Postgraduate medical education quality metrics panels can be enhanced by including learner outcomes.

Postgraduate medical education (PME) quality assurance at Health Education England (HEE) currently relies upon survey data. As no one metric can reflect all aspects of training, and each has its limitations, additional metrics should be explored. At HEE (West Midlands), we explored the use of learner outcomes, speciality examination pass rates and Annual Review of Competence Progression (ARCP) outcomes, as quality metrics. Feedback received from our local Quality Forum of 40 senior educators frames the discussion through this paper. Overall, learner outcomes are useful quality metrics that add to survey data to provide a more comprehensive picture of PME quality. However, the utility of ARCP outcomes as quality metrics is currently limited by concerns regarding variations in ARCP practice between regions. To address these concerns, ARCPs need the same processes, rigour, scrutiny and investment as other high-stakes assessments. This will improve the reliability and validity of the ARCP as an assessment and improve the usefulness of ARCP outcomes as quality metrics. Research is required to determine the optimal combination of metrics to use in PME quality assurance and to appraise the validity and reliability of the ARCP as an assessment.

A Stabilizer Framework for the Contextual Subspace Variational Quantum Eigensolver and the Noncontextual Projection Ansatz.

Quantum chemistry is a promising application for noisy intermediate-scale quantum (NISQ) devices. However, quantum computers have thus far not succeeded in providing solutions to problems of real scientific significance, with algorithmic advances being necessary to fully utilize even the modest NISQ machines available today. We discuss a method of ground state energy estimation predicated on a partitioning of the molecular Hamiltonian into two parts: one that is noncontextual and can be solved classically, supplemented by a contextual component that yields quantum corrections obtained via a Variational Quantum Eigensolver (VQE) routine. This approach has been termed Contextual Subspace VQE (CS-VQE); however, there are obstacles to overcome before it can be deployed on NISQ devices. The problem we address here is that of the ansatz, a parametrized quantum state over which we optimize during VQE; it is not initially clear how a splitting of the Hamiltonian should be reflected in the CS-VQE ansätze. We propose a "noncontextual projection" approach that is illuminated by a reformulation of CS-VQE in the stabilizer formalism. This defines an ansatz restriction from the full electronic structure problem to the contextual subspace and facilitates an implementation of CS-VQE that may be deployed on NISQ devices. We validate the noncontextual projection ansatz using a quantum simulator and demonstrate chemically precise ground state energy calculations for a suite of small molecules at a significant reduction in the required qubit count and circuit depth.

A Comparison of the Bravyi-Kitaev and Jordan-Wigner Transformations for the Quantum Simulation of Quantum Chemistry.

The ability to perform classically intractable electronic structure calculations is often cited as one of the principal applications of quantum computing. A great deal of theoretical algorithmic development has been performed in support of this goal. Most techniques require a scheme for mapping electronic states and operations to states of and operations upon qubits. The two most commonly used techniques for this are the Jordan-Wigner transformation and the Bravyi-Kitaev transformation. However, comparisons of these schemes have previously been limited to individual small molecules. In this paper, we discuss resource implications for the use of the Bravyi-Kitaev mapping scheme, specifically with regard to the number of quantum gates required for simulation. We consider both small systems, which may be simulatable on near-future quantum devices, and systems sufficiently large for classical simulation to be intractable. We use 86 molecular systems to demonstrate that the use of the Bravyi-Kitaev transformation is typically at least approximately as efficient as the canonical Jordan-Wigner transformation and results in substantially reduced gate count estimates when performing limited circuit optimizations.