Thermodynamics of Quantum Trajectories on a Quantum Computer

No prior work had resolved how to effectively bias open-system dynamics using current noisy intermediate-scale quantum hardware. Researchers often struggle to maintain control over quantum states when they interact with external environments. This uncertainty drove the need for new methods to manipulate these interactions directly. Prior research has shown that quantum computers offer a unique platform for simulating complex physical processes. However, these devices remain susceptible to noise, which complicates the precise execution of long-duration quantum operations. That gap motivated the current investigation into using ancilla coupling to influence system evolution. It was already known that quantum trajectories provide a useful framework for describing open-system behavior. This study builds upon those foundations to address the limitations of existing digital quantum architectures.

The aim of this research is to investigate how open-system dynamics can be simulated and controlled on a quantum computer. The authors seek to address the challenge of manipulating quantum states when they interact with an environment. This study explores the use of an ancilla to influence the evolution of a system of interest. The researchers propose a thermodynamic analogy to bias the probability of specific quantum trajectories. They intend to demonstrate that desired measurement patterns can be achieved through this biasing technique. The work addresses the difficulty of implementing non-Markovian dynamics on digital, gate-based quantum hardware. By conducting proof-of-principle experiments, the team evaluates the feasibility of their approach on current devices. This investigation provides a foundation for understanding the control of complex quantum systems in the noisy intermediate-scale era.

The researchers employ a simulation design that couples a target system to an auxiliary qubit. This approach facilitates the observation of open-system evolution through iterative interaction cycles. Each cycle concludes with a projective measurement of the auxiliary component to record the state. The team maps these recorded sequences to a thermodynamic framework for statistical analysis. They implement the gate-based operations on the ibmq_jakarta processor to test their theoretical model. This review approach focuses on the practical execution of biased dynamics on digital hardware. The authors verify the consistency of their results by comparing observed patterns against expected theoretical distributions. They evaluate the performance of their method by assessing the success rate of achieving targeted measurement outcomes.

The study demonstrates that biasing quantum trajectories is achievable on current noisy intermediate-scale quantum devices. The authors report proof-of-principle success in steering measurement outcomes toward desired temporal correlations. They observe that their method effectively influences the probability of specific measurement patterns within the simulated system. The results confirm that non-Markovian dynamics can be implemented on a unitary, gate-based quantum computer. The data show that the ancilla-coupling technique allows for the manipulation of system evolution. The researchers highlight that their findings are currently limited to small-scale systems. They document the specific challenges associated with maintaining control over complex dynamics in the presence of hardware noise. The analysis provides evidence that thermodynamic analogies offer a useful tool for managing quantum system behavior.

The authors demonstrate that biasing quantum trajectories is feasible on current gate-based hardware. Their findings suggest that specific measurement patterns can be encouraged through thermodynamic-like control mechanisms. This work highlights the inherent difficulties in managing non-Markovian dynamics within small-scale quantum processors. The researchers propose that their approach offers a viable path for future investigations into complex system behavior. These results provide a proof-of-principle for manipulating quantum evolution on publicly accessible devices. The study emphasizes that controlling these dynamics remains a significant challenge for digital quantum platforms. Future efforts should focus on scaling these techniques to larger, more stable quantum systems. The authors conclude that their methodology effectively bridges the gap between theoretical thermodynamics and practical quantum computation.