Precise Hamiltonian identification of a superconducting quantum processor

Hangleiter, Dominik; Roth, Ingo; Eisert, Jens; Roushan, Pedram

doi:10.48550/arxiv.2108.08319

Cited by 12 publications

(9 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3 With the advent of versatile and universal quantum simulators and computers, ways have been developed of both characterising directly and verifying the features of the object of interest in an experimental scenario -the quantum state, Hamiltonian, or process (Eisert et al, 2020). But also for analogue systems, methods for direct validation of the experimentally implemented object of interest have been developed, including in particular the identification of the Hamiltonian or Liouvillian parameters (Hangleiter et al, 2021;Samach et al, 2021), benchmarking of Hamiltonian time-evolution across the parameter range accessible in the experiment (Helsen et al, 2020;Derbyshire et al, 2020;Shaffer et al, 2021), and fidelity estimation of a quantum state (Elben et al, 2020). In another vein, it has also been argued that analogue simulations might often be insensitive to certain details of the experiment, for example due to slack in the model space (Sarovar et al, 2017), or because certain noise processes affect both the simulator and the target in the same way (Cubitt et al, 2018).…”

Section: Internal Validity Of Analogue Quantum Simulationsmentioning

confidence: 99%

How to engineer a quantum wavefunction

Evans¹,

Hangleiter²,

Thébault³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

In a conventional experiment, inductive inferences between source and target systems are typically justified with reference to a uniformity principle between systems of the same material type. In an analogue quantum simulation, by contrast, scientists aim to learn about target quantum systems of one material type via an experiment on a source quantum system of a different material type. In this paper, we argue that such an inference can be justified by reference to the two quantum systems being of the same empirical type. We illustrate this novel experimental practice of wavefunction engineering with reference to the example of Bose-Hubbard systems.

show abstract

Section: Internal Validity Of Analogue Quantum Simulationsmentioning

confidence: 99%

How to engineer a quantum wavefunction

Evans¹,

Hangleiter²,

Thébault³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our approach builds on recently developed techniques of Hamiltonian learning (HL). These were developed as protocols to infer Hamiltonians of closed systems from steady states [47][48][49][50][51][52][53][54], as well as methods for reconstructing Hamiltonians governing the time evolution in quench dynamics [55][56][57][58][59][60][61] from measurements performed on the quantum device. In our context, the goal is to infer the operator content of ĤF (τ ) (see Eq.…”

Section: Introductionmentioning

confidence: 99%

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

Pastori¹,

Olsacher²,

Kokail³

et al. 2022

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Such constraints have been used in machine learning [44] and are also starting to become more popular in the quantum information literature, see e.g. [46][47][48]. In order to deal with the non-convex optimization landscape we adopt a second order saddle-free Newton method [49] to this setting.…”

Section: Introductionmentioning

confidence: 99%