arXiv subjects

Massimiliano Dispenza

Publications and source records attributed to Massimiliano Dispenza.

7 recordsLinked to original sources

arXiv2026

Efficient simulation of noisy entanglement generation

End-to-end entanglement distribution is a key capability of upcoming quantum networks, enabling applications like distributed quantum computing, quantum sensor networks, and secure communications. Hence, its realistic and efficient simulation is crucial for quantum network design and for assessing the ability of a network to run certain applications. This work provides tools to scale-up and improve the realism of entanglement generation simulations in quantum networks. This is achieved by deriving analytical results that directly return the success probability, the output state and corresponding fidelity of a selected entanglement generation protocol, while accounting for a variety of noise sources affecting the protocol. These results are then integrated and streamlined in an upgraded version of SeQUeNCe, one of the most popular quantum network simulators. The resulting simulator features increased scalability by reducing computation time by more than 60%, while allowing for a variety of realistic noise sources, including imperfect mode matching, dark counts, and imperfect memory initialization. The simulator is also benchmarked with real experimental data and is capable of replicating the average entanglement generation time and the final state fidelity of a selected experiment. Altogether, the results can enhance current quantum network simulation capabilities towards large-scale networks, paving the way for the future quantum internet.

quant-ph↗

arXiv2025

Quantum machine learning algorithms for anomaly detection: A review

The advent of quantum computers has justified the development of quantum machine learning algorithms , based on the adaptation of the principles of machine learning to the formalism of qubits. Among such quantum algorithms, anomaly detection represents an important problem crossing several disciplines from cybersecurity, to fraud detection to particle physics. We summarize the key concepts involved in quantum computing, introducing the formal concept of quantum speed up. The review provides a structured map of anomaly detection based on quantum machine learning. We have grouped existing algorithms according to the different learning methods, namely quantum supervised, quantum unsupervised and quantum reinforcement learning, respectively. We provide an estimate of the hardware resources to provide sufficient computational power in the future. The review provides a systematic and compact understanding of the techniques belonging to each category. We eventually provide a discussion on the computational complexity of the learning methods in real application domains.

quant-ph↗

arXiv2025

Quantum machine learning with Adaptive Boson Sampling via post-selection

The implementation of large-scale universal quantum computation represents a challenging and ambitious task on the road to quantum processing of information. In recent years, an intermediate approach has been pursued to demonstrate quantum computational advantage via non-universal computational models. A relevant example for photonic platforms has been provided by the Boson Sampling paradigm and its variants, which are known to be computationally hard while requiring at the same time only the manipulation of the generated photonic resources via linear optics and detection. Beside quantum computational advantage demonstrations, a promising direction towards possibly useful applications can be found in the field of quantum machine learning, considering the currently almost unexplored intermediate scenario between non-adaptive linear optics and universal photonic quantum computation. Here, we report the experimental implementation of quantum machine learning protocols by adding adaptivity via post-selection to a Boson Sampling platform based on universal programmable photonic circuits fabricated via femtosecond laser writing. Our experimental results demonstrate that Adaptive Boson Sampling is a viable route towards dimension-enhanced quantum machine learning with linear optical devices.

quant-ph↗

arXiv2024

Near-single-photon atto-watt detection at mid-infrared wavelengths by a room-temperature balanced heterodyne set-up

Single photon detection is the underpinning technology for quantum communication and quantum sensing applications. At visible and near-infrared wavelengths, single-photon-detectors (SPDs) underwent a significant development in the past two decades, with the commercialization of SPADs and superconducting detectors. At longer wavelengths, in the mid-infrared range (4-11$μ$um), given the reduced scattering and favourable transparent atmospheric windows, there is an interest in developing quantum earth-satellites-links and quantum imaging for noisy environments or large-distance telescopes. Still, SPD-level mid-infrared devices have been rarely reported in the state-of-the-art (superconductors, single-electron-transistors or avalanche-photodiodes) and, crucially, all operating at cryogenic temperatures. Here, we demonstrate a room-temperature detection system operating at 4.6$μ$m-wavelength with a sensitivity-level of atto-watt optical power, corresponding to few tens of mid-infrared photons. This result was obtained by exploiting a pair of commercially available photodetectors within two balanced-heterodyne-detection setups: one involving a quantum-cascade-laser (QCL) and an acousto-optic-modulator (AOM) and the other one including two QCLs with mutual coherence ensured by a phase-lock-loop (PLL). Our work not only validates a viable method to detect ultra-low-intensity signals, but is also potentially scalable to the entire wavelength range already accessible by mature QCL technology, unfolding - for the first time - quantum applications at mid- and long-wave-infrared-radiation.

quant-ph↗

arXiv2024

Phase Noise in Real-World Twin-Field Quantum Key Distribution

The impact of noise sources in real-world implementations of Twin-Field Quantum Key Distribution (TF-QKD) protocols is investigated, focusing on phase noise from photon sources and connecting fibers. This work emphasizes the role of laser quality, network topology, fiber length, arm balance, and detector performance in determining key rates. Remarkably, it reveals that the leading TF-QKD protocols are similarly affected by phase noise despite different mechanisms. This study demonstrates duty cycle improvements of over a factor of two through narrow-linewidth lasers and phase-control techniques, highlighting the potential synergy with high-precision time/frequency distribution services. Ultrastable lasers, evolving toward integration and miniaturization, offer promise for agile TF-QKD implementations on existing networks. Properly addressing phase noise and practical constraints allows for consistent key rate predictions, protocol selection, and layout design, crucial for establishing secure long-haul links for the Quantum Communication Infrastructures under development in several countries.

quant-ph↗

arXiv2024

Simulating photonic devices with noisy optical elements

Quantum computers are inherently affected by noise. While in the long-term error correction codes will account for noise at the cost of increasing physical qubits, in the near-term the performance of any quantum algorithm should be tested and simulated in the presence of noise. As noise acts on the hardware, the classical simulation of a quantum algorithm should not be agnostic on the platform used for the computation. In this work, we apply the recently proposed noisy gates approach to efficiently simulate noisy optical circuits described in the dual rail framework. The evolution of the state vector is simulated directly, without requiring the mapping to the density matrix framework. Notably, we test the method on both the gate-based and measurement-based quantum computing models, showing that the approach is very versatile. We also evaluate the performance of a photonic variational quantum algorithm to solve the MAX-2-CUT problem. In particular we design and simulate an ansatz which is resilient to photon losses up to $p \sim 10^{-3}$ making it relevant for near term applications.

quant-ph↗

arXiv2023

A native measurement-based QAOA algorithm, applied to the MAX K-CUT problem

Photonic quantum computers, programmed within the framework of the measurement-based quantum computing (MBQC), currently concur with gate-based platforms in the race towards useful quantum advantage, and some algorithms emerged as main candidates to reach this goal in the near term. Yet, the majority of these algorithms are only expressed in the gate-based model of computation, which is incompatible with photonic platforms. Methods to translate gate-based algorithms into the MBQC framework exist, but they are not always optimal in terms of resource cost. In our work, we propose an MBQC algorithm to run the Quantum Approximate Optimization Algorithm (QAOA). Furthermore, we apply the MBQC-QAOA algorithm to the MAX $K$-CUT problem, working for all values of $K$, expressing the cost Hamiltonian and its constraints in a form easily implementable in the MBQC model. We conclude analyzing the resource-cost of our algorithm, compared to the case of translating a gate-based QAOA algorithm into MBQC rules showing up to a 30-fold improvement. With our work, we contribute to close the gap between gate-based and MBQC near-term algorithms, a gap not reflecting the current status of the hardware development.

quant-ph↗