arXiv subjects

Andrea Pasquale

Publications and source records attributed to Andrea Pasquale.

14 recordsLinked to original sources

arXiv2026

Qibocal: an open-source framework for calibration of self-hosted quantum devices

Calibration of quantum devices is fundamental to successfully deploy quantum algorithms on current available quantum hardware. We present Qibocal, an open-source software library to perform calibration and characterization of superconducting quantum devices within the Qibo framework. Qibocal completes the Qibo middleware framework by providing all necessary tools to easily (re)calibrate self-hosted quantum platforms. After presenting the layout and the features of the library, we give an overview on some of the protocols implemented to perform single and two-qubit gates calibration. Finally, we present applications involving recalibration and monitoring of superconducting platforms.

quant-ph↗

arXiv2025

Double-bracket quantum algorithms for high-fidelity ground state preparation

Ground state preparation is a central application for quantum computers but remains challenging in practice. In this work, we quantitatively investigate the performance and gate counts of double-bracket quantum algorithms (DBQAs) for ground state preparation. We propose a practical strategy in which DBQAs refine initial state preparation circuits, and we compile them for Heisenberg chains using controlled-Z and single-qubit gates. Warm-started DBQAs consistently improve both the energy and ground-state fidelity relative to the initial states provided by variational ansätze, indicating that DBQAs offer an effective unitary synthesis method. To demonstrate compatibility with near-term hardware, we executed a proof-of-concept example on IBM devices. With error mitigation, we observed a statistically significant improvement over the corresponding warm-start circuit. Furthermore, numerical emulations for the same system size indicate that executing DBQAs on Quantinuum's hardware could achieve similar cost-function gains without requiring error mitigation. These findings suggest that DBQAs are a promising approach for enhancing ground-state approximations on near-term quantum devices.

quant-ph↗

arXiv2025

Qiboml: towards the orchestration of quantum-classical machine learning

We present Qiboml, an open-source software library for orchestrating quantum and classical components in hybrid machine learning workflows. Building on Qibo's quantum computing capabilities and integrating with popular machine learning frameworks such as TensorFlow and PyTorch, Qiboml enables the construction of quantum and hybrid models that can run on a broad range of backends: (i) multi-threaded CPUs, GPUs, and multi-GPU systems for simulation with statevector or tensor network methods; (ii) quantum processing units, both on-premise and through cloud providers. In this paper, we showcase its functionalities, including diverse simulation options, noise-aware simulations, and real-time error mitigation and calibration.

quant-ph↗

arXiv2024

Strategies for optimizing double-bracket quantum algorithms

Recently double-bracket quantum algorithms have been proposed as a way to compile circuits for approximating eigenstates. Physically, they consist of appropriately composing evolutions under an input Hamiltonian together with diagonal evolutions. Here, we present strategies to optimize the choice of the double-bracket evolutions to enhance the diagonalization efficiency. This can be done by finding optimal generators and durations of the evolutions. We present numerical results regarding the preparation of double-bracket iterations, both in ideal cases where the algorithm's setup provides analytical convergence guarantees and in more heuristic cases, where we use an adaptive and variational approach to optimize the generators of the evolutions. As an example, we discuss the efficacy of these optimization strategies when considering a spin-chain Hamiltonian as the target. To propose algorithms that can be executed starting today, fully aware of the limitations of the quantum technologies at our disposal, we finally present a selection of diagonal evolution parametrizations that can be directly compiled into CNOTs and single-qubit rotation gates. We discuss the advantages and limitations of this compilation and propose a way to take advantage of this approach when used in synergy with other existing methods.

quant-ph↗

arXiv2024

An open-source framework for quantum hardware control

The development of quantum computers needs reliable quantum hardware and tailored software for controlling electronics specific to various quantum platforms. Middleware is a type of computer software program that aims to provide standardized software tools across the entire pipeline, from high level execution of quantum computing algorithms to low level driver instructions tailored to specific experimental setups, including instruments. This paper presents updates to Qibolab, a software library that leverages Qibo capabilities to execute quantum algorithms on self hosted quantum hardware platforms. Qibolab offers an application programming interface (API) for instrument control through arbitrary pulses and driver operations including sweepers. This paper offers an overview of the new features implemented in Qibolab, including the redefined boundaries between platform and channel classes, the integration of an emulator for simulating quantum hardware behaviour, and it shows updated execution times benchmarks for superconducting single qubit calibration routines.

quant-ph↗

arXiv2024

Beyond full statevector simulation with Qibo

In this proceedings, we present two new quantum circuit simulation protocols recently added as optional backends to Qibo, an open-source framework for quantum simulation, hardware control and calibration. We describe the current status of the framework as for version 0.2.9. In detail, the two new backends for Clifford and tensor networks simulation are presented and benchmarked against the state-of-the-art.

quant-ph↗

arXiv2024

Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting

In this paper we report the use of superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, like the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on the single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favouring applications like axion dark matter searches.

quant-ph↗

arXiv2024

Qibolab: an open-source hybrid quantum operating system

We present Qibolab, an open-source software library for quantum hardware control integrated with the Qibo quantum computing middleware framework. Qibolab provides the software layer required to automatically execute circuit-based algorithms on custom self-hosted quantum hardware platforms. We introduce a set of objects designed to provide programmatic access to quantum control through pulses-oriented drivers for instruments, transpilers and optimization algorithms. Qibolab enables experimentalists and developers to delegate all complex aspects of hardware implementation to the library so they can standardize the deployment of quantum computing algorithms in a extensible hardware-agnostic way, using superconducting qubits as the first officially supported quantum technology. We first describe the status of all components of the library, then we show examples of control setup for superconducting qubits platforms. Finally, we present successful application results related to circuit-based algorithms.

quant-ph↗

arXiv2024

Product Jacobi-Theta Boltzmann machines with score matching

The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques. Successful applications can be obtained using models inspired by the Boltzmann machine (BM) architecture. In this manuscript, the product Jacobi-Theta Boltzmann machine (pJTBM) is introduced as a restricted version of the Riemann-Theta Boltzmann machine (RTBM) with diagonal hidden sector connection matrix. We show that score matching, based on the Fisher divergence, can be used to fit probability densities with the pJTBM more efficiently than with the original RTBM.

stat.ML↗

arXiv2024

Towards an open-source framework to perform quantum calibration and characterization

In this proceedings we present Qibocal, an open-source software package for calibration and characterization of quantum processing units (QPUs) based on the Qibo framework. Qibocal is specifically designed for self-hosted QPUs and provides the groundwork to easily develop, deploy and distribute characterization and calibration routines for all levels of hardware abstraction. Qibocal is based on a modular QPU platform agnostic approach and it provides a general purpose toolkit for superconducting qubits with the possibility of extensions to other quantum technologies. After motivating the need for such a module, we explain the program's flow and show examples of actual use for QPU calibration. We also showcase additional features provided by the library including automatic report generation and live plotting.

quant-ph↗

arXiv2023

Benchmarking machine learning models for quantum state classification

Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits. Current physical realizations of qubits require a careful calibration, composed by different experiments, due to noise and decoherence phenomena. Among the different characterization experiments, a crucial step is to develop a model to classify the measured state by discriminating the ground state from the excited state. In this proceedings we benchmark multiple classification techniques applied to real quantum devices.

quant-ph↗

arXiv2022

A quantum analytical Adam descent through parameter shift rule using Qibo

In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm. We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework. After numerically testing the implementation using quantum simulation on classical hardware, we perform successfully a full quantum hardware optimization exercise using a single superconducting qubit chip controlled by Qibo. We show results for a quantum regression model by comparing simulation to real hardware optimization.

quant-ph↗

arXiv2022

Quantum simulation with just-in-time compilation

Quantum technologies are moving towards the development of novel hardware devices based on quantum bits (qubits). In parallel to the development of quantum devices, efficient simulation tools are needed in order to design and benchmark quantum algorithms and applications before deployment on quantum hardware. In this context, we present a first attempt to perform circuit-based quantum simulation using the just-in-time (JIT) compilation technique on multiple hardware architectures and configurations based on single-node central processing units (CPUs) and graphics processing units (GPUs). One of the major challenges in scientific code development is to balance the level of complexity between algorithms and programming techniques without losing performance or degrading code readability. In this context, we have developed qibojit: a new module for the Qibo quantum computing framework, which uses a just-in-time compilation approach through Python. We perform systematic performance benchmarks between our JIT approach and a subset of relevant publicly available libraries for quantum computing. We show that our novel approach simplifies the complex aspects of the implementation without deteriorating performance.

quant-ph↗

arXiv2022

An open-source modular framework for quantum computing

In this proceedings we describe the current development status and recent technical achievements of Qibo, an open-source framework for quantum simulation. After a concise overview of the project goal, we introduce the modular layout for backend abstraction released in version 0.1.7. We discuss the advantages of each backend choice with particular emphasis on hardware accelerators for quantum state vector simulation. Finally, we summarize the primitives and models currently available.

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