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IEEE International Workshop on Workload Characterization (WWC)

Adaptive control of quantum computing systems

Robert L. Kosut, HERSCHEL A. RABITZ

Published Apr 23, 2004• 0 citations
Publisher

Abstract

In this paper we promote the use of adaptive control for deducing the external control fields required to make a quantum computer behave as desired in an uncertain environment. Adaptive systems can be classified as indirect or direct. In an indirect adaptive control system, data is used to first. determine parameters in a system model. Then the model is used to determine the control parameters in order to achieve a desired goal.

DOI
10.1109/phycon.2003.1237009

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In this paper we promote the use of adaptive control for deducing the external control fields required to make a quantum computer behave as desired in an uncertain environment. Adaptive systems can be classified as indirect or direct. In an indirect adaptive control system, data is used to first. determine parameters in a system model. Then the model is used to determine the control parameters in order to achieve a desired goal.
The single and two-qubit logic gates which are universal for building a quantum computer are not, as yet, produced "naturally" -error correction and fault tolerant constructions are required, and making these requires control.To meet the requisite stringent performance goals places resource demands both spatially (ancilla qubits for error correction) and temporally (complex well timed control signals).On-line adaptive tuning of initially good controls offers a possible means to significantly reduce these overhead requirements.Two methods are proposed for control tuning: (i) direct estimation of fidelity between the actual system and the desired (unitary) logic gate, and (ii) estimating model parameters via compressive sensing.Both methods are evaluated numerically for a single qubit system with Hamiltonian parameter uncertainty.
The single and two-qubit logic gates which are universal for building a quantum computer are not, as yet, produced “naturally” - error correction and fault tolerant constructions are required, and making these requires control. To meet the requisite stringent performance goals places resource demands both spatially (ancilla qubits for error correction) and temporally (complex well timed control signals). On-line adaptive tuning of initially good controls offers a possible means to significantly reduce these overhead requirements. Two methods are proposed for control tuning: (i) direct estimation of fidelity between the actual system and the desired (unitary) logic gate, and (ii) estimating model parameters via compressive sensing. Both methods are evaluated numerically for a single qubit system with Hamiltonian parameter uncertainty.
This paper investigates a novel approach to quantum state generation and control, leveraging adaptive parameter adjustment to dynamically tailor quantum states. Traditional quantum computing methods often rely on fixed parameters, limiting the system's versatility. Our work proposes a system that adjusts quantum state parameters based on input data, enabling more flexible and efficient quantum computation. This design incorporates a self-adaptive control mechanism to achieve this dynamic state evolution. The core claim of this paper is that the proposed method offers a significant improvement in terms of adaptability and computational efficiency compared to existing approaches. This paper details the design of the adaptive control system, explores its performance through simulations, and discusses potential future directions. Keywords: Quantum State, Adaptive Control, Quantum Computation, Self-Adaptation, Quantum Algorithm.
Quantum states represent the fundamental building blocks of quantum information, exhibiting inherent uncertainty and sensitivity to external influences. Traditional approaches to managing these states often rely on pre-defined parameters and static control schemes, limiting their adaptability to complex and dynamic environments. This paper introduces a novel quantum state space – the Adaptive Space of Quantum States – designed to automatically adjust quantum states based on computational goals and environmental noise, ultimately fostering more efficient and robust quantum computation. The core mechanism centers around a dynamic space construction, coupled with quantum error correction for optimized parameter adaptation. This offers a significant advancement over existing methods, enabling quantum algorithms to respond to changing conditions with unprecedented precision.
This paper introduces a novel dynamic self-adaptive quantum state optimization technique. Traditional quantum state optimization methods rely on static parameter settings, which are often inadequate for dynamic environments. Our approach leverages a dynamic adjustment of quantum state parameters, enabling precise control and optimization of quantum states. We propose a method that continuously refines the quantum state based on input data, achieving a significant improvement in state fidelity and control compared to static optimization techniques. The core mechanism revolves around a feedback loop that adjusts parameters to minimize errors and maximize desired outcomes. The resulting system demonstrates superior performance in various quantum control applications.
This paper presents a novel feedback learning algorithm for adaptive quantum control. The algorithm leverages reinforcement learning principles combined with quantum control theory to dynamically adjust control strategies based on the observed state and environment of a quantum system. The core mechanism involves constructing an adaptive quantum control feedback learning algorithm that enables autonomous optimization of the quantum system. The algorithm utilizes a state representation of the quantum system, a reward function reflecting the desired outcome, and a control policy updated through a reinforcement learning process. Mathematical formulations are provided to describe the system dynamics, the control policy, and the learning process. The proposed algorithm aims to overcome the limitations of traditional fixed-parameter quantum control methods by adapting to uncertainties and complex dynamics, ultimately leading to improved control performance and robustness. The theoretical framework presented here offers a foundation for developing advanced quantum control strategies in various applications, including quantum computation, quantum sensing, and quantum metrology.