Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals

Wright, Emily; De Sousa, Rogério

doi:10.1109/qce57702.2023.00146

2023 IEEE International Conference on Quantum Computing and Engineering (QCE) 2023

DOI: 10.1109/qce57702.2023.00146

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Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals

Emily Wright,

Rogério De Sousa

Abstract: We acknowledge and respect the l e , k w e ŋ e n peoples on whose territory the university stands and the Songhees, Esquimalt and _ WSÁNEĆ peoples whose historical relationships with the land continue to this day.

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2024

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Cited by 2 publications

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Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators

Castaldo,

Rosa,

Corni

2024

The Journal of Chemical Physics

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We show that optimal control of the electron dynamics is able to prepare molecular ground states, within chemical accuracy, with evolution times approaching the bounds imposed by quantum mechanics. We propose a specific parameterization of the molecular evolution only in terms of interaction already present in the molecular Hamiltonian. Thus, the proposed method solely utilizes quantum simulation routines, retaining their favorable scalings. Due to the intimate relationships between variational quantum algorithms and optimal control, we compare, when possible, our results with state-of-the-art methods in the literature. We found that the number of parameters needed to reach chemical accuracy and algorithmic scaling is in line with compact adaptive strategies to build variational Ansätze. The algorithm, which is also suitable for quantum simulators, is implemented by emulating a digital quantum processor (up to 16 qubits) and tested on different molecules and geometries spanning different degrees of electron correlation.

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Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators

Castaldo,

Rosa,

Corni

2024

The Journal of Chemical Physics

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Reinforcement learning pulses for transmon qubit entangling gates

Nam Nguyen,

Motzoi,

Metcalf

et al. 2024

Mach. Learn.: Sci. Technol.

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The utility of a quantum computer depends heavily on the ability to reliably perform accurate quantum logic operations. For finding optimal control solutions, it is of particular interest to explore model-free approaches, since their quality is not constrained by the limited accuracy of theoretical models for the quantum processor -- in contrast to many established gate implementation strategies.In this work, we utilize a continuous-control reinforcement learning algorithm to design entangling two-qubit gates for superconducting qubits; specifically, our agent constructs cross-resonance and CNOT gates without any prior information about the physical system. Using a simulated environment of fixed-frequency, fixed-coupling transmon qubits, we demonstrate the capability to generate novel pulse sequences that outperform the standard cross-resonance gates in both fidelity and gate duration, while maintaining a comparable susceptibility to stochastic unitary noise. We further showcase an augmentation in training and input information that allows our agent to adapt its pulse design abilities to drifting hardware characteristics, importantly with little to no additional optimization.Our results exhibit clearly the advantages of unbiased adaptive-feedback learning-based optimization methods for transmon gate design.

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Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals

Abstract: We acknowledge and respect the l e , k w e ŋ e n peoples on whose territory the university stands and the Songhees, Esquimalt and _ WSÁNEĆ peoples whose historical relationships with the land continue to this day.

Cited by 2 publications

References 73 publications

Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators

Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators

Reinforcement learning pulses for transmon qubit entangling gates

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