Christophe Pere, PhD’s Post

> - sharing resource - not my work - < Interesting paper last week about the internal training in organizations for #quantumcomputing. It's always fascinating to see the intersection between the #workforce in #quantum and the goal of an organization. Title: Why Teach Quantum In Your Own Time: The Values of Grassroots Organizations Involved in Quantum Technologies Education and Outreach by Ulrike Genenz, Neelanjana Anne, Zeynep Kılıç, Daniel Matthews, Oya Ok, Adrian Schmidt and Zeki Can Seskir Abstract: This paper examines the intersection of goals and values within grassroots organizations operating in the realm of quantum technologies (QT) education. It delineates a fundamental distinction between the objective to provide education and the drive to democratize learning through principles of inclusivity, accessibility, and diversity. The analysis reveals how these organizations navigate their nascent stages, grappling with the dual challenge of adhering to their foundational values while aspiring for sustainable growth and development in the highly specialized field of QT. The study uncovers the strategic approaches adopted by these entities, including efforts to create educational ecosystems and foster community engagement. The research underscores the potential vulnerabilities of these grassroots organizations, particularly in relation to the longevity and evolution of their initiatives as members transition into professional roles within the quantum sector. Through this investigation, the paper contributes to a nuanced understanding of how emerging educational organizations in the QT field balance their ideological commitments with practical growth considerations, highlighting the critical factors that influence their trajectory and impact. Link: https://lnkd.in/e85juJrk #quantumcomputing #teaching #trainingworkforce

>- Not my work - sharing resources -< Great paper today: "Single-shot quantum machine learning" by Erik Recio Armengol, Jens Eisert, and Johannes Jakob Meyer Abstract: Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because the outputs of quantum learning models are inherently random. This creates an often considerable overhead, as many executions of a quantum learning model have to be aggregated to obtain an actual prediction. In this work, we analyze when quantum learning models can evade this issue and produce predictions in a near-deterministic way – paving the way to single-shot quantum machine learning. We give a rigorous definition of single-shotness in quantum classifiers and show that the degree to which a quantum learning model is near-deterministic is constrained by the distinguishability of the embedded quantum states used in the model. Opening the black box of the embedding, we show that if the embedding is realized by quantum circuits, a certain depth is necessary for single-shotness to be even possible. We conclude by showing that quantum learning models cannot be single-shot in a generic way and trainable at the same time. Link: https://lnkd.in/eyKE439p #quantummachinelearning #quantumcomputing #research #researchpaper

Interesting approach this morning for random forest on a quantum computer: "QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest" by Romina Yalovetzky, Niraj Kumar, Changhao Li, and Marco Pistoia Abstract: Random Forest (RF) is a popular tree-ensemble method for supervised learning, prized for its ease of use and flexibility. Online RF models require to account for new training data to maintain model accuracy. This is particularly important in applications were data is periodically and sequentially generated over time in data streams, such as auto-driving systems, and credit card payments. In this setting, performing periodic model retraining with the old and new data accumulated is beneficial as it fully captures possible drifts in the data distribution over time. However, this is unpractical with state-of-the-art classical algorithms for RF as they scale linearly with the accumulated number of samples. We propose QC-Forest, a classical-quantum algorithm designed to time-efficiently retrain RF models in the streaming setting for multi-class classification and regression, achieving a runtime poly-logarithmic in the total number of accumulated samples. QC-Forest leverages Des-q, a quantum algorithm for single tree construction and retraining proposed by Kumar et al. by expanding to multi-class classification, as the original proposal was limited to binary classes, and introducing an exact classical method to replace an underlying quantum subroutine incurring a finite error, while maintaining the same poly-logarithmic dependence. Finally, we showcase that QC-Forest achieves competitive accuracy in comparison to state-of-the-art RF methods on widely used benchmark datasets with up to 80,000 samples, while significantly speeding up the model retrain. Link: https://lnkd.in/eVfMAUFg #quantummachinelearning #quantumcomputing #randomforest #research #researchpaper

Interesting paper this morning: "Predicting quantum learnability from landscape fluctuation" by Hao-Kai Zhang, Chenghong Zhu, and Xin Wang Abstract: The tradeoff between trainability and expressibility is a central challenge faced by today’s variational quantum computing. Recent studies indicate that resolving this dilemma necessitates designing specific parametrized quantum circuits (PQC) tailored for specific problems, which urgently needs a general and efficient method to assess the learnability of PQCs regarding a given target. In this Letter, we demonstrate a simple and efficient metric for learnability by comparing the fluctuations of the given training landscape with standard learnable landscapes. This metric shows surprising effectiveness in predicting learnability as it unifies the effects of insufficient expressibility, barren plateaus, bad local minima, and overparametrization. Importantly, it does not require actual training and can be estimated efficiently on classical computers via Clifford sampling. We conduct extensive numerical experiments to validate its effectiveness regarding both physical and random Hamiltonians. We also prove a compact lower bound for the metric in locally scrambled circuits as analytical guidance. Our findings enable efficient predictions of learnability, allowing fast selection of suitable PQCs for a given problem without training, which can improve the efficiency of variational quantum computing especially when access to quantum devices is limited. Link: https://lnkd.in/eXD4hjFu #quantummachinelearning #quantumcomputing #research #researchpaper

I Like this one on Hamiltonian: "Variational quantum Hamiltonian engineering" by Benchi Zhao, and Keisuke Fujii Abstract: The Hamiltonian of a quantum system is represented in terms of operators corresponding to the kinetic and potential energies of the system. The expectation value of a Hamiltonian and Hamiltonian simulation are two of the most fundamental tasks in quantum computation. The overheads for realizing the two tasks are determined by the Pauli norm of Hamiltonian, which sums over all the absolute values of Pauli coefficients. In this work, we propose a variational quantum algorithm (VQA) called variational quantum Hamiltonian engineering (VQHE) to minimize the Pauli norm of Hamiltonian, such that the overhead for executing expectation value estimation and Hamiltonian simulation can be reduced. First, we develop a theory to encode the Pauli norm optimization problem into the vector l1-norm minimization problem. Then we devise an appropriate cost function and utilize the parameterized quantum circuits (PQC) to minimize the cost function. We also conduct numerical experiments to reduce the Pauli norm of the Ising Hamiltonian and molecules’ Hamiltonian to show the efficiency of the proposed VQHE. Link: https://lnkd.in/eaMqMVxd #quantummachinelearning #quantumcomputing #hamiltonian #researchpaper

New paper from Pasqal team this morning: "Quantum Positional Encodings for Graph Neural Networks" by Slimane Thabet, Djellabi Mehdi, Igor Sokolov, Sachin Kasture, Louis-Paul Henry and Loïc Henriet Abstract: In this work, we propose novel families of positional encodings tailored to graph neural networks obtained with quantum computers. These encodings leverage the long-range correlations inherent in quantum systems that arise from mapping the topology of a graph onto interactions between qubits in a quantum computer. Our inspiration stems from the recent advancements in quantum processing units, which offer computational capabilities beyond the reach of classical hardware. We prove that some of these quantum features are theoretically more expressive for certain graphs than the commonly used relative random walk probabilities. Empirically, we show that the performance of state-of-the-art models can be improved on standard benchmarks and large-scale datasets by computing tractable versions of quantum features. Our findings highlight the potential of leveraging quantum computing capabilities to enhance the performance of transformers in handling graph data. Link: https://lnkd.in/eEyfczhA #quantummachinelearning #graph #graphneuralnetworks #quantumcomputer #neutralatoms

Very nice paper this morning on: "Geometric Quantum Machine Learning with Horizontal Quantum Gates" by Roeland Wiersema, Alexander F. Kemper, Bojko N. Bakalov, and Nathan Killoran Abstract: In the current framework of Geometric Quantum Machine Learning, the canonical method for constructing a variational ansatz that respects the symmetry of some group action is by forcing the circuit to be equivariant, i.e., to commute with the action of the group. This can, however, be an overzealous constraint that greatly limits the expressivity of the circuit, especially in the case of continuous symmetries. We propose an alternative paradigm for the symmetry-informed construction of variational quantum circuits, based on homogeneous spaces, relaxing the overly stringent requirement of equivariance. We achieve this by introducing horizontal quantum gates, which only transform the state with respect to the directions orthogonal to those of the symmetry. We show that horizontal quantum gates are much more expressive than equivariant gates, and thus can solve problems that equivariant circuits cannot. For instance, a circuit comprised of horizontal gates can find the ground state of an SU(2)-symmetric model where the ground state spin sector is unknown–a task where equivariant circuits fall short. Moreover, for a particular subclass of horizontal gates based on symmetric spaces, we can obtain efficient circuit decompositions for our gates through the KAK theorem. Finally, we highlight a particular class of horizontal quantum gates that behave similarly to general SU(4) gates, while achieving a quadratic reduction in the number of parameters for a generic problem. Link: https://lnkd.in/edCBWVMF #quantummachinelearning #quantumcomputing #research

Still interested in graphs? To continue on the material shared recently, this paper (book) was out today: "Graphs and their symmetries" by Teo Banica Abstract: This is an introduction to graph theory, from a geometric viewpoint. A finite graph X is described by its adjacency matrix d ∈ MN (0, 1), which can be thought of as a kind of discrete Laplacian, and we first discuss the basics of graph theory, by using d and linear algebra tools. Then we discuss the computation of the classical and quantum symmetry groups G(X) ⊂ G+(X), which must leave invariant the eigenspaces of d. Finally, we discuss similar questions for the quantum graphs, with these being again described by certain matrices d ∈ MN (C), but in a more twisted way. Link: https://lnkd.in/e523rU3E #math #graphs #symmetry #quantumsymmetry

Excellent paper this morning: "Quantum optimization using a 127-qubit gate-model IBM quantum computer can outperform quantum annealers for nontrivial binary optimization problems" by Natasha Sachdeva, Gavin S. Hartnett, Smarak Maity, Samuel Marsh, Yulun Wang, Adam Winick, Ryan Dougherty, Daniel Canuto, You Quan Chong, Michael Hush, Pranav S. Mundada, Christopher D. B. Bentley, Michael J. Biercuk, and Yuval Baum the Q-CTRL team Abtrstect: We introduce a comprehensive quantum solver for binary combinatorial optimization problems on gate-model quantum computers that outperforms any published alternative and consistently delivers correct solutions for problems with up to 127 qubits. We provide an overview of the internal workflow, describing the integration of a customized ansatz and variational parameter update strategy, efficient error suppression in hardware execution, and overhead-free post-processing to correct for bit-flip errors. We benchmark this solver on IBM quantum computers for several classically nontrivial unconstrained binary optimization problems -- the entire optimization is conducted on hardware with no use of classical simulation or prior knowledge of the solution. First, we demonstrate the ability to correctly solve Max-Cut instances for random regular graphs with a variety of densities using up to 120 qubits, where the graph topologies are not matched to device connectivity. Next, we apply the solver to higher-order binary optimization and successfully search for the ground state energy of a 127-qubit spin-glass model with linear, quadratic, and cubic interaction terms. Use of this new quantum solver increases the likelihood of finding the minimum energy by up to ∼1,500×  relative to published results using a DWave annealer, and it can find the correct solution when the annealer fails. Furthermore, for both problem types, the Q-CTRL solver outperforms a heuristic local solver used to indicate the relative difficulty of the problems pursued. Overall, these results represent the largest quantum optimizations successfully solved on hardware to date, and demonstrate the first time a gate-model quantum computer has been able to outperform an annealer for a class of binary optimization problems. Link: https://lnkd.in/eHeEK8MT #quantummachinelearning #research #quantumcomputing

A new LLM for quantum computing. "RydbergGPT" by David Fitzek, Yi Hong Teoh, Hin Pok Fung, Gebremedhin A. Dagnew, Ejaaz Merali, M. Schuyler Moss, Benjamin MacLellan, Roger G. Melko Abstract: We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vicinity of a quantum phase transition in Rydberg atoms in a square lattice array. We explore the ability of the architecture to generalize, by producing groundstate measurements for Hamiltonian parameters not seen in the training set. We focus on examples of physical observables obtained from inference on three different models, trained in fixed compute time on a single NVIDIA A100 GPU. These can act as benchmarks for the scaling of larger RydbergGPT models in the future. Finally, we provide RydbergGPT open source, to aid in the development of foundation models based off of a wide variety of quantum computer interactions and data sets in the future Link: https://lnkd.in/e-6gyumA #quantummachinelearning #llm #machinelearning #research #researchpaper