Seminar

Building large-scale modular quantum computers and quantum networks require high fidelity, high efficiency, and long lifetime quantum memories [1]. Quantum memories are proposed to increase photon-mediatated matter-qubit entanglment rates by synchronizing photon interference between network nodes [2]. Hybrid quantum networking leverages trapped ions’ high fidelity operations and neutral-atoms’ single photon manipulation for increased entanglement rates over single-species quantum networks [3-8].

In this talk, we will mainly focus on noisy quantum trees: at each node of a tree, a received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by delocalization of information.

Many-body fermionic systems can be simulated in a hardware-efficient manner using a fermionic quantum processor. Neutral atoms trapped in optical potentials can realize such processors, where non-local fermionic statistics are guaranteed at the hardware level. Implementing quantum error correction in this setup is however challenging, due to the atom-number superselection present in atomic systems, that is, the impossibility of creating coherent superpositions of different particle numbers.

Fundamental forces of nature are described by gauge theories, and the interactions of matter with gauge fields lead to intriguing phenomena like the confinement of quarks in quantum chromodynamics. Separating a confined quark-anti-quark pair incurs an energy cost that grows linearly with their separation, eventually leading to the production of additional particles by an effect that is called string-breaking. In this talk, I will discuss how similar phenomenology can be probed using Rydberg atom arrays.

Irreversible quantum computation requires thermodynamic work. In principle, one can often evade work costs by implementing reversible transformations. In practice, complexity---the difficulty of realizing a quantum process---poses an obstacle: a realistic agent can perform only a limited number of gates and so not every reversible transformation. Hence an agent, if unable to complete a task unitarily, may expend work on an irreversible process, such as erasure, to finish the job.

Superconducting circuits are at the forefront of quantum computing and quantum sensing technologies, where accurate modeling and simulation are crucial for understanding and optimizing their performance. In this dissertation, we study modeling techniques and novel device designs to advance these technologies, focusing on efficient simulations, direct velocity measurement, and nonreciprocal devices for quantum information processing.

In this session, we will break out into subgroups to work through the mathematics in the paper "Hidden-State Proofs of Quantumness" (https://arxiv.org/abs/2410.06368).

Each group will have at least one person with familiarity in cryptography familiarity to guide the process.

Participants should read the paper before the session, but are not expected to have grasped all of its concepts.

Experimental control over the strength and angular dependence of interactions between atoms is a key capability for advancing quantum technologies. Here, we use microwave dressing to manipulate and enhance Rydberg-Rydberg interactions in an atomic ensemble. By resonantly coupling opposite parity Rydberg states, we create eigenstates with first-order dipole-dipole interactions. We study the modification of the interactions by measuring the statistics of the light retrieved from the ensemble.

Quantum computing leverages the quantum properties of subatomic matter to enable algorithms to run faster than those possible on a regular computer. Quantum computers have become increasingly practical in recent years, with some small-scale machines available for public use. Quantum computing applications are largely dependent on the software that manipulates computations on the hardware. These applications rely on a variety of symbolic representations including quantum programs to describe and manipulate quantum information effectively.

Quantum mechanics is one of our most profound and successful theoretical frameworks for understanding the physical world. It continues to drive remarkable technological and theoretical breakthroughs, spanning computing, coding theory, cryptography, material science, and chemistry. In this talk, I will describe how the algorithmic lens has been pivotal in rigorously analyzing such quantum systems and revealed deeper structural properties that were previously inaccessible through traditional approaches.