Towards quantum simulations in particle physics and beyond on noisy intermediate-scale quantum devices

Abstract

We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high-energy physics and beyond on noisy intermediate-scale quantum (NISQ) devices. The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits. The second method is an efficient mitigation of readout errors on quantum devices. Both methods can lead to significant improvements in quantum simulations, e.g. when variational quantum eigensolvers are used. This article is part of the theme issue ‘Quantum technologies in particle physics’.

Investigating the variance increase of readout error mitigation through classical bit-flip correction on IBM and Rigetti quantum computers

“…In this section, we briefly review the readout mitigation method proposed in Refs. [1,2]. We first focus on the impact of this method on the expectation values and afterwards on the variances.…”

“…In this work, we investigate this effect for a recently developed mitigation protocol for measurement errors [1,2] on IBM's and Rigetti's quantum devices. In particular, we compare the change in variance from the hardware results with and without applying the mitigation procedure to the theoretically predicted values and benchmark the performance of the protocol.…”

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Readout errors are among the most dominant errors on current noisy intermediate-scale quantum devices. Recently, an efficient and scaleable method for mitigating such errors has been developed, based on classical bit-flip correction [1,2]. In this talk, we compare the performance of this method for IBM's and Rigetti's quantum devices, demonstrating how the method improves the noisy measurements of observables obtained on the quantum hardware.

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Topological-Graph Dependencies and Scaling Properties of a Heuristic Qubit-Assignment Algorithm

“…Quantum computation may still be in its infancy, but new advances have allowed for the first experimental demonstrations of quantum computing in recent years [1], [2], [3], [4], [5]. Quantum computers themselves promise to aid in solving classically-intractable problems for such fields as quantum chemistry [6], [7], [8], [9], quantum machine learning [10], [11], quantum field theory [12], [13], [14], [15], and quantum cryptography [16], [17], among others. However, such promise comes with a catch: protecting the quantum states in a quantum computer from deleterious noise channels has proven to be a most difficult task, preventing scalability and implementations of most quantum algorithms [18].…”

Quantum technologies in particle physics

“…For quantum computing, control of errors with quantum algorithms is an important issue. Jansen and collaborators discuss two recent algorithmic advances for simulating particle physics phenomena using intermediate scale quantum devices [13]. Tensor network methods have been extensively used for studying lattice gauge theories in recent years, allowing for computations beyond the capabilities of Monte Carlo methods.…”