Abstract

The variational principle of quantum mechanics is the backbone of hybrid quantum computing for a range of applications. However, as the problem size grows, quantum logic errors and the effect of barren plateaus overwhelm the quality of the results. There is now a clear focus on strategies that require fewer quantum circuit steps and are robust to device errors. Here we present an approach in which problem complexity is transferred to dynamic quantities computed on the quantum processor – Hamiltonian moments, ⟨Hn⟩. From these quantum computed moments, an estimate of the ground-state energy can be obtained using the ``infimum'' theorem from Lanczos cumulant expansions which manifestly corrects the associated variational calculation. With higher order effects in Hilbert space generated via the moments, the burden on the trial-state quantum circuit depth is eased. The method is introduced and demonstrated on 2D quantum magnetism models on lattices up to 5×5 (25 qubits) implemented on IBM Quantum superconducting qubit devices. Moments were quantum computed to fourth order with respect to a parameterised antiferromagnetic trial-state. A comprehensive comparison with benchmark variational calculations was performed, including over an ensemble of random coupling instances. The results showed that the infimum estimate consistently outperformed the benchmark variational approach for the same trial-state. These initial investigations suggest that the quantum computed moments approach has a high degree of stability against trial-state variation, quantum gate errors and shot noise, all of which bodes well for further investigation and applications of the approach.

Highlights

  • Quantum computers represent a new paradigm for computing that is witnessing rapid advances in both hardware and software

  • The Quantum Computed Moments approach presented here shifts the focus of the representation of problem complexity from the trial-state to the quantities being measured on the quantum computer – Hamiltonian moments

  • We demonstrated the method on models of 2D quantum magnetism using IBM Quantum processors for instances up to 25 qubits

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Summary

Introduction

Quantum computers represent a new paradigm for computing that is witnessing rapid advances in both hardware and software. In NISQ devices, the quest for quantum advantage is challenged by errors in logic and read-out which place severe restrictions on the number of time-steps, or “depth”, of any given quantum circuit before the results are scrambled Hybrid quantum algorithms such as the Variational Quantum Eigensolver (VQE) [10] or the Quantum Approximate Optimisation Algorithm (QAOA) [5] adapt variational-style hybrid approaches to the problem cast in Hamiltonian form. In this work we introduce an alternative method for computing the lowest energy of a problem Hamiltonian system, H, based on minimising circuit depth by transferring complexity to the computation of moments of the Hamiltonian, Hn , with respect to a given trial-state (Figure 1).

Hamiltonian form and the variational limit
Reduction
Results
Conclusion
Contributions and acknowledgements
Full Text
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