Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing

Morrell, Zachary; Vuffray, Marc; Lokhov, Andrey Y.; Bärtschi, Andreas; Albash, Tameem; Coffrin, Carleton

doi:10.1103/physrevapplied.19.034053

Cited by 3 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another area of interest is determining if there are any modifications that can be made to the core algorithm, such as the modification made to SVMC in SVMC TF, that result in more accurate simulations of QA. One such example can be random longitudinal field noise, which was shown in [27] to be necessary to accurately model the dynamics of QA.…”

Section: Discussionmentioning

confidence: 99%

Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Zielewski,

Takahashi,

Shimomura

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Despite increases in qubit count and connectivity in quantum annealers, a quantum speedup has yet to be observed for problems of practical significance. In order to further improve annealer performance, some researchers focus on tuning annealer parameters, such as the annealing schedule. In this work, we focus on pausing, an annealing schedule modification that has been shown to improve the probability of solving an optimization problem by orders of magnitude. However, a challenge associated with pausing is selecting an appropriate pause location, as pausing is only effective in problem-dependent regions and ineffective elsewhere. Moreover, there is little advice on how to determine where pausing is effective. Thus, pausing effectively is difficult and often inaccessible to the majority of users. To address these issues, we propose a data-driven method that leverages machine learning to predict optimal pause locations. First, we construct a dataset consisting of optimization problems and their corresponding optimal pause locations. The optimal pause locations are determined using spin-vector Monte Carlo, a method known to yield results similar to quantum annealing. Next, we train a convolutional neural network on this dataset, demonstrating its ability to distinguish between problem types and accurately predict the optimal pause location. Finally, we evaluate the model on multiple types of optimization problems. Our results show that the pause locations predicted by our method improve solution quality for all selected problem types. Additionally, our model can be pretrained and easily distributed, making the power of pausing accessible to users unfamiliar with annealing schedule modifications.

show abstract

Section: Discussionmentioning

confidence: 99%

Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Zielewski,

Takahashi,

Shimomura

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To address Q, we consider as a platform a generic system of spins with two-body interactions, such as those currently programmable in quantum annealers. Their open system dynamics is starting to be studied [50][51][52][53][54], and they represent flexible physical devices with a high degree of control. More specifically, we consider a Hamiltonian of the form:…”

Section: Via Realistic Hamiltonians Ie Featuring Two-body and Local I...mentioning

confidence: 99%

Optimal thermometers with spin networks

Abiuso,

Andrea Erdman,

Ronen

et al. 2024

Quantum Sci. Technol.

View full text Add to dashboard Cite

The heat capacity C of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, C is limited by a quadratic scaling with the number of constituents of the probe, which provides a fundamental limit in quantum thermometry. Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem. In this work, we tackle the problem of engineering optimal thermometers by using networks of spins. Restricting ourselves to two-body interactions, we derive general properties of the optimal conﬁgurations and exploit machine-learning techniques to ﬁnd the optimal couplings. This leads to simple architectures, which we show analytically to approximate the theoretical maximal value of C and maintain the optimal scaling for short- and long-range interactions. Our models can be encoded in currently available quantum annealers, and ﬁnd application in other tasks requiring Hamiltonian engineering, ranging from quantum heat engines to adiabatic Grover’s search.

show abstract

Mapping state transition susceptibility in quantum annealing

Pelofske

2023

Phys. Rev. Research

View full text Add to dashboard Cite

Signatures of Open and Noisy Quantum Systems in Single-Qubit Quantum Annealing

Cited by 3 publications

References 55 publications

Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Optimal thermometers with spin networks

Mapping state transition susceptibility in quantum annealing

Contact Info

Product

Resources

About