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QI: Fachverband Quanteninformation

QI 9: Quantum Machine Learning and Classical Simulability

QI 9.6: Vortrag

Dienstag, 19. März 2024, 11:00–11:15, HFT-FT 101

Efficient classical surrogate simulation of quantum circuits — •Manuel S. Rudolph^{1, 5}, Enrico Fontana^{2, 3, 4}, Ross Duncan³, Ivan Rungger⁴, Zoë Holmes¹, Lukasz Cincio⁵, and Cristina Cîrstoiu³ — ¹EPFL, Lausanne, Schweiz — ²University of Strathclyde, Glasgow, UK — ³Quantinuum, Cambridge, UK — ⁴National Physical Laboratory, Teddington, UK — ⁵Los Alamos National Lab, Los Alamos, USA

Performant classical simulation of quantum systems is crucial for benchmarking quantum algorithms and verifying potential quantum advantages. Here, we provide two results. First, we prove that there exists a polynomial-time algorithm for simulating quantum circuits affected by constant local Pauli noise with bounded average error as the number of qubits or circuit depth increases. This highlights that, on average, there cannot be an exponential quantum-classical separation in observable estimation tasks when the quantum hardware is affected by such noise. Second, we turn our Theorems into a full-fledged high-performance simulation algorithm called ``LOWESA'' for noisy and noise-free quantum circuits. LOWESA can be understood as a classical surrogate for expectation landscapes with fast re-evaluation at different circuit parameters. We show that we can scale our simulations to the 127-qubit examples presented in Nature 618, 500-505 (2023), where we produce near-exact expectation values and highlight the strengths of LOWESA compared to other established simulation methods.

Keywords: Quantum Simulation; Classical Simulation; Quantum Algorithms; Parametrized Quantum Circuits; Classical Surrogate