We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamiltonian that encodes the loss function. Current quantum machine learning retains classical optimization: parameters are updated by a classical outer loop using gradient estimates from measurements, and quantum coherence has no role in the training dynamics, just as in any classical treatment of the same problem. In the quantum case, a parameter register initialized in superposition evolves unitarily, and probability amplitude concentrates near low-loss configurations through interference, without gradient computation or classical feedback. We give an explicit construction using block encodings and Hamiltonian simulation, applicable to arbitrary parameterized circuits. Numerical experiments on binary classification and interferometric phase estimation confirm that the evolved distribution peaks at the optimal parameters, matching gradient-based performance. The construction is compatible in principle with fault-tolerant implementations and extends to batched training via sequential Hamiltonian evolution.
Learning quantum Hamiltonians from low-temperature thermal state measurements is a fundamental problem in quantum physics. Scalability of existing methods is limited by the complexity of semidefinite optimization problems or partition function computation. Here, we develop a quantum analog of classical score matching m...
Shreya Shukla, Abhijith Jayakumar, A. Lokhov· 0 citations
Accurate identification of unknown quantum systems is essential for quantum computing, sensing, and control because the Hamiltonian governs quantum state evolution. This work proposes a QNN based framework for black box Hamiltonian learning and quantum system emulation using full density matrix trajectory learning. Unl...
Interactive Quantum Classifiers (IQCs) constitute a family of quantum machine learning models inspired by open quantum systems, in which the interaction between a target qubit and an environment is described by a Hamiltonian. Previous works introduced alternative Hamiltonian parameterizations and showed empirically tha...
F. Novaes, Fernando M. de Paula, J. V. Cardoso· 0 citations
The Hamiltonian of a natural quantum system is usually determined indirectly, by proposing microscopic models and comparing their predicted observables with experiment. A more direct approach is to extract information about the Hamiltonian from measurements on the system itself. In many settings, however, direct access...
Shunji Matsuura, Yoji Kawamura, J. Salfi et al.· 1 citation
Physics-informed neural networks are widely used for inverse problems governed by differential equations, whereas evidence for physics-informed quantum models remains limited. We study a hybrid quantum model for recovering the damping coefficient of an underdamped harmonic oscillator from sparse noisy observations. T...
Thanh (Tim) Nguyen, Hiep L. Thi, Thi Tran Luong· International Journal of Mod...· 0 citations
Simulating nonlinear dynamics with quantum computers has gained increasing attention. In general, such simulations require additional quantum resources because unitary quantum evolution is linear. A fundamental question is how nonlinear dynamics can be embedded into fully coherent, ancilla-free unitary circuits and how...
Yuki Ito, H. Hakoshima, Keisuke Fujii· 0 citations
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