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Abstract Interacting many-electron problems pose some of the greatest computational challenges in science, with essential applications across many fields. The solutions to these problems will offer accurate predictions of chemical reactivity and kinetics, and other properties of quantum systems 1–4 . Fermionic quantum Monte Carlo (QMC) methods 5,6 , which use a statistical sampling of the ground state, are among the most powerful approaches to these problems. Controlling the fermionic sign problem with constraints ensures the efficiency of QMC at the expense of potentially significant biases owing to the limited flexibility of classical computation. Here we propose an approach that combines constrained QMC with quantum computation to reduce such biases. We implement our scheme experimentally using up to 16 qubits to unbias constrained QMC calculations performed on chemical systems with as many as 120 orbitals. These experiments represent the largest chemistry simulations performed with the help of quantum computers, while achieving accuracy that is competitive with state-of-the-art classical methods without burdensome error mitigation. Compared with the popular variational quantum eigensolver 7,8 , our hybrid quantum-classical computational model offers an alternative path towards achieving a practical quantum advantage for the electronic structure problem without demanding exceedingly accurate preparation and measurement of the ground-state wavefunction.
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Neural Network Enhanced Robustness for Noisy Quantum Applications
Due to the limitations of current NISQ systems, error mitigation strategies are under development to alleviate the negative effects of error-inducing noise on quantum applications. This work proposes the use of machine learning (ML) as an error mitigation strategy, using ML to identify the accurate solutions to a quantum application in the presence of noise. Methods of encoding the probabilistic solution space of a basis-encoded quantum algorithm are researched to identify the characteristics which represent good ML training inputs. A multilayer perceptron artificial neural network (MLP ANN) was trained on the results of 8-state and 16-state basis-encoded quantum applications both in the presence of noise and in noise-free simulation. It is demonstrated using simulated quantum hardware and probabilistic noise models that a sufficiently trained model may identify accurate solutions to a quantum applications with over 90% precision and 80% recall on select data. The model makes confident predictions even with enough noise that the solutions cannot be determined by direct observation, and when it cannot, it can identify the inconclusive experiments as candidates for other error mitigation techniques.
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- Award ID(s):
- 2300476
- PAR ID:
- 10608929
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-4127-9
- Page Range / eLocation ID:
- 1 to 10
- Subject(s) / Keyword(s):
- Quantum Computing Quantum Error Mitigation Machine Learning
- Format(s):
- Medium: X
- Location:
- San Diego, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRC64395.2024.10937016
