arXiv subjects

Vinul Wimalaweera

Publications and source records attributed to Vinul Wimalaweera.

5 recordsLinked to original sources

arXiv2026

Locally optimized variational evolution for quantum many-body systems

Conventional quantum advantage in many-body dynamics is based on avoiding the simulation cost on a classical computer that arises from the extensive exponential complexity of the global wavefunction. Local observables, however, do not inherit this extensive complexity and may instead be governed by an intrinsic local complexity that is independent of the total system size. This distinction is particularly relevant in thermalising systems, where local observables lose memory of microscopic details and relax towards equilibrium values determined by only a few parameters. Here we introduce a variational time-evolution principle that exploits this distinction by replacing global-state fidelity with a cost function defined on local reduced density matrices. The resulting evolution retains coherent short-time dynamics while exploiting the simplification produced by thermalisation at later times. The concrete algorithm we propose is based on locally optimising matrix-product states and admits closed-form equations of motion analogous to the time-dependent variational principle. We show that the same variational principle has a quantum-classical counterpart, combining quantum evaluation of the local cost with an optimisation strategy robust to both shot and hardware noise. Proof-of-principle implementations on Quantinuum H2 and IBM Heron processors recover the characteristic local dynamics.

quant-ph↗

arXiv2026

Fully optimised variational simulation of a dynamical quantum phase transition on a trapped-ion quantum computer

We time-evolve a translationally invariant quantum state on the Quantinuum H1-1 trapped-ion quantum processor, studying the dynamical quantum phase transition of the transverse field Ising model. This physics requires a delicate cancellation of phases in the many-body wavefunction and presents a tough challenge for current quantum devices. We follow the dynamics using a quantum circuit matrix product state ansatz, optimised for the time-evolution using a fidelity cost function. Sampling costs are mitigated by using the measured values of this circuit as stochastic corrections to a simple classical extrapolation of the ansatz parameters. Our results demonstrate the feasibility of variational quantum time-evolution and reveal a hitherto hidden simplicity of the evolution of the transverse-field Ising model through the dynamical quantum phase transition.

quant-ph↗

arXiv2025

Simulating the Antiferromagnetic Heisenberg Model on a Spin-Frustrated Kagome Lattice with the Contextual Subspace Variational Quantum Eigensolver

In this work we investigate the ground state properties of a candidate quantum spin liquid using a superconducting Noisy Intermediate-Scale Quantum (NISQ) device. Specifically, we study the antiferromagnetic Heisenberg model on a Kagome lattice, a geometrically frustrated structure that gives rise to a highly degenerate energy spectrum. To successfully simulate this system, we employ a qubit reduction strategy leveraging the Contextual Subspace methodology, significantly reducing the problem size prior to execution on the quantum device. We improve the quality of these subspaces by using the wavefunctions obtained from low bond dimension Density Matrix Renormalization Group (DMRG) calculations to bias the subspace stabilizers through a symplectic approximate symmetry generator extraction algorithm. Reducing the Hamiltonian size allows us to implement tiled circuit ensembles and deploy the Variational Quantum Eigensolver (VQE) to estimate the ground state energy. We adopt a hybrid quantum error mitigation strategy combining Readout Error Mitigation (REM), Symmetry Verification (SV) and Zero Noise Extrapolation (ZNE). This approach yields high-accuracy energy estimates, achieving error rates on the order of 0.01% and thus demonstrating the potential of near-term quantum devices for probing frustrated quantum materials.

quant-ph↗

arXiv2022

Simulating groundstate and dynamical quantum phase transitions on a superconducting quantum computer

We optimise a translationally invariant, sequential quantum circuit on a superconducting quantum device to simulate the groundstate of the quantum Ising model through its quantum critical point. We further demonstrate how the dynamical quantum critical point found in quenches of this model across its quantum critical point can be simulated. Our approach avoids finite-size scaling effects by using sequential quantum circuits inspired by infinite matrix product states. We provide efficient circuits and a variety of error mitigation strategies to implement, optimise and time-evolve these states.

quant-ph↗

arXiv2021

Matrix Product State Pre-Training for Quantum Machine Learning

Hybrid Quantum-Classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, Parametrised Quantum Circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Training PQCs relies on methods to overcome the fact that the gradients of PQCs vanish exponentially in the size of the circuits used. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pre-training method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization.

quant-ph↗