Fall 2021

The development of spin qubits with long coherence times for quantum information processing requires sources of spin noise to be identified and minimized. Although microwave-based spin control is typically used to extract the noise spectrum, this becomes infeasible when high frequency noise components are stronger than the available microwave power. Here, we introduce an all-optical approach for noise spectroscopy of spin qubits based on Raman spin rotation using Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences.

Near-term applications of early quantum devices, such as quantum simulations, rely on accurate estimates of expectation values to become relevant. Decoherence and gate errors lead to wrong estimates. This problem was, at least in theory, remedied with the advent of quantum error correction.

The typical model for measurement noise in quantum error correction is to randomly flip the binary measurement outcome. In experiments, measurements yield much richer information - e.g., continuous current values, discrete photon counts - which is then mapped into binary outcomes by discarding some of this information. In this work, we consider methods to incorporate all of this richer information, typically called soft information, into the decoding of the surface code.

Modern quantum algorithms originate historically from three disparate origins: simulation, search, and factoring.  Today, we can now understand and appreciate all of these as being instances of a single framework, and remarkably, the essence is how the rotations of a single quantum bit can be transformed non-linearly by a simple sequence of operations.  On the face of it, this is physically non-intuitive, because quantum mechanics is linear.  The key is to think not about eigenvalues and closed systems, but instead, about singular values and subsystem dynamics.

This short talk provides a snapshot of opportunities in quantum science, technology, engineering, and mathematics (qSTEM) at the University of Maryland College Park (UMD). The UMD quantum ecosystem consists of seven quantum institutes, five quantum-adjacent institutes, and approximately 100 faculty, split 55/45 between theory and experiment. I organize the ecosystem into subfields: each subfield is described, and its corresponding faculty is listed. 

Pizza and drinks served after the talk.  This talk will start at 12:10 p.m.

The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any level of abstraction away from the underlying hardware incurs overhead. In the era of near-term, noisy, intermediate-scale quantum (NISQ) hardware with severely restricted resources, this overhead may be unjustifiable. We develop quantum algorithms for Hamiltonian simulation "one level below" the circuit model, exploiting the underlying control over qubit interactions available in most quantum hardware implementations.

Quantifying quantum states’ complexity is a key problem in various subfields of science, from quantum computing to black-hole physics. We prove a prominent conjecture by Brown and Susskind about how random quantum circuits’ complexity increases. Consider constructing a unitary from Haar-random two-qubit quantum gates. Implementing the unitary exactly requires a circuit of some minimal number of gates - the unitary’s exact circuit complexity.

The quantum beats are a well-understood phenomenon that has long been used as a spectroscopic technique in various systems. Here we demonstrate two new aspects in understanding and using quantum beats - (i) coupling to the electromagnetic vacuum allows for beating without an initial superposition between the excited levels, and (ii) by detecting the transmission in the forward direction in a superradiant burst, quantum beats can be collectively enhanced, increasing the signal strength useful in systems with low signal-to-noise.