How a Quantum Computer Distinguished Past from Future express
A film of a shattered cup cannot be watched backwards: the growth of disorder, or entropy, points the way of time. Yet the equations of the microworld are symmetric — past and future are on equal footing. To understand how the arrow of time emerges, scientists used a quantum processor made of carbon diamond with ten qubits (quantum analogs of bits).
They ran processes where heat flows from hot to cold, and their reverse copies, inserting measurements that forced entropy to grow. The data was fed to a neural network. Without any hints, it learned to distinguish forward heat flow from backward with 92% accuracy, as if plucking signs of irreversibility out of the noise.
This is more than just a game. The ability to read the arrow of time in quantum systems will bring us closer to building microscopic engines and reveal how irreversibility is born from symmetry. Incidentally, the diamond processor operated at room temperature — a rarity for quantum devices.
Original abstract
as written by the authors arXiv:2603.10344
Understanding the emergence of the thermodynamic arrow of time in microscopic systems is of fundamental importance, particularly given that unitary evolution preserves time-reversal symmetry. While projective measurements introduce temporal irreversibility, identifying this asymmetry from single evolution trajectories in the presence of stochastic fluctuations presents a considerable challenge. Here, we harness machine learning to identify the arrow of time from individual trajectories generated by a programmable ten-qubit quantum processor based on a nitrogen-vacancy center in diamond. We implement quantum circuits that realize unitary evolutions where heat flows from hotter to colder subsystems and their time-reversed counterparts. Projective measurements inserted in these processes induce entropy production, and their outcomes constitute the evolution trajectory. We demonstrate that an unsupervised clustering algorithm autonomously divides the experimental trajectories into two distinct groups without prior knowledge, while a convolutional neural network identifies the temporal direction of these trajectories with approximately 92% accuracy. In addition, we show that a diffusion-based generative model reproduces essential signatures of directional energy flow and entropy production. Our results establish machine learning as a powerful tool for uncovering underlying physical processes from complex experimental data, advancing the interface between quantum thermodynamics and artificial intelligence.
Remove the measurements from the process, and symmetry returned — the neural network could no longer tell the direction of time. It is quantum measurements that 'switch on' irreversibility.
The idea of recognizing the arrow of time in quantum data harks back to the film 'Tenet', where inverted entropy makes time flow backwards.