arXiv subjects

Alexandre Fleury

Publications and source records attributed to Alexandre Fleury.

6 recordsLinked to original sources

arXiv2026

Reducing entanglement with a Hamiltonian derived Clifford transformation

Recently (Physica Scripta, 100(10):105401, 2025), an algorithm was introduced that deterministically generates a Clifford transformation from the Qubit Coupled Cluster (QCC) algorithm which we call Q-Cliff (QCC+Clifford). There, it was shown that Q-Cliff could be utilized to generate a hardware efficient version of the QCC ansatz. Here, we examine and refine these techniques and show that Q-Cliff can be utilized to generate efficient classical and quantum approximations to the ground states of chemical systems. The algorithm generates an efficient variational method that generally has accuracy between MP2 and CISD with $O(N^6)$. Furthermore, we show through DMRG calculations that the entanglement between qubits is reduced significantly and therefore the accuracy for a given bond dimension can be vastly improved (up to an order of magnitude). Finally, we refine the previously reported algorithm to generate low-depth and CNOT efficient circuits that can be optimized with a comparable number of energy evaluations to state-of-the-art VQE algorithms. All these results show that this Hamiltonian derived Clifford transformation should be a tool used for many classical and quantum algorithms.

quant-ph↗

arXiv2025

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data. To extend these capabilities, we introduce AQCat25, a complementary dataset of 13.5 million density functional theory (DFT) single point calculations designed to improve the treatment of systems where spin polarization and/or higher fidelity are critical. We also investigate methodologies for integrating new datasets, such as AQCat25, with the broader Open Catalyst 2020 (OC20) dataset to create spin-aware models without sacrificing generalizability. We find that directly tuning a general model on AQCat25 leads to catastrophic forgetting of the original dataset's knowledge. Conversely, joint training strategies prove effective for improving accuracy on the new data without sacrificing general performance. This joint approach introduces a challenge, as the model must learn from a dataset containing both mixed-fidelity calculations and mixed-physics (spin-polarized vs. unpolarized). We show that explicitly conditioning the model on this system-specific metadata, for example by using Feature-wise Linear Modulation (FiLM), successfully addresses this challenge and further enhances model accuracy. Ultimately, our work establishes an effective protocol for bridging DFT fidelity domains to advance the predictive power of foundational models in catalysis.

cond-mat.mtrl-sci↗

arXiv2025

Offline recovery of magic and entanglement from noisy Pauli product states

The dependence of quantum algorithms on state fidelity is difficult to characterize analytically and is best explored experimentally as hardware scales and noisy simulations become intractable. While low fidelity states are often disregarded, they may still retain valuable information, as long as their dominant eigenvector approximates the target state. Through classical purification, we demonstrate the ability to recover resources specific to quantum computing such as magic and entanglement from noisy states generated by Pauli product formulas, which are common subroutines of many quantum algorithms. Additionally we show that the fidelity of the purified state is dependent on both the magnitude and order in which magic and entanglement are generated, which can be used to inform the order of operators within an ansatz. Consistent across simulation and experiment on IonQ's Aria quantum device, correlations within a state are found to be much more robust to noise than magic, and we show the advantage of designing algorithms targeting these low error states. This study uses quantum informatic tools for analyzing and optimizing quantum algorithms in a noisy framework.

quant-ph↗

arXiv2024

Non-unitary Coupled Cluster Enabled by Mid-circuit Measurements on Quantum Computers

Many quantum algorithms rely on a quality initial state for optimal performance. Preparing an initial state for specific applications can considerably reduce the cost of probabilistic algorithms such as the well studied quantum phase estimation (QPE). Fortunately, in the application space of quantum chemistry, generating approximate wave functions for molecular systems is well studied, and quantum computing algorithms stand to benefit from importing these classical methods directly into a quantum circuit. In this work, we propose a state preparation method based on coupled cluster (CC) theory, which is a pillar of quantum chemistry on classical computers, by incorporating mid-circuit measurements into the circuit construction. Currently, the most well studied state preparation method for quantum chemistry on quantum computers is the variational quantum eigensolver (VQE) with a unitary-CC with single- and double-electron excitation terms (UCCSD) ansatz whose operations are limited to unitary gates. We verify the accuracy of our state preparation protocol using mid-circuit measurements by performing energy evaluation and state overlap computation for a set of small chemical systems. We further demonstrate that our approach leads to a reduction of the classical computation overhead, and the number of CNOT and T gates by 28% and 57% on average when compared against the standard VQE-UCCSD protocol.

quant-ph↗

arXiv2022

Iterative Qubit Coupled Cluster using only Clifford circuits

The performance of quantum algorithms for ground-state energy estimation is directly impacted by the quality of the initial state, where quality is traditionally defined in terms of the overlap of the input state with the target state. An ideal state preparation protocol can be characterized by being easily generated classically and can be transformed to a quantum circuit with minimal overhead while having a significant overlap with the targeted eigenstate of a given Hamiltonian. We propose a method that meets these requirements by introducing a variant of the iterative qubit coupled cluster (iQCC) approach, which exclusively uses Clifford circuits. These circuits can be efficiently simulated on a classical computer, with polynomial scaling according to the Gottesman-Knill theorem. Since the iQCC method has been developed as a quantum algorithm firstly, our variant can be mapped naturally to quantum hardware. We additionally implemented several optimizations to the algorithm enhancing its scalability. We demonstrate the algorithm's correctness in ground-state simulations for small molecules such as H2, LiH, and H2O, and extend our study to complex systems like the titanium-based compound Ti(C5H5)(CH3)3 with a (20, 20) active space, requiring 40 qubits. Results show that the convergence of the algorithm is well-behaved, and the ground state can be represented accurately. Moreover, we show an automated workflow for restricting the qubit active space, thus relieving computational resources by considering only qubits affected by non-trivial operations.

quant-ph↗

arXiv2022

Tangelo: An Open-source Python Package for End-to-end Chemistry Workflows on Quantum Computers

Tangelo [link: https://github.com/goodchemistryco/Tangelo] is an open-source Python software package for the development of end-to-end chemistry workflows on quantum computers, released under Apache 2.0 license. It aims to support the design of successful experiments on quantum hardware, and to facilitate advances in quantum algorithm development. The software enables quick exploration of different approaches by assembling reusable building blocks and algorithms, with the flexibility to let users introduce their own. Tangelo is backend-agnostic and enables switching between various backends (Braket, Qiskit, Qulacs, Azure Quantum, QDK, Cirq...) with minimal changes in the code. The package can be used to explore quantum computing applications such as open-shell systems, excited states, or more industrially-relevant systems by leveraging problem decomposition at scale. This paper outlines the design choices, philosophy, and main features of Tangelo.

quant-ph↗