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Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows

We explore the first-order phase transition in the lattice Schwinger model in the presence of a topological $\theta$-term by means of the variational quantum eigensolver (VQE). Using two different fermion discretizations, Wilson and staggered fermions, we develop parametric ansatz circuits suitable for both discretizations, and compare their performance by simulating classically an ideal VQE optimization in the absence of noise. The states obtained by the classical simulation are then prepared on the IBM's superconducting quantum hardware. Applying state-of-the art error-mitigation methods, we show that the electric field density and particle number, observables which reveal the phase structure of the model, can be reliably obtained from the quantum hardware. To investigate the minimum system sizes required for a continuum extrapolation, we study the continuum limit using matrix product states, and compare our results to continuum mass perturbation theory. We demonstrate that taking the additive mass renormalization into account is vital for enhancing the precision that can be obtained with smaller system sizes. Furthermore, for the observables we investigate we observe universality, and both fermion discretizations produce the same continuum limit.

High-fidelity, multi-qubit generalized measurements with dynamic circuits

2-qubit POVMs on superconducting qubits with decent fidelities using a big bag of tricks!

First-Order Phase Transition of the Schwinger Model with a Quantum Computer

We explore the first-order phase transition in the lattice Schwinger model in the presence of a topological $\theta$-term by means of the variational quantum eigensolver (VQE). Using two different fermion discretizations, Wilson and staggered fermions, we develop parametric ansatz circuits suitable for both discretizations, and compare their performance by simulating classically an ideal VQE optimization in the absence of noise. The states obtained by the classical simulation are then prepared on the IBM's superconducting quantum hardware. Applying state-of-the art error-mitigation methods, we show that the electric field density and particle number, observables which reveal the phase structure of the model, can be reliably obtained from the quantum hardware. To investigate the minimum system sizes required for a continuum extrapolation, we study the continuum limit using matrix product states, and compare our results to continuum mass perturbation theory. We demonstrate that taking the additive mass renormalization into account is vital for enhancing the precision that can be obtained with smaller system sizes. Furthermore, for the observables we investigate we observe universality, and both fermion discretizations produce the same continuum limit.

Programmable Simulations of Molecules and Materials with Reconfigurable Quantum Processors

We show how Rydberg atom arrays can be used to simulate high-spin Heisenberg Hamiltonians.

Simulating polaritonic ground states on noisy quantum devices

We simulate polaritonic systems on a quantum computer for the first time.

Machine learning for practical quantum error mitigation

We explore classical machine learning to accelerate quantum error mitigation on noisy quantum computers.

Efficient long-range entanglement using dynamic circuits

We use dynamic circuits to teleport CNOT gates across 100 qubits and prepare GHZ states.

Isolated Majorana mode in a quantum computer from a duality twist

Experimental investigation of the interplay of dualities, generalized symmetries, and topological defects is an important challenge in condensed matter physics and quantum materials. A simple model exhibiting this physics is the transverse-field Ising model, which can host a noninvertible topological defect that performs the Kramers-Wannier duality transformation. When acting on one point in space, this duality defect imposes the duality twisted boundary condition and binds a single Majorana zero mode. This Majorana zero mode is unusual as it lacks localized partners and has an infinite lifetime, even in finite systems. Using Floquet driving of a closed Ising chain with a duality defect, we generate this Majorana zero mode in a digital quantum computer. We detect the mode by measuring its associated persistent autocorrelation function using an efficient sampling protocol and a compound strategy for error mitigation. We also show that the Majorana zero mode resides at the domain wall between two regions related by a Kramers-Wannier duality. Finally, we highlight the robustness of the isolated Majorana zero mode to integrability and symmetry-breaking perturbations. Our findings offer an experimental approach to investigating exotic topological defects in Floquet systems

Uncovering Local Integrability in Quantum Many-Body Dynamics

Interacting many-body quantum systems and their dynamics, while fundamental to modern science and technology, are formidable to simulate and understand. However, by discovering their symmetries, conservation laws, and integrability one can unravel their intricacies. Here, using up to 124 qubits of a fully programmable quantum computer, we uncover local conservation laws and integrability in one- and two-dimensional periodically-driven spin lattices in a regime previously inaccessible to such detailed analysis. We focus on the paradigmatic example of disorder-induced ergodicity breaking, where we first benchmark the system crossover into a localized regime through anomalies in the one-particle-density-matrix spectrum and other hallmark signatures. We then demonstrate that this regime stems from hidden local integrals of motion by faithfully reconstructing their quantum operators, thus providing a detailed portrait of the system's integrable dynamics. Our results demonstrate a versatile strategy for extracting hidden dynamical structure from noisy experiments on large-scale quantum computers.

Best practices for quantum error mitigation with digital zero-noise extrapolation

Digital zero-noise extrapolation (dZNE) has emerged as a common approach for quantum error mitigation (QEM) due to its conceptual simplicity, accessibility, and resource efficiency. In practice, however, properly applying dZNE to extend the computational reach of noisy quantum processors is rife with subtleties. Here, based on literature review and original experiments on noisy simulators and real quantum hardware, we define best practices for QEM with dZNE for each step of the workflow, including noise amplification, execution on the quantum device, extrapolation to the zero-noise limit, and composition with other QEM methods. We anticipate that this effort to establish best practices for dZNE will be extended to other QEM methods, leading to more reproducible and rigorous calculations on noisy quantum hardware.