Seminar

Finding the ground energy of a quantum system is a fundamental problem in condensed matter physics and quantum chemistry. Existing classical algorithms for tackling this problem often assume that the ground state has a succinct classical description, i.e. a poly-size classical circuit for computing the amplitude. Notable examples of succinct states encompass matrix product states, contractible projected entangled pair states, and states that can be represented by classical neural networks.

We analyze a model of qubits which we argue has an emergent quantum gravitational description similar to the fermionic Sachdev-Ye-Kitaev (SYK) model. The model we consider is known as the quantum q-spin model because it features q-local interactions between qubits. It was previously studied as a model of a quantum spin glass, and while we find that the model is glassy for q=2, q=3, and likely q=4, we also find evidence for previously unexpected SYK-like behavior for the quenched free energy down to the lowest temperatures for q >= 5.

Optical spectroscopy is used to study a material by measuring the intensity of light modes that scatter off it. In this work, we develop a theory for G2 spectroscopy of correlated materials, where instead of measuring the intensity of scattered photons, one measures the second order coherence between pairs of photons scattered off a material. We map this correlation function of the photons to the correlation functions of the material being probed.

Randomized benchmarking (RB) is a widely used strategy to assess the quality of available quantum gates in a computational context. The quality is usually expressed as an effective depolarizing error per step. RB involves applying random sequences of gates to an initial state and making a final measurement to determine the probability of an error. Current implementations of RB estimate this probability by repeating each randomly chosen sequence many times.

Simulating Fermionic Hamiltonians requires a mapping from fermionic to qubit operators. This mapping must obey the underlying algebra of fermionic operators; in particular, their specific anticommutation relations. The traditional mapping is the Jordan-Wigner encoding, which is simple and qubit minimal, but can incur significant overheads during simulation. This is because the qubit weight of fermionic operators is high, i.e. operators typically must involve many qubits. New mappings address this trade-off and hold other intriguing features.

Decoding algorithms based on approximate tensor network contraction have proven tremendously successful in decoding 2D local quantum codes such as surface/toric codes and color codes, effectively achieving optimal decoding accuracy. We introduce several techniques to generalize tensor network decoding to higher dimensions so that it can be applied to 3D codes as well as 2D codes with noisy syndrome measurements (phenomenological noise or circuit-level noise).

We use time-dependent density functional theory to investigate the possibility of hosting organic color centers in (6, 6) armchair single-walled carbon nanotubes, which are known to be metallic. Our calculations show that in short segments of (6, 6) nanotubes ∼5 nm in length there is a dipole-allowed singlet transition related to the quantum confinement of charge carriers in the smaller segments. The introduction of sp3 defects to the surface of (6, 6) nanotubes results in new dipole-allowed excited states.

Calibrating and re-calibrating quantum processors to achieve and maintain error rates below fault tolerance thresholds in the presence of drift will be a key engineering challenge in emerging large scale quantum information processing systems. To address this problem, we are adapting a canonical technique in classical electrical and control engineering: combining Kalman filters with linear-quadratic regulators for streaming adaptive control. We start by identifying a gate set error mode for a device.

Learning properties of quantum processes is a fundamental task in physics. It is well known that full process tomography scales exponentially in the number of qubits. In this work, we consider online learning quantum processes in a mistake bounded model and prove exponentially improved bounds compared to the stronger notion of diamond norm learning. The problem can be modelled as an interactive game over any given number of rounds, T, between a learner and an adversary.