Quantum Computing’s Next Test: Real-World Physics and the Challenge of Scaling
Quantum advantage—the moment a quantum computer performs a clearly defined task more efficiently than any classical machine—has become one of the most powerful promises in modern science. Yet despite rapid progress in quantum hardware, convincing demonstrations remain rare, particularly for practical problems. Two recent publications involving researchers at the Fraunhofer Institute for Applied Solid State Physics IAF argue that the field needs more realistic and more demanding standards. Their central message is that quantum advantage cannot be judged solely by spectacular small-scale experiments or idealized mathematical models. It must be evaluated under the physical conditions in which quantum systems actually operate and across problem sizes large enough to reveal whether a claimed benefit can survive in practice.
The first publication, a collaborative review by researchers from Fraunhofer IAF, ETH Zurich, and HQS Quantum Simulations, focuses on quantum chemistry and the limitations of conventional simulation strategies. Its title, “Beyond Unitary Quantum Simulation: Open-System Approaches to Quantum Chemistry toward Quantum Advantage,” points to a fundamental issue in the way many quantum algorithms are designed. They commonly assume that a molecule or material is a perfectly isolated system evolving through reversible, unitary quantum dynamics. In such models, the system is often reduced to its ground state and treated within the Born-Oppenheimer approximation, which separates the motion of atomic nuclei from that of electrons. These assumptions are mathematically useful, but they do not fully describe the behavior of real molecular and condensed-matter systems.
In nature, quantum systems are never completely isolated. Molecules exchange energy with their surroundings, lose coherence, relax into lower-energy configurations, and may reach thermal equilibrium through interactions with their environments. These processes are described by open-system dynamics rather than by the purely unitary evolution of an isolated quantum state. In quantum chemistry, this distinction can be decisive because chemical reactions, energy transfer, spectroscopy, catalysis, and material behavior are often controlled by precisely the effects that simplified models treat as external disturbances. The review therefore calls for quantum algorithms that can represent dissipation, noise, and environmental coupling not only as obstacles to be suppressed, but also as physical resources that can be engineered and controlled.
This proposal represents a significant change in perspective. Dissipation is usually associated with errors in quantum computing because unwanted interactions with the environment destroy fragile superpositions and entanglement. Carefully designed dissipative processes, however, can also drive a quantum system toward a desired state. Instead of forcing a quantum processor to construct a complex state entirely through precisely calibrated reversible operations, researchers may be able to use engineered interactions to prepare, stabilize, or sample chemically relevant states. In theoretical terms, this means developing algorithms based on open quantum dynamics, including master equations and controlled coupling to auxiliary systems or environments. Such approaches could make it possible to study states that are difficult to prepare through conventional Hamiltonian evolution alone.
The implications extend beyond chemistry. Open-system methods could contribute to dissipative state preparation, fault-tolerant quantum computation, quantum machine learning, and optimization techniques such as the Quantum Approximate Optimization Algorithm, or QAOA. In QAOA, a quantum processor alternates between operators associated with a problem and operators that mix possible solutions. The performance of the algorithm depends on parameters controlling these alternating operations, and finding useful parameters becomes increasingly difficult as the problem grows. By treating environmental interactions and state relaxation as part of the algorithmic design rather than merely as sources of error, researchers hope to create methods that are more closely aligned with the behavior of real devices. The review’s broader argument is that the most meaningful quantum algorithms may emerge not from idealized systems, but from models that deliberately incorporate the imperfections and physical mechanisms found in nature.
The second publication approaches quantum advantage from a different direction: how to determine whether an algorithm continues to improve as a problem becomes larger. Authored by Vanessa Dehn of Fraunhofer IAF, the study examines an extrapolation method for optimizing the parameters of a linear-ramp version of QAOA and evaluates how its runtime scales. QAOA has attracted attention because it could eventually address difficult combinatorial optimization problems in areas such as portfolio management, logistics, network design, materials discovery, and machine learning. Yet a quantum algorithm that performs well on a handful of small examples does not automatically provide evidence of a practical advantage. Small instances can hide the costs of parameter optimization, circuit execution, error correction, and data processing that become dominant at larger scales.
Scaling analysis addresses this problem by asking how the computational resources required by an algorithm change when the number of variables or constraints increases. Classical algorithms may solve small instances rapidly but become prohibitively expensive as the problem expands. A quantum algorithm would need to demonstrate not merely a temporary improvement, but a favorable growth rate that remains meaningful as the input size increases. In the study, simulations indicate that QAOA-based portfolio optimization may show scaling advantages over classical approaches within the problem sizes examined. The result is not a definitive demonstration of quantum advantage, but it offers a framework for testing whether a potential advantage is structurally plausible rather than an artifact of a carefully selected small example.
A key element of the work is the use of extrapolation to transfer algorithm parameters from smaller problems to larger ones. Parameter optimization is one of the major practical challenges facing QAOA because every candidate parameter set may require repeated quantum measurements to evaluate its quality. As the circuit depth and problem size increase, this search can become extremely expensive. An extrapolation-based strategy attempts to identify how useful parameters change with system size and then predict suitable values for larger instances. If reliable, this approach could reduce the amount of classical optimization required before a quantum circuit is executed. It also creates a more direct way to study runtime scaling, because researchers can evaluate larger virtual instances without treating every case as an entirely new optimization problem.
Together, the two publications highlight two different dimensions of credibility in quantum computing. The open-system review asks whether the physical models used to design quantum algorithms reflect the behavior of molecules, materials, and hardware in the real world. The QAOA study asks whether an algorithm’s apparent success survives the transition from small demonstrations to larger and more demanding problems. These questions are closely connected. A quantum processor may be theoretically faster but unable to maintain the required state in the presence of noise and dissipation. Conversely, an algorithm may tolerate realistic conditions but lose its advantage when the costs of parameter tuning, measurement, and error correction are included. A convincing claim therefore requires both a realistic physical model and a transparent account of how performance changes with scale.
The researchers’ message arrives as the quantum industry faces increasing pressure to distinguish measurable progress from ambitious projections. Quantum machine learning studies have already explored mathematically provable advantages and investigated the types of data structures that quantum models might process particularly effectively. The new work adds chemistry, open-system physics, and algorithmic scaling to that discussion. It suggests that the future of quantum advantage will not be defined by a single dramatic experiment, but by a collection of carefully tested results showing where quantum methods outperform classical alternatives, why they do so, and whether the benefit remains as systems become larger and more realistic. For quantum science, that shift could be crucial: the path to a useful quantum computer may depend less on escaping the imperfections of nature than on learning how to turn those imperfections into part of the computation.
Article Title: Beyond Unitary Quantum Simulation: Open-System Approaches to Quantum Chemistry toward Quantum Advantage
News Publication Date: 14 May 2026
Web References: https://doi.org/10.1016/bs.aamop.2026.04.001
References: 10.1016/bs.aamop.2026.04.001
Image Credits: Fraunhofer IAF
Keywords: quantum computing, quantum advantage, quantum chemistry, open quantum systems, dissipative dynamics, QAOA, quantum algorithms, quantum simulation, quantum optimization, Fraunhofer IAF
