Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design

The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain's infrastructures and facilities based on customer demand. Key economic decisions are taken about the location, number, and size of manufacturing facilities and warehouses based on the optimal solution. Therefore, improvements in the methods to address this question, which is known to be in the NP-hard complexity class, would have relevant financial consequences. Here, we implement in the D-Wave quantum annealer a hybrid classical-quantum annealing algorithm. The cost function with constraints is translated to a spin Hamiltonian, whose ground state encodes the searched result. As a benchmark, we measure the accuracy of results for a set of paradigmatic problems against the optimal published solutions (the error is on average below $1\%$), and the performance is compared against the classical algorithm, showing a remarkable reduction in the number of iterations. This work shows that state-of-the-art quantum annealers may codify and solve relevant supply-chain problems even still far from useful quantum supremacy.Comment: 9 pages and 2 figure

Active Learning in Physics: From 101, to Progress, and Perspective

Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.Comment: 15 page

Towards Prediction of Financial Crashes with a D-Wave Quantum Computer

Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, i.e., a problem which cannot be solved efficiently with a classical computer. We experimentally explore a novel approach to this problem by using a D-Wave quantum computer to obtain financial equilibrium more efficiently. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed to a spin-$1/2$ Hamiltonian with at most two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. Our experiment paves the way to study quantitative macroeconomics, enlarging the number of problems that can be handled by current quantum computers

Time-Optimal Quantum Driving by Variational Circuit Learning

The simulation of quantum dynamics on a digital quantum computer with parameterized circuits has widespread applications in fundamental and applied physics and chemistry. In this context, using the hybrid quantum-classical algorithm, combining classical optimizers and quantum computers, is a competitive strategy for solving specific problems. We put forward its use for optimal quantum control. We simulate the wave-packet expansion of a trapped quantum particle on a quantum device with a finite number of qubits. We then use circuit learning based on gradient descent to work out the intrinsic connection between the control phase transition and the quantum speed limit imposed by unitary dynamics. We further discuss the robustness of our method against errors and demonstrate the absence of barren plateaus in the circuit. The combination of digital quantum simulation and hybrid circuit learning opens up new prospects for quantum optimal control.Comment: 10 pages, 8 figure

Transplantation of Pro-Oligodendroblasts, Preconditioned by LPS-Stimulated Microglia, Promotes Recovery After Acute Contusive Spinal Cord Injury

Spinal cord injury (SCI) is a significant clinical challenge, and to date no effective treatment is available. Oligodendrocyte progenitor cell (OPC) transplantation has been a promising strategy for SCI repair. However, the poor posttransplantation survival and deficiency in differentiation into myelinating oligodendrocytes (OLs) are two major challenges that limit the use of OPCs as donor cells. Here we report the generation of an OL lineage population [i.e., pro-oligodendroblasts (proOLs)] that is relatively more mature than OPCs for transplantation after SCI. We found that proOLs responded to lipopolysaccharide (LPS)-stimulated microglia conditioned medium (L+M) by preserving toll-like receptor 4 (TLR4) expression, improving cell viability, and enhancing the expression of a myelinating OL marker myelin basic protein (MBP), compared to other OL lineage cells exposed to either LPS-stimulated (L+M) or nonstimulated microglia conditioned medium (L−M). When L+M-stimulated proOLs were intrathecally delivered through a lumbar puncture after a T10 thoracic contusive SCI, they promoted behavioral recovery, as assessed by the Basso‐Beattie‐Bresnahan (BBB) locomotor rating scale, stride length, and slips on the grid tests. Histologically, transplantation of L+M proOLs caused a considerable increase in intralesional axon numbers and myelination, and less accumulation of invading macrophages when compared with the vehicle control or OPC transplantation. Thus, transplantation of proOLs, preconditioned by L+M, may offer a better therapeutic potential for SCI than OPCs since the former may have initiated the differentiation process toward OLs prior to transplantation