Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols

Abstract

Quantum computer emulators model the behavior and error rates of specific quantum processors. Without accurate noise models in these emulators, it is challenging for users to optimize and debug executable quantum programs prior to running them on the quantum computer, as device-specific noise is not properly accounted for. To overcome this challenge, we design a machine learning(ML)-driven approach to construct approximate device-specific emulators that applies to different hardware platforms. We apply supervised ML on a pre-generated library containing simulated gate set tomography training data. The ML model then analyses gate set tomography data from a target quantum computer to predict its noise model, which is in turn used to construct the device-specific emulator. We demonstrate the effectiveness of our protocol's emulator in estimating the unitary coupled cluster energy of the H${}_2$ molecule and compare the results with those from actual quantum hardware. Remarkably, our noise model captures device noise with high accuracy, achieving a percentage relative error of just 0.128\% in expectation value relative to the actual quantum hardware. Importantly, we show that even without access to pulse-level control, noise from the quantum computer can nonetheless be characterized and independently validated by our protocol.

19 pages, 10 figures

References in corpus (22)

• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Variational Quantum Algorithms
• Parameterized quantum circuits as machine learning models
• Randomized Benchmarking of Quantum Gates
• Scalable Quantum Simulation of Molecular Energies
• Power of data in quantum machine learning
• Challenges and Opportunities in Quantum Machine Learning
• Practical Quantum Error Mitigation for Near-Future Applications
• Characterizing Quantum Gates via Randomized Benchmarking
• Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography
• Barren Plateaus in Variational Quantum Computing
• Gate Set Tomography
• Quantum circuit architecture search for variational quantum algorithms
• Qibo: a framework for quantum simulation with hardware acceleration
• Modelling and Simulating the Noisy Behaviour of Near-term Quantum Computers
• Benchmarking a trapped-ion quantum computer with 30 qubits
• Digital-analog quantum algorithm for the quantum Fourier transform
• Simulating noisy variational quantum eigensolver with local noise models
• Efficient Lindblad synthesis for noise model construction
• Computing Electronic Correlation Energies using Linear Depth Quantum Circuits
• TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computing