arXiv subjects

Phalgun Lolur

Publications and source records attributed to Phalgun Lolur.

11 recordsLinked to original sources

arXiv2026

Sample-based quantum diagonalization approach for open-shell transition-metal complexes in gas and implicit-solvent

Open-shell $3d$ transition-metal complexes pose a stringent challenge for correlated electronic-structure methods because near-degenerate metal $d$ orbitals give rise to competing spin states and oxidation-state-dependent charge transfer, while solvation can reshape the relative stability of the resulting electronic configurations. Building on prior demonstrations of open-shell sample-based quantum diagonalization (SQD) for light-atom systems and, separately, closed-shell SQD coupled to the integral-equation-formalism polarizable continuum model (IEF-PCM), we combine these capabilities through an open-shell SQD--IEF-PCM framework and apply it to the octahedrally coordinated $\mathrm{[Co(H_2O)_5CO_2]^{2+/3+}}$ complex, spanning the Co(III) singlet and quintet and Co(II) doublet and quartet states along a metal--ligand dissociation coordinate. Using quantum samples collected on an IBM Heron processor and active spaces of up to 50 qubits, SQD closely reproduces coupled-cluster and heat-bath configuration-interaction benchmarks within the same active spaces in both gas phase and implicit solvent; for high-spin quintet $\mathrm{[Co(H_2O)_5CO_2]^{3+}}$, it additionally resolves an avoided crossing associated with internal charge transfer that is absent in the corresponding singlet and in the lower oxidation state, while IEF-PCM suppresses this crossover by preferentially stabilizing the dissociation channel leading to neutral CO$_2$. To our knowledge, this work provides the first hardware demonstration of SQD for an open-shell 3d transition-metal complex with discrete metal-centered d-orbital chemistry, and the first application of open-shell SQD in a dielectric continuum, establishing a quantum-centric framework for transition-metal chemistry in regimes where spin-state energetics, charge transfer, and environmental response are strongly coupled.

quant-ph↗

arXiv2026

Circuit Depth Reduction for Executable Hamiltonian Dynamics of Covalent Inhibitor Reactivity on Quantum Hardware

Quantum chemistry applications in the noisy intermediate-scale quantum era require end-to-end approaches that balance algorithmic fidelity with practical executability on existing hardware. We present an end-to-end Hamiltonian dynamics case study for predicting the reactivity of pharmaceutically relevant covalent inhibitors containing sulfonyl fluoride warheads, using a quantum-centric data-driven research and development framework that combines Hamiltonian time evolution with classical machine learning. To make such simulations executable on current quantum processors, we introduce a systematic circuit reduction strategy based on Hamiltonian term truncation with observable error bounds, Clifford Decomposition and Transformation, and hardware-aware transpilation. Across representative molecular fragments, this approach achieves circuit depth reductions of up to 28.5x under all-to-all connectivity assumptions and up to 15.5x on IBM Heron-class architectures. For an eight-qubit Hamiltonian dynamics simulation, a transpiled instruction set architecture (ISA) circuit depth of 1330 is rendered executable through middleware-enabled circuit decomposition, enabling the execution of sub-circuits with depths up to 371 and containing up to 216 two-qubit gates on real hardware. We evaluate the impact of circuit reduction on downstream reactivity prediction accuracy and show that chemically meaningful predictions can be retained despite aggressive circuit simplifications, clarifying the trade-offs that govern practical quantum chemistry workflows on near-term quantum systems.

quant-ph↗

arXiv2026

Practical Scalability of Tensor Network Quantum Emulators for Molecular Hamiltonian Simulation

Quantum computing holds promise for computational chemistry, but near-term quantum hardware remains limited by noise and scale, motivating classical bridging technologies such as quantum emulators. We present an application-specific systems-level benchmark of matrix product state (MPS) tensor-network emulation for real-time Hamiltonian evolution of a density matrix embedding theory (DMET)-embedded sulfonyl-fluoride pharmaceutical fragment, using state-vector simulation as reference. We evaluate runtime, accuracy, resource requirements, and entanglement growth across active spaces from 4 to 24 qubits for a one-body temporal observable used as a quantum fingerprint for reactivity prediction. The results identify a practical boundary for this workflow. At fixed bond dimension, MPS emulation retains favorable scaling, but the bond dimension required to estimate the observable within a 1.6 mHa chemical-accuracy threshold grows rapidly with active-space size. At 20-24 qubits, it approaches the maximum available MPS representation, eliminating the runtime advantage over state-vector simulation. Entanglement entropy analysis shows that increasing bipartite entanglement in the time-evolved state drives this cost growth, consistent with a mismatch between a one-dimensional MPS ansatz and the non-local correlations generated by molecular electronic dynamics. We do not claim a universal crossover across molecules, observables, mappings, or tensor-network geometries. Rather, this study measures where MPS emulation ceases to be an efficient classical surrogate for this chemically motivated Hamiltonian-simulation workflow. The results motivate entanglement-aware algorithm design, orbital-ordering and mapping optimization, alternative tensor-network geometries, and ultimately fault-tolerant quantum hardware for regimes where accurate molecular dynamics generate non-compressible entanglement.

quant-ph↗

arXiv2026

Scientific applications of quantum computing: challenges and opportunities

The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.

quant-ph↗

arXiv2026

The Future of Computing for Materials Science Challenges

Materials discovery increasingly relies on the coordinated use of theory, computation, experiment, data-driven methods, and emerging quantum technologies, yet the full potential of these tools is realised only when they operate within workflows that reflect the complexity of real systems. This perspective summarises current capabilities, limitations, and opportunities across these domains, drawing on contributions from academia, industry, and national laboratories to identify the scientific and structural requirements for more reliable and efficient discovery. Classical simulations provide broad coverage across design spaces, while experimental measurements reveal degradation, heterogeneity, and kinetic processes that determine performance under realistic conditions. Machine learning accelerates exploration when supported by well-curated datasets with clear provenance and uncertainty quantification, and quantum computing offers promising routes into correlated electronic behaviour when aligned with properties that influence engineering decisions. Collectively, these insights highlight the need for reproducible workflows, shared data standards, realistic benchmarks, and a research culture that prepares scientists to work across paradigms. By integrating these methodological and organisational elements, the community can move toward discovery processes that deliver robust predictions, support confident decision making, and shorten the path from conceptual design to deployable materials.

cond-mat.mtrl-sci↗

arXiv2026

Digital Quantum Simulation of the quantum $β$-FPUT Lattice: Formulation and Resource Estimation

Heat conduction in low-dimensional systems exhibits strong deviations from Fourier behavior due to anharmonicity and long-lived vibrational correlations, challenging conventional computational approaches. The $β$-Fermi--Pasta--Ulam--Tsingou ($β$-FPUT) chain provides a minimal nonlinear lattice model for studying anomalous transport, yet its quantum real-time dynamics remain difficult to access with classical methods. We develop a first-quantized digital quantum-simulation framework for the quantum $β$-FPUT lattice, targeting fault-tolerant quantum computers. By working directly with discretized lattice displacements rather than truncated phonon occupation spaces, the approach captures anharmonic interactions while avoiding bosonic encoding overheads. We construct Trotterized circuit blocks for real-time evolution and introduce a Hermitian quadrature decomposition of Fourier-mode displacement operators that enables shallow quantum circuits for mode-resolved displacement correlators. We analyze the quantum resources required for the full simulation and measurement workflow, providing qubit counts, gate complexity, circuit-depth and resource estimates as functions of system size and resolution within a fault-tolerant workflow. These results establish a concrete algorithmic blueprint for simulating quantum transport dynamics in nonlinear low-dimensional lattice models on fault-tolerant quantum hardware.

quant-ph↗

arXiv2026

Cross-Platform Benchmarking of Near-Term Quantum Optimisation Algorithms

Quantum computers show potential for achieving computational advantage over classical computers, with many candidate applications in combinatorial optimisation. We present an application level benchmarking framework for near-term quantum optimisation algorithms using a dense Quadratic Unconstrained Binary Optimisation (QUBO) materials science problem as a representative test-case. To solve this problem, we implement two methods, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA), on commercially-available gate-based and quantum annealing devices that are accessible via Quantum-Computing-as-a-Service (QCaaS) models. To analyse the performance of these algorithms, we use a toolbox of relevant metrics and compare performance against three classical algorithms. We employ quantum methods to solve fully-connected QUBOs of up to $72$ variables, and find that algorithm performance beyond this is restricted by device connectivity, noise and classical computation time overheads. The applicability of our approach goes beyond the selected configurational analysis test-case, and we anticipate that our approach will be of use for optimisation problems in general.

quant-ph↗

arXiv2025

Excitation Amplitude Sampling for Low Variance Electronic Structure on Quantum Computers

We combine classical heuristics with partial shadow tomography to enable efficient protocols for extracting information from correlated ab initio electronic systems encoded on quantum devices. By proposing the use of a correlation energy functional and sampling of a polynomial set of excitation amplitudes of the quantum state, we can demonstrate an almost two order of magnitude reduction in required number of shots for a given statistical error in the energy estimate, as well as observing a linear scaling to accessible system sizes. Furthermore, we find a high-degree of noise resilience of these estimators on real quantum devices, with up to an order of magnitude increase in the tolerated noise compared to traditional techniques. While these approaches are expected to break down asymptotically, we find strong evidence that these large system arguments do not prevent algorithmic advantage from these simple protocols in many systems of interest. We further extend this to consider the extraction of beyond-energetic properties by mapping to a coupled cluster surrogate model, as well as a natural combination within a quantum embedding framework. This embedding framework avoids the unstable self-consistent requirements of previous approaches, enabling application of quantum solvers to realistic correlated materials science, where we demonstrate the volume-dependence of the spin gap of Nickel Oxide.

quant-ph↗

arXiv2025

A Multi-Scale Quantum Framework for Evaluating Metal-Organic Frameworks in Carbon Capture

Metal Organic Frameworks (MOFs) are promising materials to help mitigate the effects of global warming by selectively absorbing $\text{CO}_{2}$ for direct capture. Accurate quantum chemistry simulations are a useful tool to help select and design optimal MOF structures, replacing costly or impractical experiments or providing chemically inspired features for data-driven approaches such as machine learning. However, applying simulations over large datasets requires efficient simulation methods such as Density Functional Theory (DFT) which, despite often being accurate, introduces uncontrolled approximations and a lack of systematic improvability. In this work we outline a hierarchical cluster model that includes a recently developed quantum embedding that provides a more systematic approach to efficiently tune accuracy. We apply this workflow to calculate the binding affinity for a small set of MOF structures and $\text{CO}_{2}$ using experimentally measured heat of adsorption as a reference. Since quantum embeddings have also been proposed as a framework to accelerate the utility of quantum hardware, we discuss some of the benefits and challenges of integrating quantum solvers into the workflow outlined in this work.

quant-ph↗

arXiv2024

Reverse Map Projections as Equivariant Quantum Embeddings

We introduce the novel class $(E_α)_{α\in [-\infty,1)}$ of reverse map projection embeddings, each one defining a unique new method of encoding classical data into quantum states. Inspired by well-known map projections from the unit sphere onto its tangent planes, used in practice in cartography, these embeddings address the common drawback of the amplitude embedding method, wherein scalar multiples of data points are identified and information about the norm of data is lost. We show how reverse map projections can be utilised as equivariant embeddings for quantum machine learning. Using these methods, we can leverage symmetries in classical datasets to significantly strengthen performance on quantum machine learning tasks. Finally, we select four values of $α$ with which to perform a simple classification task, taking $E_α$ as the embedding and experimenting with both equivariant and non-equivariant setups. We compare their results alongside those of standard amplitude embedding.

quant-ph↗

arXiv2022

Reference-State Error Mitigation: A Strategy for High Accuracy Quantum Computation of Chemistry

Decoherence and gate errors severely limit the capabilities of state-of-the-art quantum computers. This work introduces a strategy for reference-state error mitigation (REM) of quantum chemistry that can be straightforwardly implemented on current and near-term devices. REM can be applied alongside existing mitigation procedures, while requiring minimal post-processing and only one or no additional measurements. The approach is agnostic to the underlying quantum mechanical ansatz and is designed for the variational quantum eigensolver (VQE). Two orders-of-magnitude improvement in the computational accuracy of ground state energies of small molecules (H2, HeH+ and LiH) is demonstrated on superconducting quantum hardware. Simulations of noisy circuits with a depth exceeding 1000 two-qubit gates are used to argue for scalability of the method.

quant-ph↗