Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Bulletin of the American Physical Society

Optimised counderdiabatic driving with additional terms

Ieva Čepaitė, C. S. Duncan, Andrew J. Daley, Anatoli Polkovnikov

Published Mar 18, 2021• 0 citations
OpenAlex

Related papers

Generalised Counterdiabatic Driving in Open Systems

Anthony Kiely

•Oct 5, 2020•0 citations

Counterdiabatic Optimised Local Driving

Ieva Čepaitė, Anatoli Polkovnikov, Andrew J. Daley +1 more

Adiabatic protocols are employed across a variety of quantum technologies, from implementing state preparation and individual operations that are building blocks of larger devices, to higher-level protocols in quantum annealing and adiabatic quantum computation. The problem of speeding up these processes has garnered a large amount of interest, resulting in a menagerie of approaches, most notably quantum optimal control and shortcuts to adiabaticity. The two approaches are complementary: optimal control manipulates control fields to steer the dynamics in the minimum allowed time while shortcuts to adiabaticity aim to retain the adiabatic condition upon speed-up. We outline a new method which combines the two methodologies and takes advantage of the strengths of each. The new technique improves upon approximate local counterdiabatic driving with the addition of time-dependent control fields. We refer to this new method as counterdiabatic optimised local driving (COLD) and we show that it can result in a substantial improvement when applied to annealing protocols, state preparation schemes, entanglement generation and population transfer on a lattice. We also demonstrate a new approach to the optimisation of control fields which does not require access to the wavefunction or the computation of system dynamics. COLD can be enhanced with existing advanced optimal control methods and we explore this using the chopped randomised basis method and gradient ascent pulse engineering.

arXiv:2203.01948•Mar 3, 2022•5 citations

Counterdiabatic-influenced Floquet-engineering: State preparation, annealing and learning the adiabatic gauge potential

Callum W. Duncan

Counterdiabatic driving, which enforces adiabatic evolution in arbitrary timescales, can be realised by engineering a Floquet Hamiltonian which oscillates between the Hamiltonian and its derivative requiring no additional control terms. However, the coefficients of the Floquet Hamiltoinan require knowledge of the counterdiabatic terms, which can be difficult to derive outside of a limited set of examples. We introduce a new hybrid technique for the control of quantum systems, Counterdiabatic-influenced Floquet-engineering or CAFFEINE for short. CAFFEINE parameterises the Floquet Hamiltonian for counterdiabatic driving and utilises numerical quantum optimal control in order to obtain the desired target state. This removes the need to both obtain and implement counterdiabatic terms, however, it does require the ability to quickly oscillate each term in the Hamiltonian. If this oscillation is possible, then CAFFEINE provides a framework to implement quantum annealing protocols and general quantum state preparation. We illustrate this through optimisation of two numerical examples of preparing a Bell state with two qubits and performing annealing protocols for the one-dimensional Ising model. Beyond this, we also illustrate CAFFEINE's capabilities to learn the counterdiabatic terms, which can potentially be used as a probe of quantum chaos and the geometry of quantum dynamics.

arXiv:2501.14881•Jan 24, 2025•0 citations

Excess work in counterdiabatic driving

Lucas P. Kamizaki, Marcus V. S. Bonança

Physical Review A•Mar 23, 2026•0 citations

Quantum optimisation for continuous multivariable functions by a structured search

Edric Matwiejew, Jason Pye, Jingbo B. Wang

Abstract Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms (QVAs) leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems. In addition, these algorithms have been designed generally under the assumption of an unstructured solution space, which constrains their speedup to the theoretical limits for the unstructured Grover’s quantum search algorithm. In this paper, we show that QVAs can efficiently optimise continuous multivariable functions by exploiting general structural properties of a discretised continuous solution space with a convergence that exceeds the limits of an unstructured quantum search. We present the quantum multivariable optimisation algorithm and demonstrate its advantage over pre-existing methods, particularly when optimising high-dimensional and oscillatory functions.

arXiv:2210.06227•Quantum Science and Technology•Jul 12, 2023•7 citations

Few Body Dynamics

Elsevier eBooks•Jan 1, 1976•0 citations
Adiabatic protocols are employed across a variety of quantum technologies, from implementing state preparation and individual operations that are building blocks of larger devices, to higher-level protocols in quantum annealing and adiabatic quantum computation. The problem of speeding up these processes has garnered a large amount of interest, resulting in a menagerie of approaches, most notably quantum optimal control and shortcuts to adiabaticity. The two approaches are complementary: optimal control manipulates control fields to steer the dynamics in the minimum allowed time while shortcuts to adiabaticity aim to retain the adiabatic condition upon speed-up. We outline a new method which combines the two methodologies and takes advantage of the strengths of each. The new technique improves upon approximate local counterdiabatic driving with the addition of time-dependent control fields. We refer to this new method as counterdiabatic optimised local driving (COLD) and we show that it can result in a substantial improvement when applied to annealing protocols, state preparation schemes, entanglement generation and population transfer on a lattice. We also demonstrate a new approach to the optimisation of control fields which does not require access to the wavefunction or the computation of system dynamics. COLD can be enhanced with existing advanced optimal control methods and we explore this using the chopped randomised basis method and gradient ascent pulse engineering.
Counterdiabatic driving, which enforces adiabatic evolution in arbitrary timescales, can be realised by engineering a Floquet Hamiltonian which oscillates between the Hamiltonian and its derivative requiring no additional control terms. However, the coefficients of the Floquet Hamiltoinan require knowledge of the counterdiabatic terms, which can be difficult to derive outside of a limited set of examples. We introduce a new hybrid technique for the control of quantum systems, Counterdiabatic-influenced Floquet-engineering or CAFFEINE for short. CAFFEINE parameterises the Floquet Hamiltonian for counterdiabatic driving and utilises numerical quantum optimal control in order to obtain the desired target state. This removes the need to both obtain and implement counterdiabatic terms, however, it does require the ability to quickly oscillate each term in the Hamiltonian. If this oscillation is possible, then CAFFEINE provides a framework to implement quantum annealing protocols and general quantum state preparation. We illustrate this through optimisation of two numerical examples of preparing a Bell state with two qubits and performing annealing protocols for the one-dimensional Ising model. Beyond this, we also illustrate CAFFEINE's capabilities to learn the counterdiabatic terms, which can potentially be used as a probe of quantum chaos and the geometry of quantum dynamics.
Abstract Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms (QVAs) leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems. In addition, these algorithms have been designed generally under the assumption of an unstructured solution space, which constrains their speedup to the theoretical limits for the unstructured Grover’s quantum search algorithm. In this paper, we show that QVAs can efficiently optimise continuous multivariable functions by exploiting general structural properties of a discretised continuous solution space with a convergence that exceeds the limits of an unstructured quantum search. We present the quantum multivariable optimisation algorithm and demonstrate its advantage over pre-existing methods, particularly when optimising high-dimensional and oscillatory functions.