Quantum technology could revolutionise fields such as physics and chemistry by solving problems that classical computers cannot handle. One promising method is analogue quantum simulation, in which scientists build physical models to mimic complex systems. New research introduces fresh ideas, such as controlled energy loss, to overcome challenges. These advances bring us closer to practical, large-scale quantum simulations, which could lead to breakthroughs in such fields as drug discovery or developing new materials.
Imagine identifying potential drug candidates for treating rare diseases in just hours or simulating complex drug-drug interactions to predict side-effects in a single day – tasks that once took years or were nearly impossible with traditional methods.
Quantum technology could make these scenarios a reality. One powerful method is analogue quantum simulation, in which scientists replicate the behaviour of complex quantum systems. In a recent article, Dylan Harley and colleagues introduced a novel approach to tackling the challenges of quantum simulation – one that could transform how we model chemical reactions or develop breakthrough pharmaceuticals at extraordinary speeds.
“Our study offers new approaches, such as using controlled energy loss – dissipation – to tackle key issues in analogue quantum simulation, including scalability and precision. These innovations enable more accurate simulations of complex systems without requiring perfect control, making quantum simulators more practical and effective. This breakthrough helps push the boundaries of what can be simulated, bringing us closer to large-scale quantum simulations,” explains Dylan Harley, PhD student at the Quantum for Life Centre, Department of Mathematical Sciences, University of Copenhagen, Denmark.
The quantum advantage
Simulating quantum systems has been a longstanding challenge in such fields as condensed matter physics and quantum chemistry. Classical computers struggle with these systems because the number of variables grows exponentially, enabling quantum computers to come into play.
“One of the most promising applications of quantum computers is to simulate systems that appear in physics and chemistry. This is very hard to do classically, but quantum hardware offers a natural advantage,” says Harley.
However, quantum hardware is still in its infancy. Fully error-free and scalable quantum computers are still being developed. Researchers are exploring different types of quantum simulations to bridge this gap. Digital quantum simulation uses quantum gates to approximate a system’s evolution, but this requires fault-tolerant computers, which are not ready yet.
Analogue quantum simulation involves creating a physical system that evolves similarly to the target system, requiring fewer resources. This makes it a promising option for near-term applications.
“Analogue simulation is more like building a toy model that naturally evolves in a way that mimics the system you are trying to simulate. It is like building a wind tunnel to study how an airplane wing behaves without constructing the entire airplane,” Harley explains.
The scaling problem
The challenge, however, is scaling these simulators to model more complex systems. Despite their potential, analogue quantum simulations face significant barriers. Dylan Harley and colleagues focus on understanding these barriers and finding solutions.
One major issue is the precision needed to control interactions between particles in the simulator. As systems grow, maintaining this precision rapidly becomes impossible in practice.
“The problem is that increasing the size of the system increases the precision, and the magnitude of necessary interactions with which you need to tune the interactions grows. This does not match the reality of what is achievable experimentally. We have a fixed range of energy scales and laser power on any given device. So there is a limit to how precisely we can engineer interactions,” says Harley.
This scaling issue creates difficulty in applying existing theoretical frameworks, such as those in Hamiltonian complexity theory, to real-world systems.
“We found that many theoretical results, which say that you can simulate anything by tuning interactions, require completely unrealistic energy scales for interesting system sizes. This is not feasible in practice.”
Turning challenges into opportunities
To address this, Dylan Harley and collaborators developed a new mathematical framework that considers real-world limitations. One key innovation is a novel use for dissipation in the quantum simulation framework.
“Dissipation is when a system loses energy to its surroundings through measurement or interaction with the environment. But instead of this causing problems, we can use it to control and stabilise the system. By repeatedly measuring, we can freeze it in place, enabling us to simulate complex interactions more precisely,” explains Harley.
Although dissipation usually leads to noise in classical systems, in quantum systems it can help to stabilise the state, making it a valuable tool for simulations.
“We showed that introducing dissipation enables us to get around some of the barriers that prevent scalable simulation. The quantum Zeno effect means that repeatedly measuring a system gets it stuck in a certain state. We can use this effect to engineer interactions that enable us to simulate systems in ways that would not otherwise be possible.”
This insight opens up new possibilities, potentially letting researchers simulate larger systems without the extreme energy scales needed in purely controlled interactions.
Constant back and forth
This construction displays the utility of hybrid methods in simulation, combining both analogue and digital methods. Although analogue simulation has its limitations, digital techniques can fine-tune control over specific system aspects. This hybrid approach could be the future of practical quantum simulators.
Although the results are still theoretical, they provide a basis for future experiments. Collaborating with experimental physicists will be key to testing these ideas in real-world systems.
“There is constant back and forth between theory and experiment. We need to speak to experimentalists to understand what is possible in practice and then use that knowledge to refine our theoretical models,” says Harley.
The team’s research revealed both limitations and new possibilities. They demonstrated that traditional methods face significant scalability problems but also showed that dissipation offers a workaround.
“We developed mathematical techniques to rigorously prove that certain theoretical approaches simply cannot work in the scalable regime. But broadening our perspective to include dissipative dynamics can achieve things that would not be possible otherwise,” explains Harley.
A quantum leap ahead
The applications of scalable quantum simulation are vast. In the near term, these techniques could tackle fundamental problems in physics and materials science, such as how electrons behave in solids.
“As quantum simulators become more powerful, they could also be applied to more complex systems, potentially revolutionising fields such as quantum chemistry and drug discovery,” says Harley.
However, many challenges remain in understanding the power of present-day analogue simulators.
“One of the greatest challenges is verification. Even if you build a simulator that can perform calculations beyond the reach of classical computers, how do you verify the results? This is a fundamental question we are still grappling with,” Harley explains.
Despite the hurdles, Dylan Harley remains hopeful about the future of quantum simulation. He believes that hybrid approaches, combining analogue and digital methods, will be important to maximise the potential of near-term simulators.
“There is enormous potential here. We are not there yet, but each step brings us closer to understanding how to design practical, scalable quantum simulations. By thinking creatively about the tools we have – such as dissipation and hybrid systems – we can make real progress towards this goal,” concludes Dylan Harley.
