Analytic Approach to Quantum Control Using Quantum Signal Processing
- URL: http://arxiv.org/abs/2606.26085v1
- Date: Wed, 24 Jun 2026 17:56:12 GMT
- Title: Analytic Approach to Quantum Control Using Quantum Signal Processing
- Abstract summary: Quantum algorithms offer rigorous error guarantees for implementing unitary and non-unitary transformations.<n>QSP-Control is an analytical framework for quantum control of qubit-oscillator dynamics.
- Score: 4.52716230518765
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Realizing coherent quantum computation requires precise and robust manipulation of quantum systems through quantum control protocols. Most quantum control techniques rely on heuristic methods for designing the driving pulses that steer the system towards a target state. Such methods are often based on brute-force optimization and offer limited understanding of the solution landscape. In contrast, quantum algorithms offer a rich body of analytical methods with rigorous error guarantees for implementing unitary and non-unitary transformations, which suggests a promising direction for developing new approaches to quantum control. Among various such algorithms, quantum signal processing (QSP) has emerged as a powerful framework for quantum algorithm design, implementation, and optimization. However, its potential for quantum control remains largely unexplored. In this work, we establish QSP-Control, an analytical framework for quantum control of qubit-oscillator dynamics. We focus on dispersively coupled qubit-oscillator systems and employ the QSP formalism to mitigate unwanted nonlinear effects arising from cross-Kerr interactions. In addition, we develop constructions for precise manipulation of Fock states by designing Fock-state-selective operators, based on structural parallels between the Jaynes-Cummings interaction and QSP. These findings demonstrate how several practically relevant problems in quantum control can be mapped to forms amenable to QSP, offering both a systematic design framework and an interpretable perspective on quantum control.
Related papers
- VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
arXiv Detail & Related papers (2025-06-04T08:30:35Z) - Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
arXiv Detail & Related papers (2025-05-24T02:51:34Z) - Learning agent-based approach to the characterization of open quantum systems [0.08496348835248901]
We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism.<n>By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system.<n>We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors.
arXiv Detail & Related papers (2025-01-09T16:25:17Z) - Quantum control by the environment: Turing uncomputability, Optimization over Stiefel manifolds, Reachable sets, and Incoherent GRAPE [56.47577824219207]
In many practical situations, the controlled quantum systems are open, interacting with the environment.
In this note, we briefly review some results on control of open quantum systems using environment as a resource.
arXiv Detail & Related papers (2024-03-20T10:09:13Z) - Quantum Signal Processing with the one-dimensional quantum Ising model [0.0]
Quantum Signal Processing (QSP) has emerged as a promising framework to manipulate and determine properties of quantum systems.
We provide examples and applications of our approach in diverse fields ranging from space-time dual quantum circuits and quantum simulation, to quantum control.
arXiv Detail & Related papers (2023-09-08T18:01:37Z) - On-the-fly Tailoring towards a Rational Ansatz Design for Digital
Quantum Simulations [0.0]
It is imperative to develop low depth quantum circuits that are physically realizable in quantum devices.
We develop a disentangled ansatz construction protocol that can dynamically tailor an optimal ansatz.
The construction of the ansatz may potentially be performed in parallel quantum architecture through energy sorting and operator commutativity prescreening.
arXiv Detail & Related papers (2023-02-07T11:22:01Z) - Model-free Quantum Gate Design and Calibration using Deep Reinforcement
Learning [7.683965448804695]
We propose a novel training framework using deep reinforcement learning for model-free quantum control.
The proposed framework relies only on the measurement at the end of the control process and offers the ability to find the optimal control policy.
arXiv Detail & Related papers (2023-02-05T12:36:03Z) - Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the
Race to Practical Quantum Advantage [43.3054117987806]
We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits.
We show this method significantly improves the trainability and performance of PQCs on a variety of problems.
By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
arXiv Detail & Related papers (2022-08-29T15:24:03Z) - High-fidelity tracking of the evolution of multilevel quantum states [0.0]
The developed algorithms for quantum control are based on the use of the spinor representation of the Lorentz transformation group.
We show that feedback through weakly perturbing adaptive quantum measurements turns out to be capable of providing high-precision control of the quantum system.
arXiv Detail & Related papers (2022-01-09T19:23:09Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
We devise three effective QAE-based learning protocols to address three classically computational hard learning problems.
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.