Multi-Neutrino Entanglement and Correlations in Dense Neutrino Systems

The time-evolution of multi-neutrino entanglement and correlations are studied in collective neutrino oscillations, relevant for dense neutrino environments. Quantum simulations performed of systems with up to 12 neutrinos using Quantinuum’s H1-1 20 qubit trapped-ion quantum computer are used to compute n-tangles, and 2- and 3-body correlations, probing beyond mean field descriptions. n-tangle re-scalings are found to converge for large system sizes.

by Marc Illa and Martin Savage

A joint QSC-IQuS project

IQuS Publications

Gauge Invariance of Non-Abelian Field Strength Correlators: the Axial Gauge Puzzle

We have taken a step forward in the study of the properties of quarks and gluons, the particles that make up atomic nuclei, by resolving a long-standing issue with a theoretical calculation in axial gauge. IQuS and MIT researchers found that previous calculations had mistakenly suggested two properties of quark-gluon plasma were identical. We also made a prediction about gluon distribution measurements that could be tested in future experiments at the Electron-Ion Collider (EIC).

Gauge Invariance of Non-Abelian Field Strength Correlators: the Axial Gauge Puzzle

Randomized measurement protocols for lattice gauge theories

Randomized measurement protocols, including classical shadows, entanglement tomography, and randomized benchmarking are powerful techniques to estimate observables, perform state tomography, or extract the entanglement properties of quantum states. Quantum systems in nature are often tightly constrained by symmetries. This can be leveraged by the symmetry-conscious randomized measurement schemes, yielding clear advantages over symmetry-blind randomization such as reducing measurement costs, enabling symmetry-based error mitigation in experiments, allowing differentiated measurement of (lattice) gauge theory entanglement structure, and, potentially, the verification of topologically ordered states in existing and near-term experiments.

by Jacob Bringewatt, Jonathan Kunjummen, Niklas Mueller

IQuS Publications

IQuS Workshop: Next-Generation Computing for Low-Energy Nuclear Physics: from Machine Learning to Quantum Computing

Organizers: Zohreh Davoudi (U. of Maryland), Ubirajara van Kolck (U. of Arizona and Université Paris-Saclay), Silas Beane (UW), and Maria Piarulli (Washington U.)

Administation: Katie Hennessey

Both lattice QCD and ab initio many-body methods generally require beyond-exascale classical computation, and are expected to be facilitated by machine learning and quantum computing techniques. Ideas in quantum information science , such as the role of entanglement in nuclear processes, can impact our understanding of nuclear phenomena.

Preparations for Quantum Simulations of Quantum Chromodynamics in 1+1 Dimensions: (II) Single-Baryon β-Decay in Real Time

A framework for quantum simulations of real-time weak decays of hadrons and nuclei in a 2-flavor lattice theory in one spatial dimension is presented. Both quantum chromodynamics and flavor-changing weak interactions are included in the dynamics, the latter through four-Fermi effective operators. Quantum circuits which implement time evolution are developed and run on Quantinuum’s H1-1 20- qubit trapped ion system to simulate the β-decay of a single baryon on one lattice site. The potential intrinsic error-correction properties of this type of lattice theory are discussed and the leading lattice Hamiltonian required to simulate 0νββ-decay of nuclei induced by a neutrino Majorana mass term is provided.

by Roland Farrell, Ivan Chernyshev, Sarah Powell, Nikita Zemlevskiy, Marc Illa and Martin Savage

A joint QSC-IQuS project

IQuS Publication

Floquet Engineering Heisenberg from Ising Using Constant Drive Fields for Quantum Simulation

The time-evolution of an Ising model with large driving fields over discrete time intervals is shown to be reproduced by an effective XXZ-Heisenberg model at leading order in the inverse field strength. For specific orientations of the drive field, the dynamics of the XXX-Heisenberg model is reproduced. These approximate equivalences are expected to enable quantum devices that natively evolve qubits according to the Ising model to simulate more complex systems.

by Anthony Ciavarella, Stephan Caspar, Hersh Singh, Martin Savage, Pavel Lougovski

A joint FNAL_Quantised-IQuS-INT project

IQuS Publications

Scalable quantum circuits are used to prepare the vacuum of the Schwinger model on 100 qubits of IBM's superconducting-qubit quantum computers

The vacuum of the lattice Schwinger model is prepared on up to 100 qubits of IBM’s Eagle-processor quantum computers. A new algorithm to prepare the ground state of a gapped translationally- invariant system on a quantum computer is presented, which we call Scalable Circuits ADAPT- VQE (SC-ADAPT-VQE). This algorithm uses the exponential decay of correlations between distant regions of the ground state, together with ADAPT-VQE, to construct quantum circuits for state preparation that can be scaled to arbitrarily large systems.

by Roland Farrell, Marc Illa, Anthony Ciavarella and Martin Savage

A joint IQuS-QSC project

Scalable Circuits for Preparing Ground States on Digital Quantum Computers: The Schwinger Model Vacuum on 100 Qubits

Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model

We introduce an iterative hybrid-classical-quantum algorithm, Hamiltonian learning variational quantum eigensolver (HL-VQE), that simultaneously optimizes an effective Hamiltonian, thereby rearranging entanglement into the effective model space, and the associated ground-state wavefunction. HL-VQE is found to provide an exponential improvement in Lipkin-Meshkov-Glick model calculations throughout a significant fraction of the Hilbert space. Quantum simulations are implemented on IBM’s QExperience quantum computers and simulators for 1- and 2-qubit effective model spaces , and are shown to provide accurate and precise results, reproducing classical predictions.

Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model

InQubator for Quantum Simulation

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Multi-Neutrino Entanglement and Correlations in Dense Neutrino Systems

Gauge Invariance of Non-Abelian Field Strength Correlators: the Axial Gauge Puzzle

Randomized measurement protocols for lattice gauge theories

IQuS Workshop: Next-Generation Computing for Low-Energy Nuclear Physics: from Machine Learning to Quantum Computing

Preparations for Quantum Simulations of Quantum Chromodynamics in 1+1 Dimensions: (II) Single-Baryon β-Decay in Real Time

Floquet Engineering Heisenberg from Ising Using Constant Drive Fields for Quantum Simulation

Scalable quantum circuits are used to prepare the vacuum of the Schwinger model on 100 qubits of IBM's superconducting-qubit quantum computers

Quantum Simulations in Effective Model Spaces (I): Hamiltonian Learning-VQE using Digital Quantum Computers and Application to the Lipkin-Meshkov-Glick Model

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