Dr Mustafa Bakr

Abstract:

Superconducting quantum circuits are a key platform for advancing quantum information processing and simulation. Scaling efforts currently encounter challenges such as Josephson-junction fabrication yield, design frequency targeting, and long-range crosstalk arising both from spurious microwave modes and intrinsic interactions between qubits. We demonstrate a scalable 4x4 square lattice with low crosstalk, comprising 16 fixed-frequency transmon qubits with nearest-neighbor capacitive coupling that is implemented in a tileable, 3D-integrated circuit architecture with off-chip inductive shunting to mitigate spurious enclosure modes. We report on the design and comprehensive characterization, and show that our implementation achieves targeted device parameters with very low frequency spreads, long-range parasitic couplings and simultaneous single-qubit gate errors across the device. Our results provide a promising pathway toward a scalable superconducting square lattice topology for quantum error correction and simulation.

Abstract:

Scaling superconducting quantum processors to large qubit counts faces challenges in control-signal delivery, thermal management, and hardware complexity, particularly in achieving microwave signal multiplexing and long-distance quantum information routing at millikelvin temperatures. We propose a space-time modulated Josephson-junction metasurface architecture to generate and multiplex microwave control signals directly at millikelvin temperatures. Theoretical and numerical results demonstrate the generation of multiple frequency tones with controlled parameters, enabling efficient and scalable qubit control while minimizing thermal loads and wiring overhead. We derive the nonlinear wave equation governing this system, simulate beam steering and frequency conversion, and discuss the feasibility of experimental implementation. These results lay the groundwork for a next-generation cryogenic signal-delivery paradigm that may enable scaling of superconducting quantum processors to thousands of qubits without overwhelming limited dilution-refrigerator cooling power.

Abstract:

Fully automated self-driving laboratories promise high-throughput, large-scale scientific discovery by reducing repetitive labor. However, they require deep integration of laboratory knowledge, which is often unstructured, multimodal, and hard to incorporate into current AI systems. This paper introduces the “k-agents” framework, designed to support experimentalists in organizing laboratory knowledge and automating experiments with agents. The framework uses large-language-model-based agents to encapsulate laboratory knowledge, including available operations and methods for analyzing results. To automate experiments, execution agents break multistep procedures into agent-based state machines, interact with other agents to execute steps, and analyze results. These results drive state transitions, enabling closed-loop feedback control. We demonstrate the system on a superconducting quantum processor, where agents autonomously planned and executed experiments for hours, successfully producing and characterizing entangled quantum states at human-level performance. Our knowledge-based agent system opens new possibilities for managing laboratory knowledge and accelerating scientific discovery.