arXiv subjects

Sebastiano Corli

Publications and source records attributed to Sebastiano Corli.

10 recordsLinked to original sources

arXiv2026

A Simple and Robust Balanced Homodyne Detector for High-Repetition-Rate Pulsed Sources

We design and experimentally characterize a balanced homodyne detector optimized for high-repetition-rate (100 MHz) pulsed optical sources. Unlike conventional transimpedance-amplifier architectures, which suffer from nonlinearities and dynamic instabilities with ultrashort pulses, our approach allows to directly amplify the photocurrent extracted at the common photodiode node without feedback loops. A theoretical model describing the detector response, noise, and pulse-to-pulse correlations is developed, providing quantitative predictions for the signal variance, signal-to-noise ratio (SNR), and inter-pulse correlations. Implemented with two matched InGaAs photodiodes illuminated by a 1030 nm mode-locked laser at 100 MHz, the detector exhibits excellent linearity and shot-noise-limited scaling of the signal variance with optical power. Optimizing the temporal integration window yields a maximum SNR of about 14 dB, while correlation measurements confirm negligible inter-pulse correlations. These results demonstrate that the proposed architecture offers a robust and simple solution for high-speed pulsed homodyne detection, suitable for quantum optics and continuous-variable quantum information applications.

physics.optics↗

arXiv2026

Experimental investigation of the effect of dispersion on squeezing generation in a synchronously pumped optical parametric oscillator

An experimental investigation of intracavity dispersion effects in a synchronously pumped optical parametric oscillator (SPOPO) is presented. A flexible setup combining spectral and phase shaping of both pump and local oscillator fields with frequency-resolved balanced homodyne detection is employed to examine how intracavity dispersion influences squeezing. Different cavity configurations with varying finesse and dispersion conditions are studied, and the squeezing is measured as a function of pump power and local oscillator bandwidth. Contrary to expectations based on existing theoretical models, the measured squeezing levels remain essentially unchanged as dispersion varies. To account for these observations, a modeling approach is introduced in which intracavity dispersion is described as an effective spectral filtering occurring at the stage of SPOPO supermode generation. Within this framework, the filtering is incorporated directly into the interaction Hamiltonian of the nonlinear process. This perspective establishes a consistent experimental benchmark for the study of dispersion in SPOPOs and underscores the importance of spectral filtering in the interpretation of multimode squeezing experiments.

physics.optics↗

arXiv2025

Transpiling quantum circuits by a transformers-based algorithm

Transformers have gained popularity in machine learning due to their application in the field of natural language processing. They manipulate and process text efficiently, capturing long-range dependencies among data and performing the next word prediction. On the other hand, gate-based quantum computing is based on controlling the register of qubits in the quantum hardware by applying a sequence of gates, a process which can be interpreted as a low level text programming language. We develop a transformer model capable of transpiling quantum circuits from the qasm standard to other sets of gates native suited for a specific target quantum hardware, in our case the set for the trapped-ion quantum computers of IonQ. The feasibility of a translation up to five qubits is demonstrated with a percentage of correctly transpiled target circuits equal or superior to 99.98%. Regardless the depth of the register and the number of gates applied, we prove that the complexity of the transformer model scales, in the worst case scenario, with a polynomial trend by increasing the depth of the register and the length of the circuit, allowing models with a higher number of parameters to be efficiently trained on HPC infrastructures.

quant-ph↗

arXiv2025

Generation and detection of squeezed states via a synchronously pumped optical parametric oscillator

A synchronously pumped optical parametric oscillator (SPOPO) operating at 93 MHz is used to generate squeezed states at 1035 nm. The system features a counter-propagating beam at the same wavelength as the quantum state, which simultaneously actively stabilizes the cavity and, after transmission, acts as the local oscillator for homodyne detection. By deriving the local oscillator directly from the SPOPO cavity, the setup establishes an intrinsically excellent spatial mode overlap and high interference visibility, forming a distinctive self-referenced architecture. Two spatial light modulators enable precise spectral shaping of both the pump and the local oscillator in amplitude and phase, allowing investigation of the spectral properties of the generated states. The versatility of the setup further allows exploration of different SPOPO configurations, including regimes with varied finesse and escape efficiency. Representative measurements, including homodyne traces and squeezing levels as functions of pump power and local oscillator bandwidth, demonstrate the performance of the system. Theoretical simulations based on a multimode singular-value-decomposition model reproduce well the measured dependence of squeezing on pump power and LO bandwidth, confirming the accuracy of the description and the robustness of the setup. Measured squeezing levels up to -3.3 dB are achieved, corresponding to -5.7 dB at SPOPO output, evidencing the robustness and versatility of this platform for stable pulsed squeezed-light generation and advanced quantum optical applications.

quant-ph↗

arXiv2025

A minimalist self-differencing gating scheme for dead-time-free single-photon avalanche diodes at high repetition rate

Gated quenched SPAD detectors are widely used in quantum communication and quantum computing setups employing high-repetition-rate lasers. Here, we present a novel scheme for high-repetition-rate (100 MHz) sine-wave gated SPADs, based on the self-differencing technique, which significantly simplifies previous designs while offering additional advantages. These include straightforward implementation, more precise control of the SPAD biasing, and an improved SNR. We implemented this approach using an InGaAs photodiode and characterized it experimentally with 100 MHz attenuated laser pulses, measuring quantum efficiency, dark count rate, and afterpulsing behavior. Importantly, we demonstrate that the detector recovers full quantum efficiency in less than one pulse-repetition period after a detection event, enabling continuous operation at 100 MHz, which, in principle, could reach the GHz regime.

quant-ph↗

arXiv2025

Quantum physics informed neural networks for multi-variable partial differential equations

Quantum Physics-Informed Neural Networks (QPINNs) integrate quantum computing and machine learning to impose physical biases on the output of a quantum neural network, aiming to either solve or discover differential equations. The approach has recently been implemented on both the gate model and continuous variable quantum computing architecture, where it has been demonstrated capable of solving ordinary differential equations. Here, we aim to extend the method to effectively address a wider range of equations, such as the Poisson equation and the heat equation. To achieve this goal, we introduce an architecture specifically designed to compute second-order (and higher-order) derivatives without relying on nested automatic differentiation methods. This approach mitigates the unwanted side effects associated with nested gradients in simulations, paving the way for more efficient and accurate implementations. By leveraging such an approach, the quantum circuit addresses partial differential equations, a challenge not yet tackled using this approach on continuous-variable quantum computers. As a proof-of-concept, we solve a one-dimensional instance of the heat equation, demonstrating its effectiveness in handling PDEs, both in an ideal and a noisy regime. We report our experiment on a photonic hardware to address a realistic noise scenario for our simulations. Such a framework paves the way for further developments in continuous-variable quantum computing and underscores its potential contributions to advancing quantum machine learning.

quant-ph↗

arXiv2024

Measurement-Based Quantum Compiling via Gauge Invariance

The measurement-based architecture is a paradigm of quantum computing, relying on the entanglement of a cluster of qubits and the measurements of a subset of it, conditioning the state of the unmeasured output qubits. While methods to map the gate model circuits into the measurement-based are already available via intermediate steps, we introduce a new paradigm for quantum compiling directly converting any quantum circuit to a class of graph states, independently from its size. Such method relies on the stabilizer formalism to describe the register of the input qubits. An equivalence class between graph states able to implement the same circuit is defined, giving rise to a gauge freedom when compiling in the MBQC frame. The graph state can be rebuilt from the circuit and the input by employing a set of graphical rules similar to the Feynman's ones. A system of equations describes the overall process. Compared to Measurement Calculus, the ancillary qubits are reduced by 50% on QFT and 75% on QAOA.

quant-ph↗

arXiv2024

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↗

arXiv2022

An efficient algebraic representation for graph states for measurement-based quantum computing

Graph states are the main computational building blocks of measurement-based computation and a useful tool for error correction in the gate model architecture. The graph states form a class of quantum states which are eigenvectors for the abelian group of stabilizer operators. They own topological properties, arising from their graph structure, including the presence of highly connected nodes, called hubs. Starting from hub nodes, we demonstrate how to efficiently express a graph state through the generators of the stabilizer group. We provide examples by expressing the ring and the star topology, for which the number of stabilizers reduces from n to n/2, and from n to 1, respectively. We demonstrate that the graph states can be generated by a subgroup of the stabilizer group. Therefore, we provide an algebraic framework to manipulate the graph states with a reduced number of stabilizers.

quant-ph↗

arXiv2021

Solving Rubik's Cube via Quantum Mechanics and Deep Reinforcement Learning

Rubik's Cube is one of the most famous combinatorial puzzles involving nearly $4.3 \times 10^{19}$ possible configurations. Its mathematical description is expressed by the Rubik's group, whose elements define how its layers rotate. We develop a unitary representation of such group and a quantum formalism to describe the Cube from its geometrical constraints. Cubies are describedby single particle states which turn out to behave like bosons for corners and fermions for edges, respectively. When in its solved configuration, the Cube, as a geometrical object, shows symmetrieswhich are broken when driven away from this configuration. For each of such symmetries, we build a Hamiltonian operator. When a Hamiltonian lies in its ground state, the respective symmetry of the Cube is preserved. When all such symmetries are preserved, the configuration of the Cube matches the solution of the game. To reach the ground state of all the Hamiltonian operators, we make use of a Deep Reinforcement Learning algorithm based on a Hamiltonian reward. The Cube is solved in four phases, all based on a respective Hamiltonian reward based on its spectrum, inspired by the Ising model. Embedding combinatorial problems into the quantum mechanics formalism suggests new possible algorithms and future implementations on quantum hardware.

quant-ph↗