As information carriers in quantum computing, photonic qubits have the advantage of undergoing negligible decoherence. However, the absence of any significant photon–photon interaction is problematic for the realization of non-trivial two-qubit gates. One solution is to introduce an effective nonlinearity by measurements resulting in probabilistic gate operations. In one-way quantum computation, the random quantum measurement error can be overcome by applying a feed-forward technique, such that the future measurement basis depends on earlier measurement results. This technique is crucial for achieving deterministic quantum computation once a cluster state (the highly entangled multiparticle state on which one-way quantum computation is based) is prepared. Here we realize a concatenated scheme of measurement and active feed-forward in a one-way quantum computing experiment. We demonstrate that, for a perfect cluster state and no photon loss, our quantum computation scheme would operate with good fidelity and that our feed-forward components function with very high speed and low error for detected photons. With present technology, the individual computational step (in our case the individual feed-forward cycle) can be operated in less than 150 ns using electro-optical modulators. This is an important result for the future development of one-way quantum computers, whose large-scale implementation will depend on advances in the production and detection of the required highly entangled cluster states.

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High-speed linear optics quantum computing via active feed-forward

The scale-up of quantum hardware is fundamental to realize the full potential of quantum technology. Among a plethora of hardware platforms, photonics stands out: it provides a modular approach where the main challenges lie in the construction of high-quality building blocks and in the development of methods to interface the modules. The subsequent scale-up could exploit mature integrated photonics foundry technology to produce small-footprint quantum processors of immense complexity. Solid-state quantum emitters can realize a deterministic photon–emitter interface and enable key quantum photonic resources and functionalities, including on-demand single- and multi-photon-entanglement sources, as well as photon–photon nonlinear quantum gates. In this Review, we use the example of quantum dot devices to present the physics of deterministic photon–emitter interfaces, including the main photonic building blocks required to scale up, and discuss quantitative performance benchmarks. While our focus is on quantum dot devices, the presented methods also apply to other quantum-emitter platforms such as atoms, vacancy centres, molecules and superconducting qubits. We also identify applications within quantum communication and computing, presenting a route towards photonics with a genuine quantum advantage. Quantum photonics offers a scalable approach to advanced quantum-information processing. Based on deterministic photon–emitter interfaces, this Review presents a road ahead for resource-efficient hardware architectures towards applications in quantum communication and quantum computing.

Quantum-dot-based deterministic photon-emitter interfaces for scalable photonic quantum technology.

Laser has been demonstrated to be a mature and versatile tool that presents great flexibility and applicability for the precision engineering of a wide range of materials over other established micromachining techniques. Past decades have witnessed its rapid development and extensive applications ranging from scientific researches to industrial manufacturing. Transparent hard materials remain several major technical challenges for conventional laser processing techniques due to their high hardness, great brittleness, and low optical absorption. A variety of hybrid laser processing technologies, such as laser-induced plasma-assisted ablation, laser-induced backside wet etching, and etching assisted laser micromachining, have been developed to overcome these barriers by introducing additional medium assistance or combining different process steps. This article reviews the basic principles and characteristics of these hybrid technologies. How these technologies are used to precisely process transparent hard materials and their recent advancements are introduced. These hybrid technologies show remarkable benefits in terms of efficiency, accuracy, and quality for the fabrication of microstructures and functional devices on the surface of or inside the transparent hard substrates, thus enabling widespread applications in the fields of microelectronics, bio-medicine, photonics, and microfluidics. A summary and outlook of the hybrid laser technologies are also highlighted.

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Hybrid laser precision engineering of transparent hard materials: challenges, solutions and applications.

Integrated quantum photonics, i.e. the generation, manipulation and detection of quantum states of light in integrated photonic chips, is revolutionizing the field of quantum information in all applications, from communications to computing. Although many different platforms are being currently developed, from silicon photonics to lithium niobate photonic circuits, none of them has shown the versatility of femtosecond laser micromachining (FLM) in producing all the components of a complete quantum system, encompassing quantum sources, reconfigurable state manipulation, quantum memories and detection. It is in fact evident that FLM has been a key enabling tool in the first-time demonstration of many quantum devices and functionalities. Although FLM cannot achieve the same level of miniaturization of other platforms, it still has many unique advantages for integrated quantum photonics. In particular, in the last five years, FLM has greatly expanded its range of quantum applications with several scientific breakthroughs achieved. For these reasons, we believe that a review article on this topic is very timely and could further promote the development of this field by convincing end-users of the great potentials of this technological platform and by stimulating more research groups in FLM to direct their efforts to the exciting field of quantum technologies.

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Femtosecond laser micromachining for integrated quantum photonics

Abstract Time-series processing is a major challenge in machine learning with enormous progress in the last years in tasks such as speech recognition and chaotic series prediction. A promising avenue for sequential data analysis is quantum machine learning, with computational models like quantum neural networks and reservoir computing. An open question is how to efficiently include quantum measurement in realistic protocols while retaining the needed processing memory and preserving the quantum advantage offered by large Hilbert spaces. In this work, we propose different measurement protocols and assess their efficiency in terms of resources, through theoretical predictions and numerical analysis. We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance both for memory and forecasting tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement while the other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory, in spite of back-action effects. Our work establishes the conditions for efficient time-series processing paving the way to its implementation in different quantum technologies. 

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Time-series quantum reservoir computing with weak and projective measurements

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures. 

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Experimental photonic quantum memristor

In this work we demonstrate novel integrated-optics modulators and switches, realized in a glass substrate by femtosecond laser pulses These devices are based on oscillating microcantilevers, machined by water-assisted laser ablation Single-mode optical waveguides are laser-inscribed inside the cantilever beam and continue in the substrate beyond the cantilever's tip By exciting the resonant oscillation of the mechanical structure, coupling between the waveguide segments is varied in time Operation frequencies are in the range of tens of kilohertz, thus they markedly overcome the response-time limitation of other glass-based modulators, which rely on the thermo-optic effect These components may be integrated in more complex waveguide circuits or optofluidic lab-on-chips, to provide periodic and high-frequency modulation of the optical signals

Resonant opto-mechanical modulators and switches by femtosecond laser micromachining

Both photonic quantum computation and the establishment of a quantum internet require fiber-based measurement and feed-forward in order to be compatible with existing infrastructure. Here we present a fiber-compatible scheme for measurement and feed-forward, whose performance is benchmarked by carrying out remote preparation of single-photon polarization states at telecom-wavelengths. The result of a projective measurement on one photon deterministically controls the path a second photon takes with ultrafast optical switches. By placing well-calibrated {bulk} passive polarization optics in the paths, we achieve a measurement and feed-forward fidelity of (99.0 $\pm$ 1)%, after correcting for other experimental errors. Our methods are useful for photonic quantum experiments including computing, communication, and teleportation.

Fiber-compatible photonic feed-forward with 99% fidelity

Both photonic quantum computation and the establishment of a quantum internet require fiber-based measurement and feed-forward in order to be compatible with existing infrastructure. Here we present an all-fiber scheme for measurement and feed-forward, whose performance is benchmarked by carrying out remote preparation of single-photon polarization states at telecom-wavelengths. The result of a projective measurement on one photon deterministically controls the path a second photon takes with ultrafast optical switches. By placing well-calibrated passive polarization optics in the paths, we achieve a measurement and feed-forward fidelity of (99.0$\pm$ 0.5)%. Our methods are useful for photonic quantum experiments including computing, communication, and teleportation.

Fiber-based photonic feed-forward with 99% fidelity

Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

Michele Spagnolo

Papers

Experimental photonic quantum memristor

Resonant opto-mechanical modulators and switches by femtosecond laser micromachining

Fiber-compatible photonic feed-forward with 99% fidelity

Fiber-based photonic feed-forward with 99% fidelity

Experimental quantum memristor