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Neural-network quantum states for periodic systems in continuous space
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Hidden-nucleons neural-network quantum states for the nuclear many-body problem
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Dilute neutron star matter from neural-network quantum states
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Entanglement purification on quantum networks
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Quantum Federated Learning with Quantum Networks
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QUANT-NET: A testbed for quantum networking research over deployed fiber
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Polarization-entangled source for flex-grid C+L-band quantum networks
We demonstrate an ultrabroadband polarization-entangled source integrating both C- and L-band wavelength-selective switches for spectral routing and allocation in a single fiberized setup, verifying high fidelity through tomography of all 150 pairs of 25 GHz-wide channels.
Quantum Networks for High Energy Physics (HEP)
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SWITCHABLE QUANTUM BUFFER FOR TRANSPARENT OPTICAL QUANTUM NETWORK
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Quantum Wrapper Networking for Entanglement Management and Control in Transparent Optical Quantum Networks
The overarching goal of this three-year project was to design and experimentally demonstrate a quantum wrapper networking (QWN) protocol and architecture for entanglement management and control in coexisted classical-quantum networks, facilitating a global quantum Internet. In QWN, the quantum payload (qubits) is wrapped with classical bits as a header. The classical bits facilitate end-to-end transport of the quantum payload without having to read or alter the quantum data payload at intermediate switches and routers.
Embedding Learning in Hybrid Quantum-Classical Neural Networks
Quantum embedding learning is an important step in the application of quantum machine learning to classical data. In this paper we propose a quantum few-shot embedding learning paradigm, which learns embeddings useful for training downstream quantum machine learning tasks. Crucially, we identify the circuit bypass problem in hybrid neural networks, where learned classical parameters are optimized to represent the dataset without the quantum kernel. We observe that the few-shot learning embeddings generalize to unseen classes, and suffer less from the circuit bypass problem in terms of better occupation of the parameter space compared with embeddings learned from regression and classification.
Training quantum neural networks using the quantum information bottleneck method
Abstract We provide in this paper a concrete method for training a quantum neural network to maximize the relevant information about a property that is transmitted through the network. This is significant because it gives an operationally well founded quantity to optimize when training autoencoders for problems where the inputs and outputs are fully quantum. We provide a rigorous algorithm for computing the value of the quantum information bottleneck quantity within error ε that requires O ( log 2 ( 1 / ϵ ) + 1 / δ 2 ) queries to a purification of the input density operator if its spectrum is supported on { 0 } ⋃ [ δ , 1 − δ ] for δ > 0 and the kernels of the relevant density matrices are disjoint. We further provide algorithms for estimating the derivatives of the QIB function, showing that quantum neural networks can be trained efficiently using the QIB quantity given that the number of gradient steps required is polynomial.
Telecom Networking with a Diamond Quantum Memory
Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy ( Si V ) center in diamond to the telecom O band, maintaining low noise ( g 2 ( 0 ) < 0.1 ) and high indistinguishability ( V = 89 ± 8 % ). We further demonstrate the utility of this system for quantum networking by converting telecom-band time-bin pulses, sent across a lossy and noisy 50-km deployed fiber link, to the visible band and entangling them with a diamond quantum memory with fidelity F ≥ 87 ± 2.5 % . These results demonstrate the viability of Si V quantum memories integrated with telecom-band systems for scalable quantum networking applications. Published by the American Physical Society 2024