Improving Quantum Circuit Synthesis with Machine Learning

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of $3.7\times$ in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process.Comment: 11 pages, 10 figure

Quantum Hardware Roofline: Evaluating the Impact of Gate Expressivity on Quantum Processor Design

The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". This paper explores hardware design trade-offs across NISQ systems to guide algorithm and hardware design choices. The evaluation is driven by algorithmic workloads and algorithm fidelity models which capture architectural features such as gate expressivity, fidelity, and crosstalk. We also argue that the criteria for gate design and selection should be extended from maximizing average fidelity to a more comprehensive approach that takes into account the gate expressivity with respect to algorithmic structures. We consider native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, $\sqrt{\text{iSWAP}}$), proposed gates (B Gate, $\sqrt[4]{\text{CNOT}}$, $\sqrt[8]{\text{CNOT}}$), as well as parameterized gates (FSim, XY). Our methodology is driven by a custom synthesis driven circuit compilation workflow, which is able to produce minimal circuit representations for a given system configuration. By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software co-design for quantum computing

Drug attitude as predictor for effectiveness in first-episode schizophrenia: Results of an open randomized trial (EUFEST)

Effectiveness has become more and more important as a comprehensive outcome measure for (long-term) treatment in schizophrenia. Early predictors to identify patients at a high risk for not succeeding the initiated treatment would be very useful. Discontinuation of the initiated treatment was used as criterion for effectiveness and patients' drug attitude was shown to be predictive for non-adherence or discontinuation of long-term treatment in schizophrenia. Accordingly, the predictive validity of the Drug Attitude Inventory (DAI) for effectiveness should be evaluated. Based on a sub-sample of patients from the EUFEST study for whom DAI assessments were available significant predictors for effectiveness as measured by discontinuation of initiated treatment were identified based on a logistic and a Cox-regression analysis. A Receiver-Operating Characteristic- (ROC-) analysis was conducted for the DAI, prognostic / diagnostic parameters (sensitivity, specificity) were calculated and a cut-off value suggested. In a sample of 228 first-episode patients, the DAI score was the most powerful predictor for effectiveness (p<0.001) besides two other significant predictors (PANSS-positive score and sexual side effects). The ROC-analysis revealed an area under the curve of 0.64 (p<0.001). The suggested cut-off point of about 20 yielded a sensitivity of 70-75% and a specificity of 40-45%. Study results indicate that the Drug Attitude Inventory, filled in by patients early in treatment seems to be a valid predictor for effectiveness as measured by discontinuation of the initiated treatment. DAI scores could also serve as an (differential) indicator for the need of enhanced treatment monitoring. These findings have to be validated in other (first-episode) samples