Quantum & Particles and Fields Parallel Talks

Quantum simulation of fermionic systems is a leading application of quantum computers. One promising approach is to represent fermions with qubits via fermion-to-qubit mappings. In this work, we present high-distance fermion-to-qubit stabilizer codes for simulating 2D and 3D fermionic systems. These codes achieve arbitrarily large code distances while keeping stabilizer weights constant. They also preserve locality by mapping local fermionic operators to local qubit operators at any fixed distance. Notably, our 3D construction is the first to simultaneously achieve high distance, constant stabilizer weights, and locality preservation. Our construction is based on concatenating a small-distance 2D or 3D fermion-to-qubit code with a high-distance fermionic color code. Together, these features provide a robust and scalable pathway to quantum simulation of fermionic systems.

Quantum computing algorithms have shown promise for performing physics simulations faster than classical computers can. We develop a quantum algorithm for solving the 1-dimensional advection equation with periodic boundary condition using a first-order upwind scheme, which can capture discontinuities in the wave envelope. We embed the non-unitary upwind scheme  using Linear Combinations of Unitaries (LCUs), introducing a small, bounded probability of failure.  We also develop an efficient quantum gate decomposition of the upwind unitary that achieves exponential speed up. That is, at each time step, our algorithm performs the advection using log(N) qubits and gates, where N is the number of data points, as opposed to a classical computer which costs O(N). For well-resolved envelopes, we also develop a method to recover the unknown quantum state when LCUs fail. Our algorithm can be used as a module for many physics simulations that involve advection.

Many problems in machine learning and physics can be expressed as finding the “best” configuration of a system out of many possibilities. One important example is Markov Random Field (MRF) Maximum A-Posteriori (MAP) estimation, which is widely used in computer vision tasks such as denoising or restoring corrupted images. The challenge is that, in its most general form, this problem is NP-hard, meaning it is extremely costly for classical computers to solve at large scales.

In this work, we explore how a Bose–Hubbard Model (BHM) machine, a quantum system realized in cold atom experiments, can be adapted to perform MRF MAP estimation more efficiently. By mapping the ingredients of the MRF energy function onto terms in the BHM Hamiltonian, such as the chemical potential (unary potential) and Rydberg-dressed interactions (pairwise potential), the natural dynamics of the system approximate the minimization needed to solve the problem. This effectively allows the quantum system itself to act as the optimizer.

Our approach suggests that BHM machines could serve as a new kind of quantum optimizer, alleviating classical computational bottlenecks. This not only opens a pathway for improved visual reasoning in AI models, but also demonstrates the broader potential of quantum simulators for tackling hard optimization problems.

The Colorado Underground Research Institute (CURIE) is a new shallow-underground laboratory that will host a low-background quantum facility housed inside the Edgar Experimental Mine (EEM). The facility is located under ~200m of rock overburden (~415 m.w.e.), providing a ~700-fold reduction in cosmogenic muon flux and attenuation of the primary cosmogenic hadronic and electromagnetic components. To characterize the cosmogenic muon flux at this site, we deploy a remotely controlled muon telescope capable of measuring the intensities of muons in a given direction. Profiling the angular distribution of this flux will provide critical information on the cosmogenic backgrounds, a crucial step in commissioning the CURIE facility. We demonstrate the calibration and initial measurements of the telescope and outline the goals for its deployment.

We express relativistic quantum mechanics (the Dirac and Weyl Lagrangians) entirely in terms of geometrical quantities—specifically real numbers and multivectors in 3+1 spacetime dimensions, also known as the spacetime algebra.  We show how spin, helicity, and chirality are expressed in this formalism and compare this to standard matrix Dirac and Weyl spinor theory.  We also compare our formalism to that of Hestenes typically used in geometric algebra.

In this paper, I will conduct a thorough review of the journey for detecting neutrinos as Majorana particles and the implications that would have on the Standard Model physicists follow today. I will begin with an introduction on the importance of the Standard Model and the neutrino's role within it. I will then provide a background on the theories that opened up the discussion of Majorana neutrinos, as well as the quantum interpretation of Neutrinoless Double Beta Decay, which is the leading methodology for detecting them. Next, I will display and explain the mechanisms behind the detector at the forefront of the search, the KamLAND-Zen detector. Then I will transition into a critique of the limitations holding modern experimentation back, and provide details on the proposed solutions scientists look to employ in future testing. Finally, I will review both the broad and specific implications the discovery of Majorana neutrinos would have on particle physics and science as a whole.