Seminar

Recent progress on noisy, intermediate scale quantum (NISQ) devices opens exciting opportunities for many-body physics. NISQ platforms are indeed not just computers, but also interesting laboratory systems in their own right, offering access to large Hilbert spaces with exceptional capabilities for control and measurement. I will argue that nonequilibrium phases in periodically-driven (Floquet) systems are a particularly good fit for such capabilities in the near term.

Trapdoor claw-free functions (TCFs) are central to a recent wave of groundbreaking work in quantum cryptography that was originated by U. Mahadev and other authors.  TCFs enable protocols for cryptography that involve quantum computers and classical communication.  In this expository talk I will present the definition of a TCF and its variants, and I will discuss quantum applications, including the recent paper "Quantum Encryption with Certified Deletion, Revisited: Public Key, Attribute-Based, and Classical Communication" by T. Hiroka et al. (arXiv:2105.05393).

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.

We show that every quantum computation can be described by a probabilistic update of a probability distribution on a finite phase space. Negativity in a quasiprobability function is not required in states or operations. Our result is consistent with Gleason’s theorem and the Pusey-Barrett-Rudolph theorem.

Joint work with: Michael Zurel and Cihan Okay
J-Ref: Phys. Rev. Lett. 125, 260404 (2020)

(Please note the earlier start time of 10:30 a.m. for this seminar.)

In quantum optics, the Hilbert space of a mode of light corresponds to functions on a plane called the phase space (so called because it reminded Boltzmann of oscillators in 2-d real space.)  This correspondence offers three important features:  it can autonomously handle quantum theoretical calculations, it allows for the infinite-dimensional Hilbert space to be easily visualized, and it is intimately related to a basic experimental measurement (the so-called heterodyne detection).  Continuous phase space correspondences exist naturally for many types of Hilbert space

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.

We will consider the problem of learning the Hamiltonian of a quantum many-body system given samples from its Gibbs (thermal) state. The classical analog of this problem, known as learning graphical models or Boltzmann machines, is a well-studied question in machine learning and statistics. This talk will describe a sample-efficient algorithm for the quantum Hamiltonian learning problem at all constant temperatures.

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.