Quantum Computing with Differentiable Quantum Transforms

Matteo, Olivia Di; Izaac, Josh; Bromley, Thomas R.; Hayes, Anthony; Lee, Christina; Schuld, Maria; Száva, Antal; Roberts, Chase; Killoran, Nathan

doi:10.1145/3592622

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…The idea of differentiation is commonplace in optimization and machine learning. DQTs provide a useful abstraction to differentiation and are implemented in the PennyLane library 17,39 . In previous sections, we already used differentiation in the context of PQCs.…”

Section: Quantum Optimizationmentioning

confidence: 99%

“…In this section, we describe a strategy for quantum error mitigation implemented under the powerful framework of differentiable quantum transforms (DQTs). 39 The idea of differentiation is commonplace in optimization and machine learning. DQTs provide a useful abstraction to differentiation and are implemented in the PennyLane library.…”

Section: Theorymentioning

confidence: 99%

“…DQTs provide a useful abstraction to differentiation and are implemented in the PennyLane library. 17,39 In previous sections, we already used differentiation in the context of PQCs. That is, if one views a PQC as a quantum function or quantum program S with m parameters 𝜃 = (𝜃 1 , 𝜃 2 , … , 𝜃 m ) then S is considered differentiable if dS∕d𝜃 i is defined for every possible value of each 𝜃 i .…”

Section: Theorymentioning

confidence: 99%

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Quantum optimization algorithms: Energetic implications

Hong Enriquez,

Badia,

Chapman

et al. 2024

Concurrency and Computation

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SummarySince the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least to more energy efficient than their classical counterparts; why feedback‐based quantum optimization is energy‐inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of 20. Finally, we use the NchooseK high‐level programming model to run optimization problems on both gate‐based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate‐based counterparts.

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Section: Quantum Optimizationmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

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Quantum optimization algorithms: Energetic implications

Hong Enriquez,

Badia,

Chapman

et al. 2024

Concurrency and Computation

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A differentiable quantum phase estimation algorithm

Castaldo,

Jahangiri,

Migliore

et al. 2024

Quantum Sci. Technol.

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The simulation of electronic properties is a pivotal issue in modern electronic structure theory, driving significant efforts over the past decades to develop protocols for computing energy derivatives. In this work, we address this problem by developing a strategy to integrate the quantum phase estimation algorithm within a fully differentiable framework. This is accomplished by devising a smooth estimator able to tackle arbitrary initial states. We provide analytical expressions to characterize the statistics and algorithmic cost of this estimator. Furthermore, we provide numerical evidence that the estimation accuracy is retained when an arbitrary state is considered and that it exceeds the one of standard majority rule. We explicitly use this procedure to estimate chemically relevant quantities, demonstrating our approach through ground-state and triplet excited state geometry optimization with simulations involving up to 19 qubits. This work paves the way for new quantum algorithms that combine interference methods and quantum differentiable programming.

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Quantum Computing with Differentiable Quantum Transforms

Cited by 2 publications

References 26 publications

Quantum optimization algorithms: Energetic implications

Quantum optimization algorithms: Energetic implications

A differentiable quantum phase estimation algorithm

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