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Sean Weinberg

Publications and source records attributed to Sean Weinberg.

4 recordsLinked to original sources

Bias-preserving and error-detectable entangling operations in a superconducting dual-rail system

For useful quantum computation, error-corrected machines are required that can dramatically reduce the inevitable errors experienced by physical qubits. While significant progress has been made in approaching and exceeding the surface-code threshold in superconducting platforms, large gains in the logical error rate with increasing system size remain out of reach. This is due both to the large number of required physical qubits and the need to operate far below threshold. Importantly, by exploiting the biases and structure of the physical errors, this threshold can be raised. Erasure qubits achieve this by detecting certain errors at the hardware level. Dual-rail qubits encoded in superconducting cavities are a promising erasure qubit wherein the dominant error, photon loss, can be detected and converted to an erasure. In these approaches, the complete set of operations, including two qubit gates, must be high performance and preserve as much of the desirable hierarchy or bias in the errors as possible. Here, we design and realize a novel two-qubit gate for dual-rail erasure qubits based on superconducting microwave cavities. The gate is high-speed ($\sim$500 ns duration), and yields a residual gate infidelity after error detection below 0.1%. Moreover, we experimentally demonstrate that this gate largely preserves the favorable error structure of idling dual-rail qubits, making it ideal for error correction. We measure low erasure rates of $\sim$0.5% per gate, as well as low and asymmetric dephasing errors that occur at least three times more frequently on control qubits compared to target qubits. Bit-flip errors are practically nonexistent, bounded at the few parts per million level. This error asymmetry has not been well explored but is extremely useful in quantum error correction and flag-qubit contexts, where it can create a faster path to effective error-corrected systems.

quant-ph

Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem

Quantum computing and machine learning have potential for symbiosis. However, in addition to the hardware limitations from current devices, there are still basic issues that must be addressed before quantum circuits can usefully incorporate with current machine learning tasks. We report a new strategy for such an integration in the context of attention models used for reinforcement learning. Agents that implement attention mechanisms have successfully been applied to certain cases of combinatorial routing problems by first encoding nodes on a graph and then sequentially decoding nodes until a route is selected. We demonstrate that simple quantum circuits can used in place of classical attention head layers while maintaining performance. Our method modifies the networks used in [1] by replacing key and query vectors for every node with quantum states that are entangled before being measured. The resulting hybrid classical-quantum agent is tested in the context of vehicle routing problems where its performance is competitive with the original classical approach. We regard our model as a prototype that can be scaled up and as an avenue for further study on the role of quantum computing in reinforcement learning.

quant-ph

DAYENU: A Simple Filter of Smooth Foregrounds for Intensity Mapping Power Spectra

We introduce DAYENU, a linear, spectral filter for HI intensity mapping that achieves the desirable foreground mitigation and error minimization properties of inverse co-variance weighting with minimal modeling of the underlying data. Beyond 21 cm power-spectrum estimation, our filter is suitable for any analysis where high dynamic-range removal of spectrally smooth foregrounds in irregularly (or regularly) sampled data is required, something required by many other intensity mapping techniques. Our filtering matrix is diagonalized by Discrete Prolate Spheroidal Sequences which are an optimal basis to model band-limited foregrounds in 21 cm intensity mapping experiments in the sense that they maximally concentrate power within a finite region of Fourier space. We show that DAYENU enables the access of large-scale line-of-sight modes that are inaccessible to tapered DFT estimators. Since these modes have the largest SNRs, DAYENU significantly increases the sensitivity of 21 cm analyses over tapered Fourier transforms. Slight modifications allow us to use DAYENU as a linear replacement for iterative delay CLEANing (DAYENUREST). We refer readers to the Code section at the end of this paper for links to examples and code.

astro-ph.CO

Gauge-invariant observables in gravity and electromagnetism: black hole backgrounds and null dressings

We address questions regarding construction and implications of gauge-invariant "dressed" observables in nontrivial background geometries such as that of a black hole. Formally, such observables can be constructed, e.g. by locating points with geodesics launched from infinity. However, practical complications arise in non-trivial geometries, and in particular for observables behind black hole horizons. Greater simplicity can be achieved by considering null constructions where the dressing lies along a null geodesic, or null surface such as a cone. We first investigate basic properties of these null dressings in the simpler context of electromagnetism. Since null constructions provide simple dressings for gauge-invariant observables inside black holes, they also allow us to investigate the question of compatibility of observables inside and outside black holes, and in particular the idea of black hole complementarity. While such observables in general have non-vanishing state-dependent commutators, the failure to commute does not appear particularly enhanced by the presence of the horizon.

hep-th