Tunable, Flexible, and Efficient Optimization of Control Pulses for Practical Qubits

Superconducting quantum computing optimization based on multi-objective deep reinforcement learning

Deep reinforcement learning is considered an effective technology in quantum optimization and can provide strategies for optimal control of complex quantum systems. More precise measurements require simulation control at multiple experimental stages. Based on this, we improved a multi-objective deep reinforcement learning method in mathematical convex optimization theory for multi-process quantum optimal control optimization. By setting the single-process quantum control optimization result as a multi-objective optimization truncation threshold and reward function transfer strategy, we finally gave a global optimal solution that considers multiple influencing factors, rather than a local optimal solution that only targets a certain error. This method achieved excellent computational results on superconducting qubits. Optimum control of multi-process quantum computing can be achieved only by regulating the microwave pulse parameters of superconducting qubits, and such a set of global parameter values and control strategies are given.

Fast-forwarding molecular ground state preparation with optimal control on analog quantum simulators

We show that optimal control of the electron dynamics is able to prepare molecular ground states, within chemical accuracy, with evolution times approaching the bounds imposed by quantum mechanics. We propose a specific parameterization of the molecular evolution only in terms of interaction already present in the molecular Hamiltonian. Thus, the proposed method solely utilizes quantum simulation routines, retaining their favorable scalings. Due to the intimate relationships between variational quantum algorithms and optimal control, we compare, when possible, our results with state-of-the-art methods in the literature. We found that the number of parameters needed to reach chemical accuracy and algorithmic scaling is in line with compact adaptive strategies to build variational Ansätze. The algorithm, which is also suitable for quantum simulators, is implemented by emulating a digital quantum processor (up to 16 qubits) and tested on different molecules and geometries spanning different degrees of electron correlation.

Controlling NMR spin systems for quantum computation

Nuclear magnetic resonance is arguably both the best available quantum technology for implementing simple quantum computing experiments and the worst technology for building large scale quantum computers that has ever been seriously put forward. After a few years of rapid growth, leading to an implementation of Shor's quantum factoring algorithm in a seven-spin system, the field started to reach its natural limits and further progress became challenging. Rather than pursuing more complex algorithms on larger systems, interest has now largely moved into developing techniques for the precise and efficient manipulation of spin states with the aim of developing methods that can be applied in other more scalable technologies and within conventional NMR. However, the user friendliness of NMR implementations means that they remain popular for proof-of-principle demonstrations of simple quantum information protocols.

Shaping the laser control landscape of a hydrogen transfer reaction by vibrational strong coupling. A direct optimal control approach

Controlling molecular reactivity by shaped laser pulses is a long-standing goal in chemistry. Here, we suggest a direct optimal control approach that combines external pulse optimization with other control parameters arising in the upcoming field of vibro-polaritonic chemistry for enhanced controllability. The direct optimal control approach is characterized by a simultaneous simulation and optimization paradigm, meaning that the equations of motion are discretized and converted into a set of holonomic constraints for a nonlinear optimization problem given by the control functional. Compared with indirect optimal control, this procedure offers great flexibility, such as final time or Hamiltonian parameter optimization. A simultaneous direct optimal control theory will be applied to a model system describing H-atom transfer in a lossy Fabry-Pérot cavity under vibrational strong coupling conditions. Specifically, optimization of the cavity coupling strength and, thus, of the control landscape will be demonstrated.

Optimal control of quantum state preparation and entanglement creation in two-qubit quantum system with bounded amplitude

We consider the optimal control problem in a two-qubit system with bounded amplitude. Two cases are studied: quantum state preparation and entanglement creation. Cost functions, fidelity and concurrence, are optimized over bang-off controls for various values of the total duration, respectively. For quantum state preparation problem, three critical time points are determined accurately, and optimal controls are estimated. A better estimation of the quantum speed limit is obtained, so is the time-optimal control. For entanglement creation problem, two critical time points are determined, one of them is the minimal time to achieve maximal entanglement (unit concurrence) starting from the product state. In addition, the comparisons between bang-off and chopped random basis (CRAB) are made.

Modulated super-Gaussian laser pulse to populate a dark rovibrational state of acetylene

A pulse-shaping technique in the mid-infrared spectral range based on pulses with a super-Gaussian temporal profile is considered for laser control. We show a realistic and efficient path to the population of a dark rovibrational state in acetylene (C2H2). The laser-induced dynamics in C2H2 are simulated using fully experimental structural parameters. Indeed, the rotation-vibration energy structure, including anharmonicities, is defined by the global spectroscopic Hamiltonian for the ground electronic state of C2H2 built from the extensive high-resolution spectroscopy studies on the molecule, transition dipole moments from intensities, and the effects of the (inelastic) collisions that are parameterized from line broadenings using the relaxation matrix [A. Aerts, J. Vander Auwera, and N. Vaeck, J. Chem. Phys. 154, 144308 (2021)]. The approach, based on an effective Hamiltonian, outperforms today's ab initio computations both in terms of accuracy and computational cost for this class of molecules. With such accuracy, the Hamiltonian permits studying the inner mechanism of theoretical pulse shaping [A. Aerts et al., J. Chem. Phys. 156, 084302 (2022)] for laser quantum control. Here, the generated control pulse presents a number of interferences that take advantage of the control mechanism to populate the dark state. An experimental setup is proposed for in-laboratory investigation.

Non-adiabatic quantum control of quantum dot arrays with fixed exchange using Cartan decomposition

In semiconductor spin qubits which typically interact through short-range exchange coupling, shuttling of spin is a practical way to generate quantum operations between distant qubits. Although the exchange is often tunable through voltages applied to gate electrodes, its minimal value can be significantly large, which hinders the applicability of existing shuttling protocols to such devices, requiring a different approach. In this work, we extend our previous results for double- and triple-dot systems, and describe a method for implementing spin state transfer in long chains of singly occupied quantum dots in a non-adiabatic manner. We make use of Cartan decomposition to break down the interacting problem into simpler problems in a systematic way, and use dynamical invariants to design smooth non-adiabatic pulses that can be implemented in devices with modest control bandwidth. Finally, we discuss the extensibility of our results to directed shuttling of spin states on two-dimensional lattices of quantum dots with fixed coupling. This article is part of the theme issue 'Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives'.

Information theoretical limits for quantum optimal control solutions: error scaling of noisy control channels

Accurate manipulations of an open quantum system require a deep knowledge of its controllability properties and the information content of the implemented control fields. By using tools of information and quantum optimal control theory, we provide analytical bounds (information-time bounds) to characterize our capability to control the system when subject to arbitrary sources of noise. Moreover, since the presence of an external noise field induces open quantum system dynamics, we also show that the results provided by the information-time bounds are in very good agreement with the Kofman-Kurizki universal formula describing decoherence processes. Finally, we numerically test the scaling of the control accuracy as a function of the noise parameters, by means of the dressed chopped random basis (dCRAB) algorithm for quantum optimal control.

Optimal Control of Families of Quantum Gates

Quantum optimal control (QOC) enables the realization of accurate operations, such as quantum gates, and supports the development of quantum technologies. To date, many QOC frameworks have been developed, but those remain only naturally suited to optimize a single targeted operation at a time. We extend this concept to optimal control with a continuous family of targets, and demonstrate that an optimization based on neural networks can find families of time-dependent Hamiltonians realizing desired classes of quantum gates in minimal time.

One decade of quantum optimal control in the chopped random basis

The chopped random basis (CRAB) ansatz for quantum optimal control has been proven to be a versatile tool to enable quantum technology applications such as quantum computing, quantum simulation, quantum sensing, and quantum communication. Its capability to encompass experimental constraints-while maintaining an access to the usually trap-free control landscape-and to switch from open-loop to closed-loop optimization (including with remote access-or RedCRAB) is contributing to the development of quantum technology on many different physical platforms. In this review article we present the development, the theoretical basis and the toolbox for this optimization algorithm, as well as an overview of the broad range of different theoretical and experimental applications that exploit this powerful technique.

Concurrent J-evolving refocusing pulses

Conventional refocusing pulses are optimised for a single spin without considering any type of coupling. However, despite the fact that most couplings will result in undesired distortions, refocusing in delay-pulse-delay-type sequences with desired heteronuclear coherence transfer might be enhanced considerably by including coupling evolution into pulse design. We provide a proof of principle study for a Hydrogen-Carbon refocusing pulse sandwich with inherent J-evolution following the previously reported ICEBERG-principle with improved performance in terms of refocusing performance and/or overall effective coherence transfer time. Pulses are optimised using optimal control theory with a newly derived quality factor and z-controls as an efficient tool to speed up calculations. Pulses are characterised in theory and experiment and compared to conventional concurrent refocusing pulses, clearly showing an improvement for the J-evolving pulse sandwich. As a side-product, also efficient J-compensated resfocusing pulse sandwiches - termed BUBU pulses following the nomenclature of previous J-compensated BUBI and BEBE^tr pulse sandwiches - have been optimised.

Laser control of a dark vibrational state of acetylene in the gas phase—Fourier transform pulse shaping constraints and effects of decoherence

We propose a methodology to tackle the laser control of a non-stationary dark ro-vibrational state of acetylene (C2H2), given realistic experimental limitations in the 7.7 μm (1300 cm−1) region. Simulations are performed using the Lindblad master equation, where the so-called Lindblad parameters are used to describe the effect of the environment in the dilute gas phase. A phenomenological representation of the parameters is used, and they are extracted from high-resolution spectroscopy line broadening data. An effective Hamiltonian is used for the description of the system down to the rotational level close to experimental accuracy. The quality of both the Hamiltonian and Lindblad parameters is assessed by a comparison of a calculated infrared spectrum with the available experimental data. A single shaped laser pulse is used to perform the control, where elements of optics and pulse shaping using masks are introduced with emphasis on experimental limitations. The optimization procedure, based on gradients, explicitly takes into account the experimental constraints. Control performances are reported for shaping masks of increasing complexity. Although modest performances are obtained, mainly due to the strong pulse shaping constraints, we gain insights into the control mechanism. This work is the first step toward the conception of a realistic experiment that will allow for population characterization and manipulation of a non-stationary vibrational “dark” state. Effects of the collisions on the laser control in the dilute gas phase, leading to decoherence in the molecular system, are clearly shown.

Simulating and Mitigating Crosstalk

We describe an efficient and scalable framework for modeling crosstalk effects on quantum information processors. By applying optimal control techniques, we show how to tune-up arbitrary high-fidelity parallel operations on systems with substantial local and nonlocal crosstalk. As an example, we simulate a 2D square array of 100 superconducting transmon qubits. These results suggest that rather than striving to engineer away undesirable interactions during fabrication, we can largely mitigate such effects with software through careful characterization and control optimization.