arXiv subjects

Gabriel Greene-Diniz

Publications and source records attributed to Gabriel Greene-Diniz.

10 recordsLinked to original sources

arXiv2026

Automated near-term quantum algorithm discovery for molecular ground states

Designing quantum algorithms is a complex and counterintuitive task, making it an ideal candidate for AI-driven algorithm discovery. To this end, we employ the Hive, an AI platform for program synthesis, which utilises large language models to drive a highly distributed evolutionary process for discovering new algorithms. We focus on the ground state problem in quantum chemistry, and discover efficient quantum heuristic algorithms that solve it for molecules LiH, H2O, and F2 while exhibiting significant reductions in quantum resources relative to state-of-the-art near-term quantum algorithms. Further, we perform an interpretability study on the discovered algorithms and identify the key functions responsible for the efficiency gains. Finally, we benchmark the Hive-discovered circuits on the Quantinuum System Model H2 quantum computer and identify minimum system requirements for chemical precision. We envision that this novel approach to quantum algorithm discovery applies to other domains beyond chemistry, as well as to designing quantum algorithms for fault-tolerant quantum computers.

quant-ph↗

arXiv2026

Quantum State Preparation Of Multiconfigurational States For Quantum Chemistry

The ability to prepare states for quantum chemistry is a promising feature of quantum computers, and efficient techniques for chemical state preparation is an active area of research. In this paper, we implement and investigate two methods of quantum circuit preparation for multiconfigurational states for quantum chemical applications. It has previously been shown that controlled Givens rotations are universal for quantum chemistry. To prepare a selected linear combination of Slater determinants (represented as occupation number configurations) using Givens rotations, the gates that rotate between the reference and excited determinants need to be controlled on qubits outside the excitation (external controls), in general. We implement a method to automatically find the external controls required for utilizing Givens rotations to prepare multiconfigurational states on a quantum circuit. We compare this approach to an alternative technique that exploits the sparsity of the chemical state vector and find that the latter can outperform the method of externally controlled Givens rotations; highly reduced circuits can be obtained by taking advantage of the sparse nature (where the number of basis states is significantly less than 2$^{n_q}$ for $n_q$ qubits) of chemical wavefunctions. We demonstrate the benefits of these techniques in a range of applications, including the ground states of a strongly correlated molecule, matrix elements of the Q-SCEOM algorithm for excited states, as well as correlated initial states for a quantum subspace method based on quantum computed moments and quantum phase estimation.

quant-ph↗

arXiv2025

Quantum simulation of actinide chemistry: towards scalable algorithms on trapped ion quantum computers

Due to the wide range of technical applications of actinide elements, a thorough understanding of their electronic structure could complement technological improvements in many different areas. Quantum computing could greatly aid in this understanding, as it can potentially provide exponential speedups over classical approaches, thereby offering insights into the complex electronic structure of actinide compounds. As a first foray into quantum computational chemistry of actinides, this paper compares the method of quantum computed moments (QCM) as a noisy intermediate-scale quantum algorithm with a single-ancilla version of quantum phase estimation (QPE), a quantum algorithm expected to run on fault-tolerant quantum computers. We employ these algorithms to study the reaction energetics of plutonium oxides and hydrides. In order to enable quantum hardware experiments, we use several techniques to reduce resource requirements: screening individual Hamiltonian Pauli terms to reduce the measurement requirements of QCM and variational compilation to reduce the depth of QPE circuits. Finally, we derive electronic structure descriptions from a series of representative chemical models and compute the energetics from quantum experiments on Quantinuum's H-series ion trap devices using up to 19 qubits. We find our experiments to be in excellent agreement with results from classical electronic structure calculations and state vector simulations.

quant-ph↗

arXiv2025

Measuring Correlation and Entanglement between Molecular Orbitals on a Trapped-Ion Quantum Computer

Quantifying correlation and entanglement between molecular orbitals can elucidate the role of quantum effects in strongly correlated reaction processes. However, accurately storing the wavefunction for a classical computation of those quantities can be prohibitive. Here we use the Quantinuum H1-1 trapped-ion quantum computer to calculate von Neumann entropies which quantify the orbital correlation and entanglement in a strongly correlated molecular system relevant to lithium-ion batteries (vinylene carbonate interacting with an O$_2$ molecule). As shown in previous works, fermionic superselection rules decrease correlations and reduce measurement overheads for constructing orbital reduced density matrices. Taking into account superselection rules we further reduce the number of measurements by finding commuting sets of Pauli operators. Using low overhead noise reduction techniques we calculate von Neumann entropies in excellent agreement with noiseless benchmarks, indicating that correlations and entanglement between molecular orbitals can be accurately estimated from a quantum computation. Our results show that the one-orbital entanglement vanishes unless opposite-spin open shell configurations are present in the wavefunction.

quant-ph↗

arXiv2024

Quantum Computed Green's Functions using a Cumulant Expansion of the Lanczos Method

In this paper, we present a quantum computational method to calculate the many-body Green's function matrix in a spin orbital basis. We apply our approach to finite-sized fermionic Hubbard models and related impurity models within Dynamical Mean Field Theory, and demonstrate the calculation of Green's functions on Quantinuum's H1-1 trapped-ion quantum computer. Our approach involves a cumulant expansion of the Lanczos method, using Hamiltonian moments as measurable expectation values. This bypasses the need for a large overhead in the number of measurements due to repeated applications of the variational quantum eigensolver (VQE), and instead measures the expectation value of the moments with one set of measurement circuits. From the measured moments, the tridiagonalised Hamiltonian matrix can be computed, which in turn yields the Green's function via continued fractions. While we use a variational algorithm to prepare the ground state in this work, we note that the modularity of our implementation allows for other (non-variational) approaches to be used for the ground state.

cond-mat.str-el↗

arXiv2023

Calculating the Single-Particle Many-body Green's Functions via the Quantum Singular Value Transform Algorithm

The Quantum Singular Value Transformation (QSVT) is a technique that provides a unified framework for describing many of the quantum algorithms discovered to date. We implement a noise-free simulation of the technique to investigate how it can be used to perform matrix inversion, which is an important step in calculating the single-particle Green's function in the Lehmann representation. Due to the inverse function not being defined at zero, we explore the effect of approximating f(x)=1/x with a polynomial. This is carried out by calculating the single-particle Green's function of the two-site single-impurity Anderson model. We also propose a new circuit construction for the linear combination of unitaries block encoding technique, that reduces the number of single and two-qubit gates required.

quant-ph↗

arXiv2023

Modelling Carbon Capture on Metal-Organic Frameworks with Quantum Computing

Despite the recent progress in quantum computational algorithms for chemistry, there is a dearth of quantum computational simulations focused on material science applications, especially for the energy sector, where next generation sorbing materials are urgently needed to battle climate change. To drive their development, quantum computing is applied to the problem of CO$_2$ adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies based on Density Matrix Embedding Theory are applied, using a variational quantum algorithm as a fragment solver, along with active space selection to minimise qubit number. By investigating different fragmentation strategies and solvers, we propose a methodology to apply quantum computing to Al-fumarate interacting with a CO$_2$ molecule, demonstrating the feasibility of treating a complex porous system as a concrete application of quantum computing. Our work paves the way for the use of quantum computing techniques in the quest of sorbents optimisation for more efficient carbon capture and conversion applications.

quant-ph↗

arXiv2021

Quantum Computational Quantification of Protein-Ligand Interactions

We have demonstrated a prototypical hybrid classical and quantum computational workflow for the quantification of protein-ligand interactions. The workflow combines the Density Matrix Embedding Theory (DMET) embedding procedure with the Variational Quantum Eigensolver (VQE) approach for finding molecular electronic ground states. A series of $β$-secretase (BACE1) inhibitors is rank-ordered using binding energy differences calculated on the latest superconducting transmon (IBM) and trapped-ion (Honeywell) Noisy Intermediate Scale Quantum (NISQ) devices. This is the first application of real quantum computers to the calculation of protein-ligand binding energies. The results shed light on hardware and software requirements which would enable the application of NISQ algorithms in drug design.

quant-ph↗

arXiv2020

Generalized unitary coupled cluster excitations for multireference molecular states optimized by the Variational Quantum Eigensolver

The variational quantum eigensolver (VQE) algorithm, designed to calculate the energy of molecular ground states on near-term quantum computers, requires specification of symmetries that describe the system, e.g. spin state and number of electrons. This opens the possibility of using VQE to obtain excited states as the lowest energy solutions of a given set of symmetries. In this paper, the performances of various unitary coupled cluster (UCC) ansätze applied to VQE calculations on excited states are investigated, using quantum circuits designed to represent single reference and multireference wavefunctions to calculate energy curves with respect to variations in the molecular geometry. These ansätze include standard UCCSD, as well as modified versions of UCCGSD and k-UpCCGSD which are engineered to tackle excited states without undesired spin symmetry cross-over to lower states during VQE optimization. These studies are carried out on a range of systems including H$_2$, H$_3$, H$_4$, NH, and OH$^{+}$, CH$_2$, NH$^{+}_{2}$, covering examples of spin singlet, doublet and triplet molecular ground states with single and multireference excited states. In most cases, our calculations are in excellent agreement with results from full configuration interaction calculations on classical machines, thus showing that the VQE algorithm is capable of calculating the lowest excited state at a certain symmetry, including multireference closed and open shell states, by setting appropriate restrictions on the excitations considered in the cluster operator, and appropriate constraints in the qubit register encoding the starting mean field state.

quant-ph↗

arXiv2016

First principles modeling of defects in the Al_2O_3/In_0.53Ga_0.47As system

Density functional theory paired with a first order many-body perturbation theory correction is applied to determine formation energies and charge transition energies for point defects in bulk In_0.53Ga_0.47As and for models of the In_0.53Ga_0.47As/Al_2O_3 interface. The results are consistent with previous computational studies that As_Ga antisites are candidates for defects observed in capacitance voltage measurements on metal-oxide-semiconductor capacitors, as the As_Ga antisite introduces energy states near the valence band maximum and near the middle of the energy band gap. However, substantial broadening in the distribution of the Ga_As charge transition levels due to the variation in the local chemical environment resulting from alloying on the cation (In/Ga) sublattice is found, whereas this effect is absent for As_Ga antisites. Also, charge transition energy levels are found to vary based on proximity to the semiconductor/oxide interface. The combined effects of alloy- and proximity-shift on the Ga_As antisite charge transition energies are consistent with the distribution of interface defect levels between the valence band edge and midgap as extracted from electrical characterization data. Hence, kinetic growth conditions leading to a high density of either Ga_As or As_Ga antisites near the In_0.53Ga_0.47As/Al_2O_3 interface are both consistent with defect energy levels at or below midgap.

cond-mat.mes-hall↗