arXiv subjects

J. M. Bello-Rivas

Publications and source records attributed to J. M. Bello-Rivas.

3 recordsLinked to original sources

arXiv2025

Repeated ancilla reuse for logical computation on a neutral atom quantum computer

Quantum processors based on neutral atoms trapped in arrays of optical tweezers have appealing properties, including relatively easy qubit number scaling and the ability to engineer arbitrary gate connectivity with atom movement. However, these platforms are inherently prone to atom loss, and the ability to replace lost atoms during a quantum computation is an important but previously elusive capability. Here, we demonstrate the ability to measure and re-initialize, and if necessary replace, a subset of atoms while maintaining coherence in other atoms. This allows us to perform logical circuits that include single and two-qubit gates as well as repeated midcircuit measurement while compensating for atom loss. We highlight this capability by performing up to 41 rounds of syndrome extraction in a repetition code, and combine midcircuit measurement and atom replacement with real-time conditional branching to demonstrate heralded state preparation of a logically encoded Bell state. Finally, we demonstrate the ability to replenish atoms in a tweezer array from an atomic beam while maintaining coherence of existing atoms -- a key step towards execution of logical computations that last longer than the lifetime of an atom in the system.

quant-ph↗

arXiv2024

Demonstration of logical qubits and repeated error correction with better-than-physical error rates

The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a trapped-ion QCCD processor where, through the use of fault-tolerant encoding and error correction, we are able to suppress logical error rates to levels below the physical error rates. In particular, we entangled logical qubits encoded in the [[7,1,3]] code with error rates 9.8 times to 500 times lower than at the physical level, and entangled logical qubits encoded in a [[12,2,4]] code based on Knill's C4/C6 scheme with error rates 4.7 times to 800 times lower than at the physical level, depending on the judicious use of post-selection. Moreover, we demonstrate repeated error correction with the [[12,2,4]] code, with logical error rates below physical circuit baselines corresponding to repeated CNOTs, and show evidence that the error rate per error correction cycle, which consists of over 100 physical CNOTs, approaches the error rate of two physical CNOTs. These results signify a transition from noisy intermediate scale quantum computing to reliable quantum computing, and demonstrate advanced capabilities toward large-scale fault-tolerant quantum computing.

quant-ph↗

arXiv2023

Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds

Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional manifold $M$. We introduce an approach to locate saddle points (and other fixed points) that utilizes gradient extremals on such a priori unknown (Riemannian) manifolds, defined by adaptively sampled point clouds, with local coordinates discovered on-the-fly through manifold learning. The technique, which efficiently biases the dynamical system along a curve (as opposed to exhaustively exploring the state space), requires knowledge of a single minimum and the ability to sample around an arbitrary point. We demonstrate the effectiveness of the technique on the M\"uller-Brown potential mapped onto an unknown surface (namely, a sphere). Previous work employed a similar algorithmic framework to find saddle points using Newton trajectories and gentlest ascent dynamics; we therefore also offer a brief comparison with these methods.

math.DS↗