IonQ and Oak Ridge National Laboratory Demonstrate a Novel, Scalable, and …

IonQ and ORNL have achieved an industry-first, with a NISQ-friendly quantum algorithm that can help accelerate time-to-solution for hard optimization problems

Partnering to Advance Quantum Optimization

Optimization is one of the most anticipated applications for quantum computers, due to a wealth of applications in both industry and scientific discovery. Applications range from finding better schedules and manufacturing processes, to improved supply chains and shipping routes, to the smart allocation of resources in power grids. The difficulty of these tasks is high due to the rapid growth in the number of possible solutions, the lack of mathematical structure, as well as the large numbers of variables and the target solution quality that are needed for industry grade solutions.

IonQ's Applications Team recently collaborated with researchers at Oak Ridge National Labs (ORNL) to demonstrate an optimization method that leverages near-term quantum computers in a new way, making use of noise-tolerant methods that facilitate the discovery of optimal and near-optimal solutions to the world's hardest optimization problems. The method is based on the Quantum Imaginary Time Evolution principle (QITE), which allows for identifying optimal or near-optimal solutions of optimization problems, formulated as finding the ground state of Hamiltonians (a mathematical operator that describes the total energy of a quantum system). This in turn can be used to solve a potentially wide range of formulations for hard optimization problems such as MaxCut, clique finding, graph partitioning, and more.

One of the key findings of this work is that QITE can significantly outperform other quantum optimization algorithms such as QAOA (Quantum Approximate Optimization Algorithm) in terms of time-to-solution and required circuit depth. Here at IonQ, we are currently developing QITE-based solutions and decomposition methods as key enabling quantum technologies for scalable optimization. This paves the way for tackling large optimization problems on near-term quantum computers. For more details on the research, check out our recent technical pre-print paper.

Combinatorial Optimization: A Problem Class Ubiquitous in the Real World

Many industries-from finance to logistics to manufacturing-face combinatorial optimization problems, where the goal is to find the most efficient solution among a large set of possibilities. For instance, optimizing delivery routes, stock portfolios, or supply chains often involves solving NP-hard problems, which are extremely challenging for classical computers to handle as the problem size grows. Even small improvements in efficiency may generate significant business value, including cost savings and competitive advantages.

As quantum computing moves from theory to practical applications, combinatorial optimization is rapidly becoming a promising area for showcasing the technology's potential. Moreover, as the industry gets closer to harnessing the power of quantum hardware to solve real-world problems, a critical challenge remains: how to make quantum algorithms work effectively on noisy, near-term hardware.

This blog highlights the recent innovative research by Oak Ridge National Laboratory (ORNL) and IonQ that addresses these technical challenges and represents a step towards realizing commercial quantum advantage. The breakthrough research demonstrates a novel algorithm designed for efficient combinatorial optimization on IonQ quantum hardware. Moreover, it has implications for direct business impact, including potential reductions in cost, time, and computational resources. In the longer term, this research could reshape how businesses and industries approach complex optimization problems.

The Technical Breakthrough: What Makes This Algorithm Different?

From a technical standpoint, the algorithm addresses some of the core limitations of existing quantum approaches, like the well-known Quantum Approximate Optimization Algorithm (QAOA), which struggles with noisy quantum hardware and requires deep circuits.

The core idea of the novel QITE method is to suppress higher energy states exponentially, which is accomplished by applying a hybrid quantum algorithm that uses shallow quantum circuits to project a set of candidate solutions into subspaces of low energy. The quantum algorithm has additional distinctive features such as being able to be operated at extremely low shot counts and low total number of 2-qubit gates.

To break it down, here are some of the key innovations of around the QITE method:

1. Reduced Circuit Depth: Unlike QAOA, which demands multiple layers of quantum gates, this new algorithm uses linear-depth circuits. By applying a linear-depth circuit design, the algorithm requires fewer operations, lowering susceptibility to noise and thus making it suitable for near-term quantum devices.This means the algorithm can run effectively on today's quantum processors, which are highly sensitive to noise as circuit depth increases. Achieving high fidelity with shallow circuits is critical for leveraging today's quantum computers.

2. Quantum Imaginary Time Evolution (QITE): The paper introduces a novel optimization technique using a modified form of imaginary time evolution. The QITE approach enables fast convergence to optimal solutions by focusing on energy states closest to the ground state, increasing fidelity in solution accuracy. By focusing on the most important parameters, the algorithm reduces computational overhead and improves convergence.

3. High Entanglement at Low Depth: Quantum entanglement is a key factor that distinguishes quantum from classical computing. The algorithm generates large amounts of entanglement even at low circuit depths, which is crucial for helping to solve complex problems that classical computers cannot handle. This is a promising sign that the algorithm could scale well and eventually outperform classical approaches on more significant tasks.

Experimental Validation on Real Hardware: One of the major achievements of this research is that it was tested on real quantum hardware - IonQ's trapped-ion quantum computers - solving problems with up to 32 qubits. The algorithm found optimal solutions with high accuracy without relying on error mitigation or post-processing.The QITE algorithm's compatibility with trapped-ion quantum computers, such as IonQ Aria and Forte, demonstrates scalability potential in the real-world.

The figures above show the algorithm's performance on IonQ hardware - Aria and Forte, respectively - across various problem types. The data underscore QITE's robustness against hardware noise, ensuring high fidelity in solutions even under real-world conditions-a notable achievement for quantum applications that are often susceptible to errors with other quantum hardware modalities or providers.

Why This Matters: Implications for Solving Real-World Business Problems

We are a commercial quantum computing company and so we are always thinking about how our technology can solve enterprise-grade problems. Here is where this research can likely play a role:

• Quantum for Optimization: The algorithm helps to show that today we can already use quantum computers - specifically IonQ Quantum hardware - to potentially find optimal or near-optimal solutions for complex problems like MaxCut, a standard combinatorial optimization problem with applications in network design, graph theory, resource allocation, and beyond.

• Scalability to Commercial Problems: By solving instances with up to 32 qubits on trapped-ion quantum computers (IonQ's Aria and Forte systems), the novel algorithm in this research paves the way for addressing real-world, computationally complex, business problems. These advancements mean that the plethora of industries that have complex optimization challenges may soon benefit from quantum solutions, unlocking new efficiencies in supply chain management, logistics, finance, and more.

• Industry-Specific Use Cases:

  • Finance: Quantum algorithms like the QITE algorithm could improve the efficiency of portfolio optimization, risk management, and fraud detection. With the ability to explore multiple scenarios simultaneously, financial institutions could potentially better manage risks and maximize returns.

  • Logistics and Supply Chain Management: Companies dealing with complex logistics challenges could find benefits from quantum-powered optimization to minimize costs and increase efficiency. The new algorithm's ability to handle large, noisy datasets makes it a potential candidate for things like optimizing global supply chains, routing, and delivery systems.

  • Manufacturing and Operations: In industries where resource allocation, scheduling, and inventory management are critical, quantum optimization could lead to significant improvements in production efficiency. For example, optimizing machine schedules in a factory could help to reduce downtime and improve throughput.

Healthcare and Pharmaceuticals: Drug discovery, genomic analysis, and clinical trial optimization are areas where combinatorial optimization plays a crucial role. With the ability to solve larger problems more efficiently, quantum algorithms could help to accelerate the development of new treatments and reduce the time to market.

What's Next? Accelerating the Journey Towards Enterprise-Grade, Commercial Advantage Capable Quantum Computers

We are pleased with the exciting results from this study which will support our journey towards enterprise-grade computers that are commercial-advantage capable. As we stay focused on our three commercial advantage pillars of performance, scale, and enterprise-grade capabilities, we aim to pursue the following:

• Continued Scaling: This algorithm demonstrated success with 32 qubits, but as our quantum hardware improves, we aim to solve problems with hundreds and even thousands of qubits, which will help to further unlock transformative business applications.

• Hybrid Quantum-Classical Approaches: Along with our quantum hardware, we are developing quantum hybrid approaches that combine best-in-class classical and quantum computing to solve different parts of optimization problems, which may enable quantum-accelerated compute for better performance and solution quality.

Expanding to New Problem Types: Our dedicated Applications Team is constantly exploring new ways to extend our algorithms to more complex problem classes across various industries, such as quadratic assignment, vehicle routing, flow optimization, bin packing, higher-order binary optimization, and more.

Stay tuned to hear more about our progress on these fronts. And, come visit us at Q2B to learn more!

Public link

Please use the above public link if you want to share this noodl on another website.