Valencia Quantum Spain mini-workshop (13 de mayo de 2024)

9:20 → 11:00

Session 1

Moderador: Nathan Harshman (Chair) (American University, Washington DC)

9:20

Welcome 10m

Ponente: Armando Pérez (Armando Pérez (Departamento de Física Teórica & IFIC, Universidad de Valencia-CSIC))
9:30

A Feynman propagator as a qubit 30m

Theoretical predictions at high-energy colliders are based on encoding the quantum fluctuations that occur at very short distances by Feynman diagrams. These diagrams are made up of interaction vertices and propagators, which in fact represent a quantum superposition of propagation in the two directions between two interaction vertices. Therefore, Feynman propagators can be identified with qubits. We analyse the consequences of this interpretation and how it can be exploited to derive a manifestly causal representation of scattering amplitudes, leading to achievable theoretical predictions in quantum field theory at very high perturbative orders.

Ponente: Germán Rodrigo ((IFIC, Universidad de Valencia-CSIC))
10:00

Bell states with three component strongly interacting few-atom systems 30m

We discuss the complete ground-state phase diagram for a one-dimensional ultracold few-atom triple mixture of interacting bosons trapped harmonically as the different coupling constant strengths range from zero to strong repulsive interactions. These results show that there are new appealing ground-state phases with various correlations, coherence and spatial localization stemming from strongly repulsive interactions.We pay particular attention to the regime in which atomic Bell states can be found and manipulated. We discuss their interest as a system valid to implement quantum gates experimentally.

Ponente: Miguel Ángel García-March (UPV)
10:30

Digital-analog quantum convolutional neural networks for image classification 30m

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes.

Ponente: Carlos Flores Garrigós (ETSE, UV)

11:00 → 11:30

Coffee Break 30m

11:30 → 13:00

Session 2

Moderador: Carmen García Almudéver (Chair)

11:30

Error-aware quantum circuit compilation 30m

Reliably executing quantum algorithms on noisy intermediate-scale quantum (NISQ) devices is
challenging, as they are severely constrained and prone to errors. Efficient quantum circuit
compilation techniques are therefore crucial for overcoming their limitations and dealing with
their high error rates. These techniques consider the quantum hardware restrictions, such as the
limited qubit connectivity, and perform some transformations to the original circuit so that it can
be executed on a given quantum processor. Certain compilation methods use error information
based on calibration data to further improve the success probability or the fidelity of the circuit to
be run. However, it is uncertain to what extent incorporating calibration information in the
compilation process can enhance the circuit performance. For instance, considering the most
recent error data provided by vendors after calibrating the processor might not be functional
enough as quantum systems are subject to drift, making the latest calibration data obsolete within
minutes.
In this talk, we will discuss how different usage of calibration data impacts the circuit fidelity, by
using several compilation techniques and quantum processors (IBM Perth and Brisbane). To this
aim, we will present a framework that incorporates some of the state-of-the-art noise-aware and
non-noise-aware compilation techniques and allows the user to perform fair comparisons under
similar processor conditions. Our experiments yield valuable insights into the effects of noise-
aware methodologies and the employment of calibration data. The main finding is that pre-
processing historical calibration data can improve fidelity when real-time calibration data is not
available due to factors such as cloud service latency and waiting queues between compilation
and execution on the quantum backend.

Ponente: Laura Rodríguez (UPV)
12:00

Machine Learning for maximizing the memristivity of single and coupled quantum memristors 30m

We propose machine learning (ML) methods to characterize the memristive properties of single and coupled quantum memristors. We show that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory. Our results strengthen the possibility of using quantum memristors as key components of neuromorphic quantum computing.

Ponente: Carlos Hernani Morales (ETSE, UV)
12:30

Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation 30m

We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the $\mathrm{H_{2}}$ and $\mathrm{LiH}$ molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms.

Ponentes: Antonio Ferrer Sánchez (ETSE, UV), José J. Orquín Marqués

13:00 → 14:30

Lunch Break 1h 30m

14:30 → 16:30

Session 3

Moderador: Bryan Zaldivar (Chair)

14:30

Low-Level mapping techniques for quantum network applications 30m

Quantum networks represent a novel approach for facilitating information
exchange across distant locations through the deployment of various quantum
applications, including Quantum Key Distribution (QKD) and blind comput-
ing. These applications are structured into programs comprising both classical
and quantum code, typically executed in a sequential manner on disparate end
nodes. These end nodes, constituting quantum processors, are composed of
communication and memory qubits that interact via entanglement and classi-
cal communication protocols. Among the candidate architectures for such end
nodes, ion trap systems exhibit promising characteristics owing to their ex-
tended coherence times and precise qubit manipulation capabilities. Nonethe-
less, achieving scalability within a single quantum processor presents a notable
challange, as the introduction of additional qubits within a trap amplifies noise
and heating phenomena, thereby diminishing operational fidelity. To address
this challenge, Trapped-ion Quantum Charge-Coupled Device (QCCD) archi-
tectures have been proposed, which entail the interconnection of multiple traps
alongside the utilization of ion shuttling mechanisms for ion transfer among
traps. This innovative architectural framework necessitates the development
of novel mapping methodologies tailored to quantum algorithms, with a focus
on efficient qubit allocation, routing, and operation scheduling optimized for
quantum network applications. The primary objective of such mapping tech-
niques is to minimize ion movements, thereby reducing circuit execution time
and enhancing overall fidelity.

Ponente: Anabel Ovide (UPV)
15:00

Quantum metrology with finite samples: Generalizing the quantum Cramér-Rao bound 30m

The cornerstone of modern quantum metrology is the quantum Cramér-Rao bound and the quantum Fisher information. Under generic conditions, this bound can be saturated by an optimal estimator and measurement, provided that many repeated measurements on the system are performed. However, in the presence of smaller data sets it typically largely underestimates the error that can actually be achieved. In this talk, we present a family of generalized bounds on the variance of unbiased estimators that are larger than the quantum Cramér-Rao bound when the sample is small and thereby provide a more realistic limit on the achievable precision of a finite-sample quantum measurement. In the large-data limit, the hierarchy of bounds collapses back onto the quantum Cramér-Rao bound.

Ponente: Manuel Gessner (Departamento de Física Teórica & IFIC, Universidad de Valencia-CSIC)
15:30

Steady-state quantum thermodynamics with synthetic negative temperatures 30m

A bath with a negative temperature is a subject of intense debate in recent times. It raises fundamental questions not only on our understanding of negative temperature of a bath in connection with thermodynamics but also on the possibilities of constructing devices using such baths. In this work, we study steady-state quantum thermodynamics involving baths with negative temperatures. A bath with a negative temperature is created synthetically using two baths of positive temperatures and weakly coupling these with a qutrit system. These baths are then coupled to each other via a working system. At steady state, the laws of thermodynamics are analyzed. We find that whenever the temperatures of these synthetic baths are identical, there is no heat flow, which reaffirms the zeroth law. There is always a spontaneous heat flow for different temperatures. In particular, heat flows from a bath with a negative temperature to a bath with a positive temperature which, in turn, implies that a bath with a negative temperature is “hotter” than a bath with a positive temperature. This warrants an amendment in the Kelvin-Planck statement of the second law, as suggested in earlier studies. In all these processes, the overall entropy production is positive, as required by the Clausius statement of the second law. We construct continuous heat engines operating between positive and negative temperature baths. These engines yield maximum possible heat-to-work conversion efficiency, that is, unity. We also study the thermodynamic nature of heat from a bath with a negative temperature and find that it is thermodynamic work but with negative entropy.

Ponente: Mohit Bera
16:00

From Colliders to Qubits: Exploring HEP with Quantum Jet Clustering and QFIAE 30m

High-energy physics (HEP) experiments generate massive datasets that challenge classical computing methods. Quantum computing offers promising avenues to tackle these challenges. This talk presents two applications of quantum algorithms in HEP:

Quantum Jet Clustering: This method leverages the principles of superposition and entanglement to efficiently reconstruct jets of particles emerging from high-energy collisions. (arXiv: 2204.06496 [hep-ph])

Quantum Fourier Iterative Amplitude Estimation (QFIAE): This novel algorithm accelerates the computation of multidimensional integrals, a crucial task in HEP calculations. (arXiv: 2305.01686v2 [quant-ph])

By exploring these applications, we demonstrate the potential of quantum computing to revolutionize HEP data analysis and unlock new discoveries in the subatomic world.

Ponente: Leandro Cieri (IFIC)

Valencia Quantum Spain mini-workshop

Room "Joan Pelechano"

ETSE