arXiv subjects

Helena Zhang

Publications and source records attributed to Helena Zhang.

7 recordsLinked to original sources

arXiv2025

Flow State: Humans Enabling AI Systems to Program Themselves

Compound AI systems, orchestrating multiple AI components and external APIs, are increasingly vital but face challenges in managing complexity, handling ambiguity, and enabling effective development workflows. Existing frameworks often introduce significant overhead, implicit complexity, or restrictive abstractions, hindering maintainability and iterative refinement, especially in Human-AI collaborative settings. We argue that overcoming these hurdles requires a foundational architecture prioritizing structural clarity and explicit control. To this end, we introduce Pocketflow, a platform centered on Human-AI co-design, enabled by Pocketflow. Pocketflow is a Python framework built upon a deliberately minimal yet synergistic set of core abstractions: modular Nodes with a strict lifecycle, declarative Flow orchestration, native hierarchical nesting (Flow-as-Node), and explicit action-based conditional logic. This unique combination provides a robust, vendor-agnostic foundation with very little code that demonstrably reduces overhead while offering the expressiveness needed for complex patterns like agentic workflows and RAG. Complemented by Pocket AI, an assistant leveraging this structure for system design, Pocketflow provides an effective environment for iteratively prototyping, refining, and deploying the adaptable, scalable AI systems demanded by modern enterprises.

cs.AI↗

arXiv2024

Empirical learning of dynamical decoupling on quantum processors

Dynamical decoupling (DD) is a low-overhead method for quantum error suppression. Despite extensive work in DD design, finding pulse sequences that optimally decouple computational qubits on noisy quantum hardware is not well understood. In this work, we describe how learning algorithms can empirically tailor DD strategies for any quantum circuit and device. We use a genetic algorithm-inspired search to optimize DD (GADD) strategies for IBM's superconducting-qubit based quantum processors. In all observed experimental settings, we find that empirically learned DD strategies significantly improve error suppression relative to canonical sequences, with relative improvement increasing with problem size and circuit sophistication. We leverage this to study mirror randomized benchmarking on 100 qubits, GHZ state preparation on 50 qubits, and the Bernstein-Vazirani algorithm on 27 qubits. We further demonstrate that our empirical learning method finds strategies, in time constant with increasing circuit width and depth, that provide stable performance over long periods of time without retraining and generalize to larger circuits when trained on small sub-circuit structures.

quant-ph↗

arXiv2023

Defining Standard Strategies for Quantum Benchmarks

As quantum computers grow in size and scope, a question of great importance is how best to benchmark performance. Here we define a set of characteristics that any benchmark should follow -- randomized, well-defined, holistic, device independent -- and make a distinction between benchmarks and diagnostics. We use Quantum Volume (QV) [1] as an example case for clear rules in benchmarking, illustrating the implications for using different success statistics, as in Ref. [2]. We discuss the issue of benchmark optimizations, detail when those optimizations are appropriate, and how they should be reported. Reporting the use of quantum error mitigation techniques is especially critical for interpreting benchmarking results, as their ability to yield highly accurate observables comes with exponential overhead, which is often omitted in performance evaluations. Finally, we use application-oriented and mirror benchmarking techniques to demonstrate some of the highlighted optimization principles, and introduce a scalable mirror quantum volume benchmark. We elucidate the importance of simple optimizations for improving benchmarking results, and note that such omissions can make a critical difference in comparisons. For example, when running mirror randomized benchmarking, we observe a reduction in error per qubit from 2% to 1% on a 26-qubit circuit with the inclusion of dynamic decoupling.

quant-ph↗

arXiv2022

Machine Learning based Discrimination for Excited State Promoted Readout

A limiting factor for readout fidelity for superconducting qubits is the relaxation of the qubit to the ground state before the time needed for the resonator to reach its final target state. A technique known as excited state promoted (ESP) readout was proposed to reduce this effect and further improve the readout contrast on superconducting hardware. In this work, we use readout data from IBM's five-qubit quantum systems to measure the effectiveness of using deep neural networks, like feedforward neural networks, and various classification algorithms, like k-nearest neighbors, decision trees, and Gaussian naive Bayes, for single-qubit and multi-qubit discrimination. These methods were compared to standardly used linear and quadratic discriminant analysis algorithms based on their qubit-state-assignment fidelity performance, robustness to readout crosstalk, and training time.

quant-ph↗

arXiv2022

Recursive Methods for Synthesizing Permutations on Limited-Connectivity Quantum Computers

We describe a family of recursive methods for the synthesis of qubit permutations on quantum computers with limited qubit connectivity. Two objectives are of importance: circuit size and depth. In each case we combine a scalable heuristic with a non-scalable, yet exact, synthesis. Our algorithms are applicable to generic connectivity constraints, scale favorably, and achieve close-to-optimal performance in many cases. We demonstrate the utility of these algorithms by optimizing the compilation of Quantum Volume circuits, and to disprove an old conjecture on reversals being the hardest permutation on a path.

quant-ph↗

arXiv2017

Parallel Position-Controlled Composite Quantum Logic Gates with Trapped Ions

We demonstrate parallel composite quantum logic gates with phases implemented locally through nanoscale movement of ions within a global laser beam of fixed pulse duration. We show that a simple four-pulse sequence suffices for constructing ideal arbitrary single-qubit rotations in the presence of large intensity inhomogeneities across the ion trap due to laser beam-pointing or beam-focusing. Using such sequences, we perform parallel arbitrary rotations on ions in two trapping zones separated by 700 $μ$m with fidelities comparable to those of our standard laser-controlled gates. Our scheme improves on current transport or zone-dependent quantum gates to include phase modulation with local control of the ion's confinement potential. This enables a scalable implementation of an arbitrary number of parallel operations on densely packed qubits with a single laser modulator and beam path.

quant-ph↗

arXiv2016

Iterative Precision Measurement of Branching Ratios Applied to 5P states in 88Sr+

We report on a method for measuring the branching ratios of dipole transitions of trapped atomic ions by performing nested sequences of population inversions. This scheme is broadly applicable and does not use ultrafast pulsed or narrow linewidth lasers. It is simple to perform and insensitive to experimental variables such as laser and magnetic field noise as well as ion heating. To demonstrate its effectiveness, we make the most accurate measurements thus far of the branching ratios of both 5P1/2 and 5P3/2 states in 88Sr+ with sub-1% uncertainties. We measure 17.175(27) for the branching ratio of 5P1/2-5S1/2, 15.845(71) for 5P3/2-5S1/2, and 0.05609(21) for 5P3/2-4D5/2, ten- fold and thirty-fold improvements in precision for 5P1/2 and 5P3/2 branching ratios respectively over the best previous experimental values.

physics.atom-ph↗