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Daniele Bajoni

Publications and source records attributed to Daniele Bajoni.

At least 37 records · Page 2Linked to original sources

Programmable frequency-bin quantum states in a nano-engineered silicon device

Photonic qubits should be controllable on-chip and noise-tolerant when transmitted over optical networks for practical applications. Furthermore, qubit sources should be programmable and have high brightness to be useful for quantum algorithms and grant resilience to losses. However, widespread encoding schemes only combine at most two of these properties. Here, we overcome this hurdle by demonstrating a programmable silicon nano-photonic chip generating frequency-bin entangled photons, an encoding scheme compatible with long-range transmission over optical links. The emitted quantum states can be manipulated using existing telecommunication components, including active devices that can be integrated in silicon photonics. As a demonstration, we show our chip can be programmed to generate the four computational basis states, and the four maximally-entangled Bell states, of a two-qubits system. Our device combines all the key-properties of on-chip state reconfigurability and dense integration, while ensuring high brightness, fidelity, and purity.

quant-ph↗

Super spontaneous four-wave mixing in an array of silicon microresonators

Composite optical systems can show compelling collective dynamics. For instance, the cooperative decay of quantum emitters into a common radiation mode can lead to superradiance, where the emission rate of the ensemble is larger than the sum of the rates of the individual emitters. Here, we report experimental evidence of super spontaneous four-wave mixing (super SFWM), an analogous effect for the generation of photon pairs in a parametric nonlinear process on an integrated photonic device. We study this phenomenon in an array of microring resonators on a silicon photonic chip coupled to bus waveguides. We measured a cooperative pair generation rate that always exceeds the incoherent sum of the rates of the individual resonators. We investigate the physical mechanisms underlying this collective behaviour, clarify the impact of loss, and address the aspects of fundamental and technological relevance of our results.

quant-ph↗

Quantum variational learning for entanglement witnessing

Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means. There has been, however, a limited amount of work on the characterization and analysis of quantum data by means of these techniques, so far. This work focuses on one such ambitious goal, namely the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits, based on their degree of entanglement. This is a notoriously hard task to be performed on classical hardware, due to the exponential scaling of the corresponding Hilbert space as $2^n$. We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled. More in detail, we made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness. This work may pave the way to an efficient combination of QML algorithms and quantum information protocols, possibly outperforming classical approaches to analyse quantum data. All these topics are discussed and properly demonstrated through a simulation of the related quantum circuit model.

quant-ph↗

Quantum neural network autoencoder and classifier applied to an industrial case study

Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers it is relevant to develop algorithms that are useful for actual industrial processes. In this work we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.

quant-ph↗

Collapse dynamics and Hilbert-space stochastic processes

Spontaneous collapse models of state vector reduction represent a possible solution to the quantum measurement problem. In the present paper we focus our attention on the Ghirardi-Rimini-Weber (GRW) theory and the corresponding continuous localisation models in the form of a Brownian-driven motion in Hilbert space. We consider experimental setups in which a single photon hits a beam splitter and is subsequently detected by photon detector(s), generating a superposition of photon-detector quantum states. Through a numerical approach we study the dependence of collapse times on the physical features of the superposition generated, including also the effect of a finite reaction time of the measuring apparatus. We find that collapse dynamics is sensitive to the number of detectors and the physical properties of the photon-detector quantum states superposition.

quant-ph↗

Quantum state features of the FEL radiation from the occupation number statistics

The statistical features of the radiation emitted by Free-Electron Lasers (FELs), either by Self-Amplified Spontaneous Emission (SASE-FELs) or by seeded emission (seeded-FELs), are attracting increasing attention because of the use of such light in probing high energy states of matter and their dynamics. While the experimental studies conducted so far have mainly concentrated on correlation functions, here we shift the focus towards reconstructing the distribution of the occupation numbers of the radiation energy states. In order to avoid the various drawbacks related to photon counting techniques when large numbers of photons are involved, we propose a Maximum Likelihood reconstruction of the diagonal elements of the FEL radiation states in the energy eigenbasis based on the statistics of no-click events. The ultimate purpose of such a novel approach to FEL radiation statistics is the experimental confirmation that SASE-FEL radiation exhibits thermal occupation number statistics, while seeded-FEL light Poissonian statistics typical of coherent states and thus of laser light. In this framework, it is interesting to note that the outcome of this work can be extended to any process of harmonic generation from a coherent light pulse, unlocking the gate to the study of the degree to which the original distinctive quantum features deduced from the statistical photon number fluctuations are preserved in non-linear optical processes.

quant-ph↗

Quantum computing models for artificial neural networks

Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small scale quantum computing devices have become available in recent years, paving the way for the development of a new paradigm in information processing. Here we give an overview of the most recent proposals aimed at bringing together these ongoing revolutions, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures. We highlight the exciting perspectives in this context and discuss the potential role of near term quantum hardware in the quest for quantum machine learning advantage.

quant-ph↗

Variational learning for quantum artificial neural networks

In the last few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systems can be realized and programmed. The rapidly growing field of Quantum Machine Learning aims at bringing together these two ongoing revolutions. Here we first review a series of recent works describing the implementation of artificial neurons and feed-forward neural networks on quantum processors. We then present an original realization of efficient individual quantum nodes based on variational unsampling protocols. We investigate different learning strategies involving global and local layer-wise cost functions, and we assess their performances also in the presence of statistical measurement noise. While keeping full compatibility with the overall memory-efficient feed-forward architecture, our constructions effectively reduce the quantum circuit depth required to determine the activation probability of single neurons upon input of the relevant data-encoding quantum states. This suggests a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware.

quant-ph↗

Characterization of photon pairs generated by a silicon ring resonator under electrical self-pumping

We report on the generation of nonclassical states of light in a silicon ring resonator in a selfpumping scheme. The ring is inserted in a lasing cavity, for which it acts as a filter, so that the lasing always occurs within a selected ring resonance, without active stabilization of the resonance frequency. We show the emission of coincident photon pairs and study their correlation properties through the reconstruction of the measurements via stimulated emission.

quant-ph↗

Quantum computing model of an artificial neuron with continuously valued input data

Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration or pattern recognition. The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector. Here we show how the design for the implementation of a previously introduced quantum artificial neuron [npj Quant. Inf. $\textbf{5}$, 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous -- instead of discrete-valued input vectors, without increasing the number of qubits. This further step is crucial to allow for a direct application of an automatic differentiation learning procedure, which would not be compatible with binary-valued data encoding.

quant-ph↗

The Excitation Ladder of Cavity Polaritons

Multidimensional coherent spectroscopy directly unravels multiply excited states that overlap in a linear spectrum. We report multidimensional coherent optical photocurrent spectroscopy in a semiconductor polariton diode and explore the excitation ladder of cavity polaritons. We measure doubly and triply avoided crossings for pairs and triplets of exciton-polaritons, demonstrating the strong coupling between light and dressed doublet and triplet semiconductor excitations. These results demonstrate that multiply excited excitonic states strongly coupled to a microcavity can be described as two coupled quantum-anharmonic ladders.

cond-mat.mes-hall↗

Quantum implementation of an artificial feed-forward neural network

Artificial intelligence algorithms largely build on multi-layered neural networks. Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run. Quantum computing promises to solve certain tasks much more efficiently than any classical computing machine, and actual quantum processors are now becoming available through cloud access to perform experiments and testing also outside of research labs. Here we show in practice an experimental realization of an artificial feed-forward neural network implemented on a state-of-art superconducting quantum processor using up to 7 active qubits. The network is made of quantum artificial neurons, which individually display a potential advantage in storage capacity with respect to their classical counterpart, and it is able to carry out an elementary classification task which would be impossible to achieve with a single node. We demonstrate that this network can be equivalently operated either via classical control or in a completely coherent fashion, thus opening the way to hybrid as well as fully quantum solutions for artificial intelligence to be run on near-term intermediate-scale quantum hardware.

quant-ph↗

An Artificial Neuron Implemented on an Actual Quantum Processor

Artificial neural networks are the heart of machine learning algorithms and artificial intelligence protocols. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt's `perceptron', but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a perceptron, which shows exponential advantage in encoding resources over alternative realizations. We experimentally test a few qubits version of this model on an actual small-scale quantum processor, which gives remarkably good answers against the expected results. We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns, as a first step towards practical training of artificial quantum neural networks to be efficiently implemented on near-term quantum processing hardware.

quant-ph↗

Nonlinear characterisation of a silicon integrated Bragg waveguide filter

Bragg waveguides are promising optical filters for pump suppression in spontaneous Four-Wave Mixing (FWM) photon sources. In this work, we investigate the generation of unwanted photon pairs in the filter itself. We do this by taking advantage of the relation between spontaneous and classical FWM, which allows for the precise characterisation of the nonlinear response of the device. The pair generation rate estimated from the classical measurement is compared with the theoretical value calculated by means of a full quantum model of the filter, which also allows to investigate the spectral properties of the generated pairs. We find a good agreement between theory and experiment, confirming that stimulated FWM is a valuable approach to characterise the nonlinear response of an integrated filter, and that the pairs generated in a Bragg waveguide are not a serious issue for the operation of a fully integrated nonclassical source.

quant-ph↗

Chip-based photon quantum state sources using nonlinear optics

The ability to generate complex optical photon states involving entanglement between multiple optical modes is not only critical to advancing our understanding of quantum mechanics but will play a key role in generating many applications in quantum technologies. These include quantum communications, computation, imaging, microscopy and many other novel technologies that are constantly being proposed. However, approaches to generating parallel multiple, customisable bi- and multi-entangled quantum bits (qubits) on a chip are still in the early stages of development. Here, we review recent developments in the realisation of integrated sources of photonic quantum states, focusing on approaches based on nonlinear optics that are compatible with contemporary optical fibre telecommunications and quantum memory infrastructures as well as with chip-scale semiconductor technology. These new and exciting platforms hold the promise of compact, low-cost, scalable and practical implementations of sources for the generation and manipulation of complex quantum optical states on a chip, which will play a major role in bringing quantum technologies out of the laboratory and into the real world.

physics.optics↗

Energy correlations of photon pairs generated by a silicon microring resonator probed by Stimulated Four Wave Mixing

Compact silicon integrated devices, such as micro-ring resonators, have recently been demonstrated as efficient sources of quantum correlated photon pairs. The mass production of integrated devices demands the implementation of fast and reliable techniques to monitor the device performances. In the case of time-energy correlations, this is particularly challenging, as it requires high spectral resolution that is not currently achievable in coincidence measurements. Here we reconstruct the joint spectral density of photons pairs generated by spontaneous four-wave mixing in a silicon ring resonator by studying the corresponding stimulated process, namely stimulated four wave mixing. We show that this approach, featuring high spectral resolution and short measurement times, allows one to discriminate between nearly-uncorrelated and highly-correlated photon pairs.

quant-ph↗

An integrated source of spectrally filtered correlated photons for large scale quantum photonic systems

We demonstrate the generation of quantum-correlated photon-pairs combined with the spectral filtering of the pump field by more than 95dB using Bragg reflectors and electrically tunable ring resonators. Moreover, we perform demultiplexing and routing of signal and idler photons after transferring them via a fiber to a second identical chip. Non-classical two-photon temporal correlations with a coincidence-to-accidental ratio of 50 are measured without further off-chip filtering. Our system, fabricated with high yield and reproducibility in a CMOS process, paves the way toward truly large-scale quantum photonic circuits by allowing sources and detectors of single photons to be integrated on the same chip.

quant-ph↗

A micrometer-scale integrated silicon source of time-energy entangled photons

Entanglement is a fundamental resource in quantum information processing. Several studies have explored the integration of sources of entangled states on a silicon chip but the sources demonstrated so far require millimeter lengths and pump powers of the order of hundreds of mWs to produce an appreciable photon flux, hindering their scalability and dense integration. Microring resonators have been shown to be efficient sources of photon pairs, but entangled state emission has never been demonstrated. Here we report the first demonstration of a microring resonator capable of emitting time-energy entangled photons. We use a Franson experiment to show a violation of Bell's inequality by as much as 11 standard deviations. The source is integrated on a silicon chip, operates at sub-mW pump power, emits in the telecom band with a pair generation rate exceeding 10$^7$ Hz per $nm$, and outputs into a photonic waveguide. These are all essential features of an entangled states emitter for a quantum photonic networks.

quant-ph↗