Holographic Analog Simulations and Multi-qubit Gates with Trapped-Ion Quantum Processors
Abstract
Quantum computers allow some problems to be solved with a superpolynomial improvement in the computational complexity with respect to their classical counterparts. Quantum information is generally hidden in a complex entangled quantum superposition and this restricts the class of problems amenable to quantum advantage, which still remains unknown. Promising near-term applications for which quantum processors can provide a significant advantage over classical computers are combinatorial optimization, calculations of low-temperature properties of correlated materials, dynamics of electrons in molecules, and simulating lattice gauge theories of particle physics. One of the most promising platforms with which to realize practical quantum computers and simulators are trapped ions. However, despite robust qubits and long coherence times, trapped-ion technology is not yet ready to fulfill these promises: for example the efficient encoding of combinatorial optimization problems, lattice gauge theories and several important models with topological order require multi-qubit interactions, which are not yet readily available in trapped-ion systems. Moreover, largescale quantum algorithms will inevitably need Quantum Error Correction (QEC), which requires high fidelity mid-circuit detection of errors via projective measurements of quantum correlations(error syndromes), as well as conditional feedback to apply corrections. In this project we aim to fundamentally enhance the quantum operations toolbox available for trapped-ion hardware, introducing parallel and multi-qubit gates, mid-circuit hiding and measurements operations, as well as new analog quantum simulation solutions to allow a more efficient mapping of problems of practical interest onto the quantum hardware. This program plans to enhance dramatically the capabilities of state-of-the-art trapped-ion quantum processors towards the long-term goal of enabling fault tolerance quantum computing. This program combines partial measurements, a necessary ingredient for quantum error correction protocols, and analog protocols and more efficient gates to generate entanglement in trapped-ion systems. These advances will allow to tackle combinatorial and machine learning problems that are closely related to applications of strategic interest for the Department of Defense, such as resource allocation and logistics. If successful, such protocols could find widespread use in a wide range of applications such as networks, transportation and even financial models. In the long run this program will be pivotal in the quest for quantum speed-up applied to optimization and machine learning problems of future naval relevance. Approved for Public Release
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jul 24, 2023
- Source ID
- N000142312665
Entities
People
- Guido Pagano
Organizations
- Office of Naval Research
- Rice University
- United States Navy