Roy Stephan’s Post

As members of the quantum consortium QED-C, we are proud to collaborate with the National Institute of Standards and Technology (NIST) and colleagues on exploring quantum benchmarking. As part of this effort, we recently co-authored a new paper on application-oriented performance benchmarks. Benchmarking is a critical element in the fast-changing landscape of quantum computing – quality benchmarking allows potential users and investors to compare different quantum computing architectures, algorithms, and approaches intelligently and efficiently. In particular, users need to gauge the performance of real-world applications.     Our recent paper extends a suite of Application-Oriented Benchmarks for quantum computing, covering a range of sizes and inputs, capturing key performance metrics related to the quality of results, total time of execution, and resources consumed. This paper includes the results of running these extended benchmarks with a variety of architectures and software tools, giving a clear measure of the field at present.    Read more here: https://lnkd.in/dp_RfUSh

🚀 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬? Dennis Nenno and I are exploring this uncharted territory but aren't quite there yet.   🔬 We solved dynamic optimization problems on quantum hardware, specifically using a quantum annealer. This marks a significant step in understanding how quantum computing can be applied to process industry problems, such as used in model predictive control.   🧩 The challenge was transforming the dynamic optimization problem into a QUBO format and conducting a detailed parameter study. While we haven't found a speed advantage over classical solvers (Ipopt), the novelty of our approach lies in its execution on quantum hardware, pushing the boundaries of current computational methods.   🌐 Our journey into quantum computing reveals the potential and current limitations in the field. It's a crucial step towards possibly achieving real-time process optimization through quantum computers in the future.   📖 Read more about our results on Arxiv: https://lnkd.in/eyXZ4aqq   #QuantumComputing #Innovation #ProcessIndustry #DynamicOptimization #QuantumAnnealing

Thrilled to share that our paper titled "Robust Virtual Network Function Optimization Under Post-Failure Uncertainty: Classical & Quantum Computing" has been accepted at the 20th International Conference on the Design of Reliable Communication Networks (DRCN). This paper was co-authored by my PhD supervisors Dr. Salimur Choudhury and Dr. Kai Salomaa. ✨ Key Highlights: ➡ The paper introduces a robust optimization framework for Virtual Network Function (VNF) deployment post-failure, addressing the limitations of deterministic models that fail to account for uncertainty in redeployment costs. ➡ It also proposes a quantum annealing-driven model to enhance resilience and efficiency in network management. ➡ The study demonstrates the superiority of robust and quantum computing models over deterministic methods, especially in handling uncertainties and supporting real-time decision-making for Service Function Chaining (SFC) failures. Pre-print Link: https://lnkd.in/eYFUYkwV Looking forward to sharing our findings at DRCN 2024, Montreal, Canada, from May 6-9, 2024. #quantumcomputing #quantumtechnology #quantumannealing #robust #optimization #quantumalgorithms #telecommunication #uncertainty

We are proud to share the new paper "Quantum Algorithm Exploration Using Application-Oriented Performance Benchmarks." The paper, written by QED-C's Standards and Performance Metrics TAC and interns of the TAC, provides the ability to gauge the performance characteristics of quantum computers as applied to real-world applications.  Click the link below to read on ArXiv how the benchmark framework can be instrumental in facilitating the exploration of algorithmic options and their impact on performance: https://lnkd.in/dp_RfUSh #quantum #quantumtechnoogy #quantumcoputers #benchmarkperformance #quantuminnovation

A perfect example of a joint work between tool developers and hardware experts: Our comprehensive overview paper of software compilation for Neutral Atom #QuantumComputing just got published in Quantum Science and Technology. Neutral Atom #QuantumComputing seems to be everyone’s darling these days. And with good reason as it offers numerous computational capabilities! But those are only as good as the corresponding compilers that make use of it. Hence, to fully utilize the potential of this technology, tool developers and hardware experts need to work hand-in-hand. Our paper aims to provide a basis for that: It reviews the physical background of Neutral Atoms and translates the corresponding principles into a form with which also tool developers can do something with. A special "feature": All take home-messages for tool developers are summarized in form of optimization constraints and figures of merit in self-contained boxes. For everyone interested in developing tools for this technology, definitely worth a look: 👉 https://lnkd.in/e4fTP4px This work is one of the results from several Technical Exchange Meetings we conducted within the Munich Quantum Valley in an effort to close the gap between tool developers and hardware experts (involving Technische Universität München, RWTH Aachen University, Forschungszentrum Jülich, Ludwig-Maximilians-Universität München, Max Planck Institute, Munich Center for Quantum Science and Technology, and the Software Competence Center Hagenberg). Huge thanks to Ludwig Schmid, David F. Locher, Manuel Rispler Sebastian Blatt, Johannes Zeiher, and Markus Müller for this great collaboration and Munich Quantum Valley as well as the European Research Council (ERC) for the support.

The paper "Quantum circuit synthesis via a random combinatorial search" describes a way to achieve high-fidelity state preparation, which is often a key first step in many quantum algorithms. This approach could make quantum circuit design more accessible and cost-effective, thereby accelerating the development of quantum computing technologies and their applications in various fields. It is significant for several reasons: Efficiency in Quantum Circuit Design: The author's use of a random search technique to find quantum gate sequences for state preparation or unitary operator synthesis is notable because it leverages the large multiplicity of quantum circuits capable of achieving unit fidelity. This implies that many different circuit configurations can successfully perform the same operation, providing flexibility in design. Reduction in Complexity: The finding that the fraction of perfect-fidelity quantum circuits increases rapidly with circuit size beyond the minimum required suggests that it's possible to achieve high performance with circuits just slightly larger than the smallest possible. This can significantly reduce the complexity and computational overhead needed to design circuits. Practical Application in Quantum Information Processing: The ability to identify near-optimal circuits by sampling only a few configurations and optimizing their parameters means faster development cycles for quantum information processing tasks. This is crucial in a field where designing efficient quantum algorithms is often complex and computationally intensive. Adaptability with Different Quantum Gates: Exploring the use of alternative two-qubit gates, which can potentially reduce the minimum size of quantum circuits, highlights adaptability in the approach. This could lead to more compact and efficient circuits, particularly beneficial for tasks requiring a large number of operations. https://hubs.ly/Q02wSH3F0

We are pleased to share a new paper on hybrid quantum optimization, titled “Solving non-native combinatorial optimization problems using hybrid quantum-classical algorithms”. The paper proposes an enhancement of hybrid quantum-classical algorithms through a key insight: instead of directly encoding the solution in a bitstring, measurement outcomes can be used as a resource for a classical algorithm to generate non-native solutions to hard combinatorial problems. This framework, called “non-native hybrid algorithms” (NNHA) was developed with one goal: to maximally exploit both quantum and classical resources to solve complex real-world problems at scale. By designing bespoke hybrid algorithms for complex problems, we can maximize quantum utility on today’s hardware and demonstrate quantum practicality on tomorrow’s solutions. https://hubs.ly/Q02nn1lw0 Jonathan Wurtz Shengtao Wang

🔍 Evaluation of Quantum and Hybrid Solvers for Combinatorial Optimization 🌟 In this recent study (outside EQUO, but very relevant for Quantum), we've meticulously evaluated D-Wave quantum and hybrid solvers, aiming to uncover their potential for real-world applications, such as energy optimization in data centers. 🧩 We've also compared both these solvers with the classical solver CPLEX, renowned for its efficiency. 🚀 Our goal? To showcase the unparalleled performance capabilities of D-Wave solvers and their potential in solving complex optimization problems. Our findings shed light on the promising future of quantum computing. 💡 #QuantumComputing #Optimization #DWaveSystems #Research #Innovation

An interesting paper titled "Control Requirements and Benchmarks for Quantum Error Correction" by Yaniv Kurman (Quantum Machines ) Lior Ella, Ramon Szmuk, Oded Wertheim, Benedikt Dorschner, Sam Stanwyck (NVIDIA) and Yonatan Cohen, focuses on key aspects necessary for achieving practical fault-tolerant quantum computation through successful implementation of quantum error correction (QEC). Key points are: 1. Focus on Quantum Error Correction (QEC): The paper emphasizes the importance of successfully implementing quantum error correction (QEC) for achieving useful fault-tolerant quantum computation. QEC involves using quantum gates and measurements to stabilize computational qubits, along with classical processing to interpret these measurements. 2. Role of Classical Control Systems: A significant portion of the paper is dedicated to discussing the classical control systems that run QEC codes. It notes that there has been a lack of specification and clarification regarding the requirements for these control systems. 3. Implementation of Low Latency Operations: The necessity of implementing low latency feed-forward quantum operations is highlighted. This is crucial for the effective functioning of QEC. 4. Benchmarks for Evaluating Control Systems: The paper proposes near-term benchmarks aimed at addressing the classical bottlenecks in QEC quantum computation. These benchmarks focus on the latency between a measurement and the subsequent operation that depends on it, considering aspects like quantum-classical parallelization capabilities and decoding throughput. 5. Analysis of QEC Control System Latency: Using dynamical system analysis, the paper shows how the latency performance of the QEC control system can determine the operational regime of a QEC circuit. This includes scenarios like latency divergence (making quantum calculations unfeasible), classical-controller limited runtime, and quantum-operation limited runtime (where classical operations do not delay the quantum circuit). 6. Development of QEC Control Systems: The analysis and benchmarks proposed in the paper are intended to facilitate the evaluation and development of QEC control systems. The goal is to realize these systems as a main component in fault-tolerant quantum computation. Read the full paper at: https://lnkd.in/eKruhDc2

0017: Quantum Optimization - Solving Max-Cut Problems with QAOA Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm used to find approximate solutions to combinatorial optimization problems. One of its applications is solving the Max-Cut problem, which is a classic problem in graph theory. QAOA is a promising algorithm for tackling combinatorial optimization problems, leveraging the power of quantum computing to find approximate solutions efficiently. MaxCut refers to a classic problem where the goal is to divide a graph's vertices into two groups such that the number of edges between these groups (cut edges) is maximized. This problem is known to be NP-hard but is fundamental in various optimization contexts, including quantum computing. In quantum optimization using tools like Qiskit, MaxCut is often used as a benchmark problem to demonstrate the capabilities of quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA). QAOA aims to find approximate solutions to combinatorial optimization problems like MaxCut by leveraging quantum circuits and classical optimization techniques. The idea is to encode the MaxCut problem into a Hamiltonian whose ground state corresponds to the optimal division of the graph's vertices. Quantum algorithms then attempt to find this ground state by evolving a quantum state using quantum gates and measuring to optimize the cut. Below is an implementation in IBM's Qiskit. Check comment section for link to repository for more insights and code. #QuantumComputing #Qiskit #QAOA #Optimization #GraphTheory #TechInnovation