Quantum Science

Introduction: Redefining Computing: Quantum Mechanics Meets Technology; Part 1: The Quantum Leap: From Silicon to Qubits; Part 2: Quantum Computing ...... Read more

Quantum technologies are currently maturing at a breath-taking pace. These technologies exploit principles of quantum mechanics in suitably ...... Read more

Unlocking the Quantum Realm: A New Tool for Uncharted Phenomena. TOPICS:Quantum Information ScienceQuantum MechanicsUniversity of Innsbruck. By ...... Read more

Quantum technology has the potential to revolutionize a variety of fields, and now a team at the University of Sydney's Nano Institute is working ...... Read more

Satyendra Nath Bose, an Indian physicist, made a groundbreaking contribution to the field of quantum mechanics with his work on Bose-Einstein ...... Read more

... quantum physics helps explain new phenomena in biology and chemistry. ... Quantum mechanics, especially, aids this discussion. It's essentially the ...... Read more

... quantum mechanics and describes electronic properties in solid-state physics, quantum chemistry, materials science, biochemistry, biology ...... Read more

We design algorithms for computing expectation values of observables in the equilibrium states of local quantum Hamiltonians, both at zero and positive temperature. The algorithms are based on hierarchies of convex relaxations over the positive semidefinite cone and the matrix relative entropy cone, and give certified and converging upper and... Read more

We consider the problem of optimizing the state average of a polynomial of non-commuting variables, over all states and operators satisfying a number of polynomial constraints, and over all Hilbert spaces where such states and operators are defined. Such non-commutative polynomial optimization (NPO) problems are routinely solved through hierarchies of... Read more

Quantum kernel methods leverage a kernel function computed by embedding input information into the Hilbert space of a quantum system. However, large Hilbert spaces can hinder generalization capability, and the scalability of quantum kernels becomes an issue. To overcome these challenges, various strategies under the concept of inductive bias have... Read more

This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification... Read more

The perturbative consistency of coherent states within interacting quantum field theory requires them to be altered beyond the simple non-squeezed form. Building on this point, we perform explicit construction of consistent squeezed coherent states, required by the finiteness of physical quantities at the one-loop order. Extending this analysis to two-loops,... Read more

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been... Read more

By connecting multiple quantum computers (QCs) through classical and quantum channels, a quantum communication network can be formed. This gives rise to new applications such as blind quantum computing, distributed quantum computing and quantum key distribution. In distributed quantum computing, QCs collectively perform a quantum computation. As each device only... Read more

We show the following unconditional results on quantum commitments in two related yet different models: 1. We revisit the notion of quantum auxiliary-input commitments introduced by Chailloux, Kerenidis, and Rosgen (Comput. Complex. 2016) where both the committer and receiver take the same quantum state, which is determined by the security... Read more

We demonstrate how to build computationally secure commitment schemes with the aid of quantum auxiliary inputs without unproven complexity assumptions. Furthermore, the quantum auxiliary input can be prepared either (1) efficiently through a trusted setup similar to the classical common random string model, or (2) strictly between the two involved... Read more

ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum... Read more

In recent years, tensor network renormalization (TNR) has emerged as an efficient and accurate method for studying (1+1)D quantum systems or 2D classical systems using real-space renormalization group (RG) techniques. One notable application of TNR is its ability to extract central charge and conformal scaling dimensions for critical systems. In... Read more

The nonlinear Schr"odinger-Newton equation, a prospective semiclassical alternative to a quantized theory of gravity, predicts a gravitational self-force between the two trajectories corresponding to the two z-spin eigenvalues for a particle in a Stern-Gerlach interferometer. To leading order, this force results in a relative phase between the trajectories. For the... Read more

Bell scenarios involve space-like separated measurements made by multiple parties. The standard no-signalling constraints ensure that such parties cannot signal superluminally by choosing their measurement settings. In tripartite Bell scenarios, relaxed non-signalling constraints have been proposed, which permit a class of post-quantum theories known as jamming non-local theories. To analyse... Read more