Quantum compiling by deep reinforcement learning

Moro, Lorenzo Del; Paris, Matteo G. A.; Restelli, Marcello; Prati, Enrico

doi:10.1038/s42005-021-00684-3

Cited by 53 publications

(39 citation statements)

References 42 publications

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“…A natural extension of our approach for tailoring N -body interactions in DQS is the integration of the protocol into a variational feedback-loop scheme for quantum gate design. In contrast to traditional approaches to quantum compiling [74,[80][81][82][83], which optimize the fidelity with respect to some target unitary, here one exploits Hamiltonian learning to directly minimize the Hamiltonian distance to design a desired target Hamiltonian.…”

Section: Discussionmentioning

confidence: 99%

Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning

Pastori¹,

Olsacher²,

Kokail³

et al. 2022

Preprint

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The goal of digital quantum simulation is to approximate the dynamics of a given target Hamiltonian via a sequence of quantum gates, a procedure known as Trotterization. The quality of this approximation can be controlled by the so called Trotter step, that governs the number of required quantum gates per unit simulation time, and is intimately related to the existence of a time-independent, quasilocal Hamiltonian that governs the stroboscopic dynamics, refered to as the Floquet Hamiltonian of the Trotterization. In this work, we propose a Hamiltonian learning scheme to reconstruct the implemented Floquet Hamiltonian order-by-order in the Trotter step: this procedure is efficient, i.e., it requires a number of measurements that scales polynomially in the system size, and can be readily implemented in state-of-the-art experiments. With numerical examples, we propose several applications of our method in the context of verification of quantum devices, from the characterization of the distinct sources of errors in digital quantum simulators to the design of new types of quantum gates. Furthermore, we show how our approach can be extended to the case of non-unitary dynamics and used to learn Floquet Liouvillians, thereby offering a way of characterizing the dissipative processes present in NISQ quantum devices.

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Section: Discussionmentioning

confidence: 99%

Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning

Pastori¹,

Olsacher²,

Kokail³

et al. 2022

Preprint

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show abstract

“…It is a time-consuming task, but it has to be performed once in the pre-compilation stage. [Moro et al, 2021] Although such a strategy could return a short circuit in minimal time, there is no guarantee that the agent will always find it. Hybrid approaches, where a planning algorithm such as A* is boosted by deep neural networks, could achieve even better performance [Zhang et al, 2020].…”

Section: Beyond the Solovay-kitaev Theoremmentioning

confidence: 99%

Quantum Compiling

Maronese¹,

Moro²,

Rocutto³

et al. 2021

Preprint

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Quantum compiling fills the gap between the computing layer of high-level quantum algorithms and the layer of physical qubits with their specific properties and constraints. Quantum compiling is a hybrid between the general-purpose compilers of computers, transforming high-level language to assembly language and hardware synthesis by hardware description language, where functions are automatically synthesized into customized hardware. Here we review the quantum compiling stack of both gate model quantum computers and the adiabatic quantum computers, respectively. The former involves low level qubit control, quantum error correction, synthesis of short quantum circuits, transpiling, while the latter involves the virtualization of qubits by embedding of QUBO and HUBO problems on constrained graphs of physical qubits and both quantum error suppression and correction. Commercial initiatives and quantum compiling products are reviewed, including explicit programming examples.

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“…While MPS-based algorithms have been used in the context of optimal many-body control to find highfidelity protocols that manipulate interacting ultracold quantum gases [17][18][19], the advantages of deep reinforcement learning (RL) for quantum control [20], have so far been investigated using exact simulations of only a small number of interacting quantum degrees of freedom. Nevertheless, policy-gradient and value-function RL algorithms have recently been established as useful tools in the study of quantum state preparation [21][22][23][24][25][26][27][28][29][30][31][32][33], quantum error correction and mitigation [34][35][36][37], quantum circuit design [38][39][40][41], and quantum metrology [42,43]; quantum reinforcement learning algorithms have been proposed as well [44][45][46][47][48]. Thus, in times of rapidly developing quantum simulators which exceed the computational capabilities of classical computers [49], the natural question arises regarding scaling up the size of quantum systems in RL control studies beyond exact diagonalization methods.…”

Section: Introductionmentioning

confidence: 99%

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Metz¹,

Bukov²

2022

Preprint

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Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL). We tackle the quantum control problem by leveraging matrix product states (i) for representing the many-body state and, (ii) as part of the trainable machine learning architecture for our RL agent. The framework is applied to prepare ground states of the quantum Ising chain, including critical states. It allows us to control systems far larger than neural-network-only architectures permit, while retaining the advantages of deep learning algorithms, such as generalizability and trainable robustness to noise. In particular, we demonstrate that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states, and of adapting control protocols on-the-fly when the quantum dynamics is subject to stochastic perturbations.

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Quantum compiling by deep reinforcement learning

Cited by 53 publications

References 42 publications

Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning

Characterization and Verification of Trotterized Digital Quantum Simulation via Hamiltonian and Liouvillian Learning

Quantum Compiling

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

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