arXiv subjects

Francisco Andrés Cárdenas-López

Publications and source records attributed to Francisco Andrés Cárdenas-López.

4 recordsLinked to original sources

arXiv2026

Analytical blueprint for 99.999% fidelity X-gates on present superconducting hardware under strong driving

Achieving very fast gates that undercut the natural limits set by decoherence requires going into the strong driving limit. Realizing single-qubit control predicted beyond semi-classical, time-dependent modeling has yet to be experimentally realized on superconducting and most other computing platforms. In this regime, the common model of dynamics within a three-level manifold breaks down, and instead, we see new quantum error channels growing abruptly with decreasing time. To identify these error processes we systematically calculate the effect of multi-photon transitions that occur out of the computational space. We then derive analytical formulas to suppress these effects, as well as amplitude and phase errors on the qubit space; we term these R1D for suppressing the $|0\rangle-|2\rangle$ transition and R2D when also suppressing $|1\rangle-|3\rangle$ leakage. We also answer long-standing questions about the optimal values of the DRAG prefactor as well as constant detuning, when accounting for time-ordering, and also show how to calibrate other prefactors for further performance improvement. Upon correcting these varied sources of error, we numerically demonstrate gate infidelities below $10^{-5}$ for a 7ns $π$-rotation when incorporating existing decoherence rates.

quant-ph↗

arXiv2026

Real-time adaptive quantum error correction by model-free multi-agent learning

Quantum error correction (QEC) is essential for scalable quantum computing, yet existing approaches rely on static assumptions about noise that break down in realistic hardware, where error channels drift over time. We introduce a unified framework that separates QEC into two learning timescales: offline code discovery and online adaptation. Offline, Multi-Agent Reinforcement Learning (MARL) autonomously discovers complete QEC cycles as explicit quantum circuits, with separate agents responsible for encoding, syndrome extraction, and error recovery, and without prescribing a code family or circuit ansatz. Online, a lightweight adaptive layer, termed Bandit Retraining for Adaptive Variational Error Correction (BRAVE), continuously retunes a low-dimensional variational parameterization without retraining the full MARL stack. This yields a "discover once, adapt continuously" strategy that combines the flexibility of learned codes with real-time adaptation to non-stationary noise. At sufficiently high sampling rates relative to the noise drift, our method reduces logical infidelity by roughly 18-fold for qubit codes and 3-fold for qutrit codes compared to static error correction, while substantially extending robustness to noise fluctuations. These results establish a paradigm in which QEC is no longer static but is dynamically optimized for realistic quantum hardware.

quant-ph↗

arXiv2026

Kraus map closed-form solution for general master equation dynamics

The Kraus representation of quantum channels allows for a precise emulation of the complex dynamics that take place on quantum processors, whether for benchmarking algorithms, predicting the performance of error correction and mitigation, or in the myriad other uses of compiled digital sequences. Nonetheless, starting from first principles to obtain continuous quantum master equations involves various approximations such as weak coupling to the environment. Further, converting these equations to Kraus operators cannot generally be obtained in closed-form due to the complicated commutator structure of the problem. In our work, we bridge this gap by providing a general closed form formulation for arbitrarily strong driving while remaining linear in the dissipator. The Kraus solution is expressed as a Riemann sum where higher terms can converge quickly to high precision, which we demonstrate numerically. Such a formulation is highly relevant to quantum computing and gate-based models, where effective models are highly sought for large rotation gate angles, even under the influence of underlying non-trivial noise mechanisms.

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arXiv2024

Pulse-based variational quantum optimization and metalearning in superconducting circuits

Solving optimization problems using variational algorithms stands out as a crucial application for noisy intermediate-scale devices. Instead of constructing gate-based quantum computers, our focus centers on designing variational quantum algorithms within the analog paradigm. This involves optimizing parameters that directly control pulses, driving quantum states towards target states without the necessity of compiling a quantum circuit. In this work, we introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external fluxes on superconducting quantum interference devices, effectively driving the wave function of this specific quantum architecture to the ground state of an encoded problem Hamiltonian. Given that the performance of variational algorithms heavily relies on appropriate initial parameters, we introduce a global optimizer as a meta-learning technique to tackle a simple problem. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.

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