Controllability of quantum dot arrays via maximum entropy
Quantum dots are a promising platform to realize practical quantum computing. However, before they can be used as qubits, quantum dots must be carefully tuned to the correct regime in the voltage space to trap individual electrons. Moreover, realizable quantum computing requires tuning of large arrays, which translates to a significant increase in the number of parameters that need to be controlled and calibrated. This necessitates the development of robust and automated methods to bring the device into an operational state.
On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks
Classical algorithms are often not effective for solving nonconvex optimization problems where local minima are separated by high barriers. In this paper, we explore possible quantum speedups for nonconvex optimization by leveraging the global effect of quantum tunneling. Specifically, we introduce a quantum algorithm termed the quantum tunneling walk (QTW) and apply it to nonconvex problems where local minima are approximately global minima.
Multidimensional Quantum Walks
While quantum walk frameworks make it easy to design quantum algorithms, as evidenced by their wide application across domains, the major drawback is that they can achieve at most a quadratic speedup over the best classical algorithm. In this work, we generalise the electric network framework – the most general of quantum walk frameworks, into a new framework that we call the multidimensional quantum walk framework, which no longer suffers from the aforementioned drawback, while still m
Learning properties of interacting fermionic systems with limited hardware
Interacting fermionic systems can model real world physical phenomenon directly. Many people are working on finding efficient and practical ways to determine properties of these quantum simulators. Specifically, estimating correlation functions, that reveal important properties such as coulomb-coulomb interaction strength and entanglement spreading, is a crucial goal. The well know classical shadows formalism allows one to find linear properties of a quantum state by reusing outcomes from simple basis measurements.
Stabilizer codes: the continuous, the infinite, and the exotic
Traditional stabilizer codes operate over prime power local-dimensions. For instance, the 5-qubit code and 9-qubit code operate over local-dimension 2. In this presentation we discuss extending the stabilizer formalism using the local-dimension-invariant framework to import stabilizer codes from these standard local-dimensions to other cases.
Extrinsic Geometry of Quantum States
A quantum state that depends on a parameter is a commonly studied structure in quantum physics. Examples include the ground state of a Hamiltonian with a parameter or Bloch states as functions of the quasimomentum. The change in the state as the parameter varies can be characterized by such geometric objects as the Berry phase or the quantum distance which has led to many insights in the understanding of quantum systems.
Quantum recurrent neural networks
I will talk about quantum recurrent neural networks based on quantum dissipative neural networks (DQNNs), which use quantum neural networks to describe general causal quantum automata. I will first introduce feed-forward DQNNs and then go to the recurrent case. After discussing the architecture and universality, I will discuss training algorithms and numerical results showing good generalisation behaviour.
Quantum gravity experiments on a quantum computer
With the long-term goal of studying quantum gravity on a quantum computer, I will discuss several results that make promising progress in this direction. Firstly, I will discuss a proposal for a holographic teleportation protocol that can be readily executed in table-top experiments. This protocol exhibits similar behavior to that seen in recent traversable wormhole constructions.
Dualities in one-dimensional quantum lattice models: symmetric Hamiltonians and matrix product operator intertwiners
Recently, we presented a systematic recipe for generating duality transformations in one dimensional lattice models. Our construction is based on a detailed understanding of the most general kind of symmetry a one-dimensional lattice model can exhibit: categorical symmetries. These symmetries are conveniently described in the language of tensor networks, where they are represented as matrix product operators.
Sample-optimal classical shadows for pure states
Huang, Kueng, Preskill introduced the learning task now known as “classical shadows”: given few copies of an unknown state ρ, construct a classical description of the state from independent measurements that can be used to predict certain properties of the state. Specifically, they show Θ(B/epsilon^2) samples of ρ suffice to approximate the expectation value Tr(Oρ) of any Hermitian observable O to within additive error epsilon provided Tr(O^2) ≤ B and the eigenvalues of O are contained in [-1,1].