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Peter Zoller

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Bounded-Error Quantum Simulation for Reliable Many-Body Physics

This paper introduces a bounded-error quantum simulation framework that quantifies uncertainties in predictions from analog quantum simulators. It combines Hamiltonian and Lindbladian Learning to infer coherent and dissipative dynamics, propagating their uncertainties to simulated observables. This approach enables rigorous uncertainty bounds, transforming quantum simulators into quantitative scientific tools capable of addressing complex many-body problems with verifiable accuracy, bridging the gap between experimental platforms and predictive many-body physics, including digital quantum simulation.

Ground-State Energy Estimation in Quantum Simulators Using Global Control

A novel protocol uses global time evolution and Loschmidt echo measurements to estimate ground-state energies and other observables in quantum many-body systems. This method bypasses the need for controlled operations, making it applicable to analog simulators. The approach demonstrates significantly improved precision over direct energy measurements and maintains accuracy even with hundreds of modes and experimental imperfections.

Scalable Quantum Sensing via Non-Local Mass Superpositions in Atomic Networks

The proposed architecture leverages entangled atomic ensembles and optical clock qubits to emulate non-local mass superpositions across a programmable quantum network. By distributing Bell-type seed states via photonic channels and employing collective internal state addressing, the system creates a non-local Ramsey interferometer sensitive to gravitational redshift. This approach bypasses the spatial limitations of conventional interferometry while maintaining scalability to large atom numbers.

MPO Learning from Classical Shadows Scales Quantum State Tomography to 96 Qubits

This work introduces a protocol for learning matrix-product operator (MPO) representations of experimentally prepared quantum states using classical shadows from local randomized measurements. The tensor optimization proceeds sequentially, analogous to DMRG, and is provably efficient for short-range correlated and noisy states. The method was experimentally validated on a superconducting processor with up to 96 qubits, marking a significant scaling milestone for quantum state tomography in realistic hardware settings.