The semiconducting technologies appear to be an ideal platform to construct quantum systems, inheriting the classical silicon techniques and controllable circuits, and exploiting the quantum nature of confined particles. Meanwhile, quantum optimal control algorithms have been proven to be an efficient tool to assist quantum system manipulation, quantum computing and quantum applications. In this paper, we review some of the main quantum optimal control algorithms in closed quantum systems, and we explore their implementation to charge and spin-based semiconductor qubits. In addition, we propose a bang-bang control algorithm and we examine its performance in selected case studies.
The semiconducting technologies appear to be an ideal platform to construct quantum systems, inheriting the classical silicon techniques and controllable circuits, and exploiting the quantum nature of confined particles. Meanwhile, quantum optimal control algorithms have been proven to be an efficient tool to assist quantum system manipulation, quantum computing and quantum applications. In this paper, we review some of the main quantum optimal control algorithms in closed quantum systems, and we explore their implementation to charge and spin-based semiconductor qubits. In addition, we propose a bang-bang control algorithm and we examine its performance in selected case studies.
Abstract Quantum control allows a wide range of quantum operations employed in molecular physics, nuclear magnetic resonance and quantum information processing. Thanks to the existing microelectronics industry, semiconducting qubits, where quantum information is encoded in spin or charge degree freedom of electrons or nuclei in semiconductor quantum dots, constitute a highly competitive candidate for scalable solid-state quantum technologies. In quantum information processing, advanced control techniques are needed to realize quantum manipulations with both high precision and noise resilience. In this review, we first introduce the basics of various widely-used control methods, including resonant excitation, adabatic passage, shortcuts to adiabaticity, composite pulses, and quantum optimal control. Then we review the practical aspects in applying these methods to realize accurate and robust quantum gates for single semiconductor qubits, such as Loss–DiVincenzo spin qubit, spinglet-triplet qubit, exchange-only qubit and charge qubit.
Recently there has been a lot of interest in the potential applications of performing computations on systems whose governing physical laws are quantum, rather than classical in nature. These quantum computers would have the ability to perform some calculations which would not be feasible for their classical counterparts. To date, however, a quantum computer large enough to perform useful calculations has yet to be built. Before this can be accomplished, a method must be developed to control the underlying quantum systems. This is a problem which can naturally be formulated in the language of control theory. This report outlines the basic control-theoretic approach to time-optimally controlling quantum systems evolving under the dynamics of the Schr\\"{o}dinger operator equation. It is found that under the assumption of non-singularity, the controls which produce time-optimal trajectories are bang-bang. With this in mind, a switching time algorithm is implemented to find optimal bang-bang controls.
The gate-defined semiconductor quantum dot system, which can be manipulated electrically and fabricated using modern microelectronic technology, is considered as an ideal platform for quantum computation. The efficiency of quantum computation depends on the speed of gate operation. And charge qubit has quick operational speed and strong inter-qubit strength but with technical challenges. In this talk, I will introduce our experiments on ultrafast quantum control in semiconductor charge qubits including ultrafast universal quantum control of single charge qubit using LZS(Landau-Zener-Stückelberg) interference, conditional rotation of two strongly coupled qubits and static Toffoli gate of three qubits. Furthermore, to find a balance between coherence and operation speed we experimentally demonstrated tunable hybrid qubit in GaAs quantum dots system. Finally, we also focuse on cryogenic electronics for quantum computing, we have measured and modeled standard process CMOS at 77K, 4.2K and 300mK. CryoCMOS can be applied to the readout and control system of quantum chips in the future.
Quantum computation exploits superposition, entanglement, interference, and measurement to process information.Research has moved from theoretical algorithms to functional devices where the performance is not only limited by qubit physics, but also by the control, compilation, error mitigation, and error correction.This review considers quantum computing on various levels of abstraction, including physical qubits, quantum algorithms, system architecture, fault-tolerance, and emerging applications.Study-specific evidence regarding scale, operational quality, connectivity, control and suppression of errors is compared across the different types of superconducting circuits, trapped ions, semiconductor spin qubits, neutral atoms and photonic processors.The review of the fundamental algorithms, noisy intermediate-scale quantum computing, variational and hybrid quantum-classical approaches, quantum simulation, quantum machine learning, and security implications is also discussed.Measurable progress has been demonstrated in published experiments for logical-qubit operation, below-threshold surface-code memory, programmable photonic sampling and platformspecific scaling.However, the reported metrics are not directly interchangeable, and practical advantage remains task-and baseline-dependent.Further progress will need to be accompanied by coordinated developments in hardware reliability, error correction, classical control, estimates of algorithmic resources, and clear classical method comparisons.
Recent advancements in quantum technologies have highlighted the importance of mitigating system imperfections, including parameter uncertainties and decoherence effects, to improve the performance of experimental platforms. However, most of the previous efforts in quantum control are devoted to the realization of arbitrary unitary operations in a closed quantum system. Here, we improve the algorithm that suppresses system imperfections and noises, providing notably enhanced scalability for robust and optimal control of open quantum systems. Through experimental validation in a superconducting quantum circuit, we demonstrate that our approach outperforms its conventional counterpart for closed quantum systems with an ultra-low infidelity of about 0.60%, while the complexity of this algorithm exhibits the same scaling, with only a modest increase in the prefactor. This work represents a notable advancement in quantum optimal control techniques, paving the way for realizing quantum-enhanced technologies in practical applications.
We investigate the optimal charging processes for several models of quantum batteries, finding how to maximize the energy stored in a given battery with a finite-time modulation of a set of external fields. We approach the problem using advanced tools of optimal control theory, highlighting the universality of some features of the optimal solutions, for instance the emergence of the well-known bang-bang behavior of time-dependent external fields. The technique presented here is general, and we apply it to specific cases in which the energy is either pumped into the battery by external forces (direct charging) or transferred into it from an external charger (mediated charging). In this paper we focus on particular systems that consist of coupled qubits and harmonic oscillators, for which the optimal charging problem can be explicitly solved using a combined analytical-numerical approach based on our optimal control techniques. However, our approach can be applied to more complex setups, thus fostering the study of many-body effects in the charging process.