I will introduce a framework to represent and optimize parametric quantum channels for quantum computing tasks, discussing the current status of research in non-unitary computation protocols and some of their applications to the investigation of quantum lattice systems.
Generalizability is a fundamental property for machine learning algorithms, detected by a grokking transition during training dynamics. In the quantum-inspired machine learning framework we numerically prove that a quantum many-body system shows an entanglement transition corresponding to a performances improvement in binary classification of unseen data. Two datasets are considered as use...
We present Qibo, an open-source quantum computing framework offering a full-stack solution for efficient deployment of quantum algorithms and calibration routines on quantum hardware.
Quantum computers require compilation of high-level circuits tailored to specific chip architectures and integration with control electronics. Our framework tackles these challenges through Qibolab, a versatile...
This work presents a novel machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. By leveraging reinforcement learning, we train an agent to introduce noise channels that accurately mimic specific noise patterns. The proposed noise characterization method has been tested on simulations for small quantum circuits, where it con- sistently...
Quantum Neural Networks hold great promise for addressing computational challenges, but noise in near-term quantum devices remains a significant obstacle for circuit depth. In this work, we propose a preliminary study on a novel noise mitigation strategy based on early exit, traditionally used in classical deep learning to improve computational efficiency. Experiments have been conducted on a...
Quantum machine learning models based on parameterized quantum circuits have attracted significant attention as early applications for current noisy quantum processors. While the advantage of such algorithms over classical counterparts in practical learning tasks is yet to be demonstrated, learning distributions generated by quantum systems, which are inherently quantum, is a promising avenue...
Variational quantum computing provides a versatile computational approach, applicable to a wide range of fields such as quantum chemistry, machine learning, and optimization problems. However, scaling up the optimization of quantum circuits encounters a significant hurdle due to the exponential concentration of the loss function, often dubbed the barren plateau (BP) phenomenon.
Although...
Machine Learning (ML) techniques for background event rejection in Liquid Argon Time Projection Chambers (LArTPCs) have been extensively studied for various physics channels [1,2], yielding promising results. In this contribution, we highlight the performance of Quantum Machine Learning (QML)-based background mitigation strategies to enhance the sensitivity of kton-scale LArTPCs for rare event...
Lithium niobate is a leading material for integrated optics for quantum and classical applications. Because of its nonlinearity, it supports the fabrication of electro-optical devices for quantum state generation and manipulation. Using this material platform, I will show our experimental results on the generation of squeezed vacuum state on chip, frequency conversion of single photons, and...
Loss-tolerant quantum codes (LTCs) are particular error-correcting codes, essential for safeguarding quantum information against physical qubit losses, with significant applications in quantum communication, as well as in quantum computation, where photons can connect various modules of a computer, facilitate remote computation, or even act as the fundamental units of all-photonic processors....
Compilation optimizes quantum algorithms performances on real-world quantum computers. To date, it is performed via classical optimization strategies, but its NP-hard nature makes finding optimal solutions difficult. We introduce a class of quantum algorithms to perform compilation via quantum computers, paving the way for a quantum advantage in compilation. We demonstrate the effectiveness of...
Individually trapped neutral atoms offer a promising path for engineering controllable many-body quantum systems: coherent manipulation has been demonstrated for arrays featuring hundreds of atoms, encouraging to envision atom-based quantum processors.
In this talk, I will present a novel approach to use Rydberg atom arrays as platforms for quantum information processing. Our model has...
Quantum Fuzzy Logic integrates two distinct mathematical frameworks—quantum computing and fuzzy logic—both of which fundamentally deal with uncertainty and imprecision. Quantum mechanics inherently involves uncertainty through its stochasticity, while fuzzy logic addresses vagueness in reasoning and decision-making processes, allowing for degrees of truth rather than binary true/false values....
Tensor network methods have emerged as powerful tools for addressing complex challenges in quantum science, particularly supporting advances in quantum computing and technologies. In this talk, I will discuss recent developments in tensor network techniques across various domains. First, I will highlight how the integration of hyper-optimized contraction protocols into tensor network...
Quantum computers are a promising platforms to efficiently simulate systems hard to tackle on classical machines. An important challenge to overcome is the efficient control of errors that, if left undisturbed, make quantum simulations useless. A solution to this challenge is quantum error correction, that exploiting redundancy is able to correct errors. In this talk I will explore the...
Quantum many-body scarring (QMBS) is an intriguing mechanism of ergodicity breaking that has recently spurred significant attention. Particularly prominent in Abelian lattice gauge theories (LGTs), an open question is whether QMBS nontrivially arises in non-Abelian LGTs. Here, we present evidence of robust QMBS in a non-Abelian SU(2) LGT with dynamical matter. Starting in product states that...
We show that a viable route to generate strongly-interacting chiral phases can exploit the interplay between onsite interactions and flux frustration for bosons in dimerized lattices with pi-flux. By constructing an effective theory, we demonstrate how this setting favours the spontaneous breaking of time-reversal symmetry. This can lead to the realization of the long-sought chiral Mott...
The homogeneous Bethe-Salpeter equation (hBSE) [1], which models a bound system within a fully relativistic quantum field theory, has been solved for the first time using a D-Wave quantum annealer [2]. Following standard discretization methods, the hBSE in the ladder approximation can be reformulated as a generalized eigenvalue problem (GEVP) involving two square matrices, one symmetric and...
Classical shadows are a versatile tool to probe many-qubit quantum systems, consisting of a
combination of randomised measurements and classical post-processing computations. In a recently
introduced version of the protocol, the randomization step is performed via unitary circuits of
variable depth t, defining the so-called shallow shadows. For sufficiently large t, this approach...
Simulating the low-temperature properties of frustrated quantum Ising models is a paradigmatic problem in condensed matter physics. It has recently gained strong interest in the context of quantum-enhanced optimization performed via quantum annealers and of quantum simulation in Rydberg-atom experiments.
We use a recently-developed self-learning projection quantum Monte Carlo algorithm...
Tracking charged particles in high-energy physics experiments is one of the most computationally demanding steps in the data analysis pipeline.
As we approach the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, particle tracking will become even more problematic due to the massive increase in the volume of data...
I will present a combination of different results obtained by my group in the last few years, about quantifying the complexity of learning with quantum data, such as quantum states, quantum dynamics and quantum channels. Example applications include the classification of quantum phases of matter, which are encoded into ground states of quantum many-particle systems, decision problems such as...
We investigate the combined use of quantum computers and classical deep neural networks, considering both quantum annealers and universal gate-based platforms.
In the first case, we show that data produced by D-Wave quantum annealers allow accelerating Monte Carlo simulations of spin glasses through the training of autoregressive neural networks [1].
In the second case, we show that deep...
In recent years, much research has investigated the potentialities of quantum computers, ranging from physical implementations to comparisons with well-known algorithms of classical computation.
I would like to present a quantum algorithm to measure quantities related to scattering theory: reflection and transmission amplitudes of a quantum particle interacting with a short-ranged...
We benchmark Quantum TEA, a simulation framework developed as well with the support of the INFN quantum initiative and INFN infrastructure. Quantum TEA supports both digital, analog, and quantum-inspired quantum simulation on classical hardware. The simulations of many-body quantum systems run on heterogeneous hardware platforms using CPUs, GPUs, and TPUs. We compare different linear algebra...
We present two examples of the application of stochastic calculus in quantum computing.
The first example involves simulating quantum circuits in the presence of noise using classical computers. Instead of directly solving the Lindblad master equation, we utilize its stochastic unravelling to model a random evolution of the state vector. This approach enables us to incorporate noise effects...
Singlet fission (SF) is an electronic transition that in the last decade has been under the spotlight for its applications in optoelectronics, from photovoltaics to spintronics. Despite considerable experimental and theoretical advancements, optimising SF in extended solids remains a challenge, due to the complexity of its analysis beyond perturbative methods. Here, we tackle the case of 1D...
Tensor network methods are a family of numerical techniques that efficiently compress the information of quantum many-body systems while accurately capturing their important physical properties. Here, we present a tensor-network-based toolbox developed for constructing the quantum many-body states at thermal equilibrium. Using this framework, we probe classical correlations as well as...
Bell’s inequality represent a cornerstone in our understanding of quantum theory, as they allow to prove the quantum mechanics is non-local and no hidden variable theory can give the same results.
While known to the community, it is often not highlighted the role of non-stabilizerness, often dubbed magic, in the violation of Bell’s inequalities.
In our work we show how much...
