Efficient Variational Quantum Simulator Incorporating Active Error Minimization

By Ying Li, Simon C. Benjamin. 2016

935 citations recorded by the source corpus.

One of the key applications for quantum computers will be the simulation of other quantum systems that arise in chemistry, materials science, etc, in order to accelerate the process of discovery. It is important to ask: Can this be achieved using near future quantum processors, of modest size and under imperfect control, or must it await the more distant era of large-scale fault-tolerant quantum computing? Here we propose a variational method involving closely integrated classical and quantum coprocessors. We presume that all operations in the quantum coprocessor are prone to error. The impact of such errors is minimised by boosting them artificially and then extrapolating to the zero-error case. In comparison to a more conventional optimised Trotterisation technique, we find that our protocol is efficient and appears to be fundamentally more robust against error accumulation.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/df6bdf26b2ad1a609f6a23e76aa7a4c3d6d82fe5

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Evidence for the utility of quantum computing before fault tolerance (2023, methodology): “In ZNE, we instead amplify the noise 10,25–27 to different gain levels and estimate the zero-noise limit using extrapolation.”
General parameter-shift rules for quantum gradients (2021, methodology): “The metric can also be computed analytically without parameter shifts via a linear combination of uni-taries (LCU) [57, 58], which also employs Hadamard tests.”
Self-verifying variational quantum simulation of lattice models (2018, background): “Measurements of cost functions are affected by statistical errors 54 .”
Variational quantum algorithms for nonlinear problems (2019, methodology): “MPS contain polynomials as well as Fourier series [24, 25] and so these are also contained in the quantum ansatz”
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “Extrapolation methods [115], [116] perform multiple measurements under different error rates and extrapolate ideal measurement outcomes when no noise.”
Digital zero noise extrapolation for quantum error mitigation (2020, methodology): “previous work that focuses on the single and two qubit cases [5], [6], [15], [16].”
Quantum advantage with shallow circuits (2017, background): “Parallelism and circuit depth are important considerations when designing quantum algorithms that can be executed in the near-future on small quantum computers that may lack error correction capabilities [11, 12, 13].”