Marie Skłodowska-Curie PhD Fellow in quantum computing, working on quantum algorithms and resource-efficient simulation, with interests in AI/ML and real-world industry applications.

About Me

I am a PhD student at Universitat de Valencia, working on quantum algorithms and quantum machine learning. My focus is on translating theoretical ideas into practical implementations and benchmarking them under realistic constraints. Previously, I worked as a data scientist at BRD, where I focused on building various ML pipelines for different needs by the bank.

My Toolkit

Python
NumPy
Qiskit
PennyLane
Matplotlib
Scikit-learn
A picture of me

Publications 📝

Adaptive time Compressed QITE (ACQ) and its geometrical interpretation

Imaginary time evolution (ITE) is a well-established method for ground-state preparation, a fundamental problem in many fields such as materials science, chemistry, and optimization. Quantum ITE (QITE) approximates this evolution on quantum hardware but suffers from high circuit depth and numerous measurements. In this work we introduce adaptive-time compressed QITE (ACQ), a novel algorithm that reduces resource-cost by combining adaptive time steps with circuit compression. This approach leverages geometric insights by characterizing its relationship to geodesic trajectories with a measure that distinguishes trajectories in CPN. Recalling that ITE is a gradient flow on the complex projective plane ℂℙN, such trajectory measures allow one to measure the deviation from geodesicity of said flow. For Hamiltonians with only two distinct eigenvalues (spectral cardiality), ITE and QITE exactly trace geodesics, this fact motivates an adaptive strategy for systems whose corresponding spectral cardinality is greater than 2, where QITE unitaries are reused until an energy increase signals departure from the ITE path. This is implemented via a line search for energy minimization. Circuit compression is achieved by approximating the sequence of QITE unitaries with a single element of a one-parameter group. Numerical simulations on the transverse field Ising model and the Heisenberg model demonstrate that ACQ achieves comparable fidelity to standard QITE while significantly reducing the number of QITE optimizations and maintaining fixed circuit depth during propagation. Gate-count estimates and an analysis of the fidelity scaling with truncation parameters are provided. A gate count and performance comparison with the state of the art method double bracket QITE is also performed.

Understanding Quantum Imaginary Time Evolution and its Variational form

Many computationally hard problems can be encoded in quantum Hamiltonians. The solution to these problems is given by the ground states of these Hamiltonians. A state-of-the-art algorithm for finding the ground state of a Hamiltonian is the so-called Quantum Imaginary Time Evolution (QITE) which approximates imaginary time evolution by a unitary evolution that can be implemented in quantum hardware. In this paper, we review the original algorithm together with a comprehensive computer program, as well as, the variational version of it.

Projects 🧑‍💻

NER for product names

A short project where I fine tuned a NER huggingface model for furniture products detection. The goal of the model was to return the products on a furniture website.

PyTorch
HuggingFace
NER
RAG for Q&A on bank documentation

Primary developer for this internal tool to be used by BRD bank employees. The main goal was to develop a Q&A tool specialized on internal bank documentation

Python
Scikit-learn
HuggingFace
Numpy
Pandas
RAG
Clustering & classification of bank clients

Segmented bank customers using k-means clustering to identify credit demand patterns, then trained a k-nearest neighbors classifier to predict customer segments and support data-driven credit targeting.

Python
Scikit-learn
Numpy
Pandas
k-means
k nearest neighbors