits with optical interfaces offer key advantages: they can minimize the heat load and give access to modular quantum computing architectures that eliminate cross-talk and offer a large connectivity.
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory for Extreme Photonics and Instrumentation, Center for Optical & Electromagnetic Research, College of Optical Science and Engineering, International Research Center for Advanced Photonics (Haining), Zhejiang University, Hangzhou, China.
Silicon photonic signal processors promise a new generation of signal processing hardware with significant advancements in processing bandwidth, low power consumption, and minimal latency. Programmable silicon photonic signal processors, facilitated by tuning elements, can reduce hardware development cycles and costs. However, traditional programmable photonic signal processors based on optical switches face scalability and performance challenges due to control complexity and transmission losses.
View Article and Find Full Text PDFAdv Sci (Weinh)
December 2024
Program on Key Materials, Academy of Innovative Semiconductor and Sustainable Manufacturing (AISSM), National Cheng Kung University, No. 1, University Road, Tainan City, 70101, Taiwan.
As the demand for the neuromorphic vision system in image recognition experiences rapid growth, it is imperative to develop advanced architectures capable of processing perceived data proximal to sensory terminals. This approach aims to reduce data movement between sensory and computing units, minimizing the need for data transfer and conversion at the sensor-processor interface. Here, an optical neuromorphic synaptic (ONS) device is demonstrated by homogeneously integrating optical-sensing and synaptic functionalities into a unified material platform, constructed exclusively by all-inorganic perovskite CsPbBr quantum dots (QDs).
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