Quantum Simulation of SU(3) Lattice Yang-Mills Theory at Leading Order in Large-N_{c} Expansion.

Quantum simulations of the dynamics of QCD have been limited by the complexities of mapping the continuous gauge fields onto quantum computers. By parametrizing the gauge invariant Hilbert space in terms of plaquette degrees of freedom, we show how the Hilbert space and interactions can be expanded in inverse powers of N_{c}. At leading order in this expansion, the Hamiltonian simplifies dramatically, both in the required size of the Hilbert space as well as the type of interactions involved.

Quantum anomaly detection for collider physics.

We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets - a regime that has been suggested for a quantum advantage.

Mitigating Depolarizing Noise on Quantum Computers with Noise-Estimation Circuits.

A significant problem for current quantum computers is noise. While there are many distinct noise channels, the depolarizing noise model often appropriately describes average noise for large circuits involving many qubits and gates. We present a method to mitigate the depolarizing noise by first estimating its rate with a noise-estimation circuit and then correcting the output of the target circuit using the estimated rate.

Simulating Collider Physics on Quantum Computers Using Effective Field Theories.

Simulating the full dynamics of a quantum field theory over a wide range of energies requires exceptionally large quantum computing resources. Yet for many observables in particle physics, perturbative techniques are sufficient to accurately model all but a constrained range of energies within the validity of the theory. We demonstrate that effective field theories (EFTs) provide an efficient mechanism to separate the high energy dynamics that is easily calculated by traditional perturbation theory from the dynamics at low energy and show how quantum algorithms can be used to simulate the dynamics of the low energy EFT from first principles.

Quantum Algorithm for High Energy Physics Simulations.

Simulating quantum field theories is a flagship application of quantum computing. However, calculating experimentally relevant high energy scattering amplitudes entirely on a quantum computer is prohibitively difficult. It is well known that such high energy scattering processes can be factored into pieces that can be computed using well established perturbative techniques, and pieces which currently have to be simulated using classical Markov chain algorithms.

Improving jet distributions with effective field theory.

We obtain perturbative expressions for jet distributions using soft-collinear effective theory (SCET). By matching SCET onto QCD at high energy, tree level matrix elements and higher order virtual corrections can be reproduced in SCET. The resulting operators are then evolved to lower scales, with additional operators being populated by required threshold matchings in the effective theory.

Determining gamma from B --> pi(pi) decays without the CP-asymmetry Cpi(0)pi(0).

Factorization based on the soft-collinear effective theory (SCET) can be used to reduce the number of hadronic parameters in an isospin analysis of B --> pi(pi) decays by one. This gives a theoretically precise method for determining the CP violating phase gamma by fitting to the B --> pi(pi) data without Cpi(0)pi(0). SCET predicts that gamma lies close to the isospin bounds.

Enhanced nonperturbative effects in jet distributions.

We consider the triple differential distribution d Gamma/dE(J)dm(2)(J)d Omega(J) for two-jet events at center of mass energy M, smeared over the end-point region m(2)(J)< View Article and Find Full Text PDF