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This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this…

Quantum Physics · Physics 2025-01-07 Nayeli A. Rodríguez-Briones , Daniel K. Park

In this work, we experimentally demonstrate the implementation of a recently proposed robust and state-independent heat-bath algorithmic cooling (HBAC) method [1] on an NMR quantum processor. While HBAC methods improve the purity of a…

Quantum Physics · Physics 2024-11-05 Krishna Shende , Arvind , Kavita Dorai

For adiabatic controls of quantum systems, the non-adiabatic transitions are reduced by increasing the operation time of processes. Perfect quantum adiabaticity usually requires the infinitely slow variation of control parameters. In this…

Quantum Physics · Physics 2022-07-01 Jin-Fu Chen

Adiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the…

Quantum Physics · Physics 2014-06-26 Constantin Brif , Matthew D. Grace , Mohan Sarovar , Kevin C. Young

A well-known approach to describe the dynamics of an open quantum system is to compute the master equation evolving the reduced density matrix of the system. This approach plays an important role in describing excitation transfer through…

Quantum Physics · Physics 2022-10-25 Kimara Naicker , Ilya Sinayskiy , Francesco Petruccione

Obtaining adiabatic processes that connect equilibrium states in a given time represents a challenge for mesoscopic systems. In this paper, we explicitly show how to build these finite-time adiabatic processes for an overdamped Brownian…

Mesoscale and Nanoscale Physics · Physics 2020-04-01 C. A. Plata , D. Guéry-Odelin , E. Trizac , A. Prados

We introduce high-order dynamical decoupling strategies for open system adiabatic quantum computation. Our numerical results demonstrate that a judicious choice of high-order dynamical decoupling method, in conjunction with an encoding…

Quantum Physics · Physics 2013-04-23 Gregory Quiroz , Daniel A. Lidar

Bose-Einstein condensates (BECs) offer the potential to examine quantum behavior at large length and time scales, as well as forming promising candidates for quantum technology applications. Thus, the manipulation of BECs using control…

Quantum Physics · Physics 2016-05-25 David Hocker , Julia Yan , Herschel Rabitz

Algorithmic cooling shows that it is possible to locally reduce the entropy of a qubit belonging to an isolated ensemble such as nuclear spins in molecules or nitrogen-vacancy centers in diamonds. In the same physical setting, we introduce…

A computational model of adiabatic evolutionary quantum system (or AEQS, pronounced "eeh-ks") was introduced in [Yamakami,2022] as a sort of quantum annealing and its underlying input-driven Hamiltonians are generated…

Quantum Physics · Physics 2025-11-25 Tomoyuki Yamakami

Heat-Bath Algorithmic Cooling techniques (HBAC) are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The…

Quantum Physics · Physics 2022-02-23 Zahra Farahmand , Reyhaneh Aghaei Saem , Sadegh Raeisi

Applications of Bose-Einstein Condensates (BEC) often require that the condensate be prepared in a specific complex state. Optimal control is a reliable framework to prepare such a state while avoiding undesirable excitations, and, when…

Quantum Gases · Physics 2022-03-14 Jimmie Adriazola , Roy H. Goodman

A general time-dependent quantum system can be driven fast from its initial ground state to its final ground state without generating transitions by adding a steering term to the Hamiltonian. We show how this technique can be modified to…

Quantum Physics · Physics 2018-12-11 A. Barış Özgüler , Robert Joynt , Maxim G. Vavilov

We report on the realization of Bose-Einstein condensation of metastable helium-4. After exciting helium to its metastable state in a novel pulse-tube cryostat source, the atomic beam is collimated and slowed. We then trap several 10^8…

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic…

Adiabatic processes driven by non-Hermitian, time-dependent Hamiltonians may be sped up by generalizing inverse engineering techniques based on Berry's transitionless driving algorithm or on dynamical invariants. We work out the basic…

Quantum Physics · Physics 2015-09-18 S. Ibáñez , S. Martínez-Garaot , Xi Chen , E. Torrontegui , J. G. Muga

Adiabatic passage is a standard tool for achieving robust transfer in quantum systems. We show that, in the context of driven nonlinear Hamiltonian systems, adiabatic passage becomes highly non-robust when the target is unstable. We show…

Quantum Physics · Physics 2020-11-11 Jing-Jun Zhu , Xi Chen , Hans-Rudolf Jauslin , Stéphane Guérin

Simulating the irreversible quantum dynamics of exciton and electron transfer problems poses a nontrivial challenge. Because the irreversibility of the system dynamics is a result of quantum thermal activation and dissipation caused by the…

Chemical Physics · Physics 2021-03-30 Seiji Ueno , Yoshitaka Tanimura

Adiabatic quantum computing is a universal model for quantum computing whose implementation using a gate-based quantum computer requires depths that are unreachable in the early fault-tolerant era. To mitigate the limitations of near-term…

Quantum Physics · Physics 2024-10-18 Ioannis Kolotouros , Ioannis Petrongonas , Miloš Prokop , Petros Wallden

A new implementation of an adiabatically-trained ensemble model is derived that shows significant improvements over classical methods. In particular, empirical results of this new algorithm show that it offers not just higher performance,…

Machine Learning · Computer Science 2022-10-17 Salvatore Certo , Andrew Vlasic , Daniel Beaulieu