Quantum Error Correction Under Control
Abstract
Research problem and objectives: Quantum error correction (QEC) provides methods for correcting the detrimental effects of environmental noise on quantum information processing. Quantum error correcting codes operate by exploiting redundancy and symmetry. QEC is usually abstracted as a discrete time process, in which encoding, the effect of noise, and decoding take place discretely and sequentially in time. In reality it is not possible to cleanly separate encoding or decoding from noise. Quantum control theory (QCT) provides complimentary methods for correcting the detrimental effects of environmental noise: control fields are applied continuously to drive the quantum system to a desired trajectory; environmental noise is added in continuously; dissipative effects and feedback are used to pump the entropy added by noise out of the system. Between the small and large spatio-temporal scales where QCT and QEC apply, respectively, there is a vast landscape of ubiquitous non-Markovian, correlated noise. It is made up of remnants of imperfections in gates and hardware, e.g., crosstalk and uncontrolled degrees of freedom. If this intermediate-scale noise is not addressed, then its complexity grows with that of the computation, and the noise correlations will span many spatio-temporal scales. Our main objective in this proposal is to marry the ideas and tools of QEC and QCT in order to develop a unifying framework that can successfully enable fault?tolerant quantum computation protected from noise at all spatio-temporal scales. Technical approaches: Understanding complex noise across many spatio-temporal scales requires deep and varied expertise. Yet a holistic integration of the tools of QCT and QEC is missing. We will close this gap by identifying the three spatio?temporal scales and bringing together theory experts to develop a comprehensive control framework, and experts in three different hardware designs: superconducting, semiconductor spin, and photonic qubits. Our approach stems from the fact that building an error-corrected quantum computer will require a holistic treatment of complex noise. In this manner, our comprehensive toolkit will be hardware agnostic while at the same time accounting for practical pitfalls. Our technical approach is based on a set of simultaneous, overarching Aims: (I) Develop a generalized QCT framework that benefits QEC, and from QEC. (II) Characterize and control spatio?temporal noise by building upon a recently developed, novel toolkit of quantum non-Markovian control (QNC) methods. (III) Develop a generalized QEC theory that benefits from QCT. (IV) Develop a generalized QEC, QNC and QCT framework that benefits from machine learning. (V) Experimentally characterize and control noise across spatio-temporal scales. (VI) Package all tools into one: demonstrate noise-resistant QEC with holistic control. Anticipated outcome of the research, if successful: The ultimate goal of the project is Aim VI: to realize functional QEC. That is, we will enhance the performance of QEC by holistically accounting for noise at all spatio-temporal scales. The three distinct toolkits we will bring to bear, namely QCT, QNC, and QEC--dedicated to eliminating noise at the small, intermediate, and large spatio-temporal scales--will suffice to control complex noise and push quantum hardware above the requisite fidelity threshold to successfully implement QEC codes. This is the key to building a fault-tolerant quantum computer. Impact on DoD capabilities: Quantum computing cannot unlock its potential without robust and reliable quantum error correction. This project will aim to make substantial progress in jointly and synergistically advancing the state of the art of QEC and QCT, through our direct testing and benchmarking over a range of leading qubit platforms (superconducting, solid-state, photonics) which will accelerate the arrival of scalable quantum computers in the service of DoD.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 28, 2023
- Source ID
- W911NF2310255
Entities
People
- Daniel Lidar
Organizations
- Army Contracting Command
- United States Army
- University of Southern California