We introduce low-rank optimal control (LROC), a method for designing control pulses in open quantum systems whose full density-matrix simulation is prohibitively expensive. The method exploits a feature of quantum computing itself: because protocols are designed to preserve purity, the density matrix is dominated by a few pure states and admits an accurate low-rank factorization. LROC propagates only this factorized form and, by deriving the corresponding adjoint equation, obtains the gradient of any differentiable objective at the same reduced cost as the simulation, leading to a quadratic improvement in time and memory compared to the full master equation. We illustrate the breadth of the method on four superconducting-circuit tasks: preparation of a five-qubit GHZ state, a CNOT gate, qubit readout, and an error correction primitive, modeled with realistic multilevel transmons, decay, and strong drives, in each case reaching fidelities consistent with the intrinsic dissipation limits. LROC thereby extends pulse-level optimization to system sizes beyond the reach of existing gradient-based methods.
The open-loop optimization of quantum dynamics using gradient-based quantum optimal control methods involves calculating the time-ordered propagator and its gradient. In this Letter, we present a unifying framework for gradient-based quantum optimal control with respect to any general pulse parameterization by deriving the formal solution from first principles. For the case of unitary propagators, we derive a series expansion involving time-independent commutators and time-dependent coefficients, significantly reducing the number of matrix exponentials needed to compute the gradient. The expansion highlights the connection between derivatives of the propagator and operator evolution in the Heisenberg picture. The method is particularly suited for simulating optimal control tasks in quantum systems with local interactions, which is a common situation in large multi-qubit platforms. We compare the computational cost required for the series with the Gradient Optimization of Analytic conTrols (GOAT) method, and, focusing on the problem of preparation of a GHZ state, demonstrate more than an order of magnitude speedup for a qubit ladder and a chain geometry.
Ashutosh Mishra, Elena Lupo, Frank K. Wilhelm et al.· 0 citations
This work proposes an enhanced PINN scheme for quantum optimal control (PINNQOC) that circumvents limitations by incorporating Fourier feature embeddings, dynamic epoch normalization, and an informed pre-training routine, and provides a comprehensive analysis of computational times, iteration efficiency, and mean leakage.
M. D. Jiménez, M. D. Forlevesi, E. D. de Lima et al.· 0 citations
Variational quantum algorithms are a leading approach to near-term quantum computing, but their scalability can be limited by barren plateaus and the sampling cost of resolving small changes in the loss landscape. Here, we study the trainability of passive linear-optical quantum circuits and introduce a framework based on the ratio of sample variance to circuit variance. This ratio determines the number of circuit samples required to resolve local loss differences and gradients to proportional accuracy. We apply this framework to photon-number observables and identify both trainable and non-trainable regimes. Supported by analytic results and a numerically observed polynomial decay of the circuit variance, we find that fixed-order photon-number polynomials require only polynomially many samples as the system size grows, whereas high-order polynomials and observables based on output probabilities generally require exponentially many samples. Within the trainable regime, we further identify classes of observables in which quantum estimation achieves a polynomial speed-up over multiple classical methods. Within this family, neural network observables provide one practical construction that allow measurement outcomes to be efficiently processed into the desired polynomial. These results establish photonic variational quantum computing as a promising platform for near-term applications.
Alexander Makarovskiy, Adam J. Taylor, Zheng-Hao Li et al.· 1 citation
Quantum processors encode an N-point field in log_2(N) qubits, which renders nonlinear wave equations an important application for quantum simulation. Nonlinear evolution, however, requires the field values themselves, and these are not directly accessible without quantum measurement. Existing algorithms circumvent this measurement through linear embeddings and state copies, thereby obscuring its cost within the truncation order, the auxiliary dimensions, and the state preparation. In order to expose this cost, a hybrid split-step solver is proposed in which the field is measured, updated classically, and reloaded at every step, with all shots and gates accounted for in a single cost-and-error model. Since the entire field is available at every step, a property unavailable to linear approximations in strongly nonlinear regimes, the design of the solver reduces to a budgeting problem over the timestep, the polynomial degree, and the shot count. The coherent kernels of the solver are validated on superconducting hardware. An identical structure and bottleneck govern the viscous Burgers'equation in one and two dimensions. Because every step reads the full field, the quantum cost per step, measured as circuit depth multiplied by measurement shots, exceeds the classical cost with increasing grid size. The framework consequently identifies a coherent, measurement-free nonlinear update as the quantitative target that any end-to-end advantage must meet.
Zi-Qing Guo, Viraj Dsouza, Alex Khan et al.· 0 citations
We show that global optimal control can drastically suppress the impact of decoherence in globally driven superconducting quantum computing architectures, taking as a prototype a recently proposed quasi-two-dimensional ladder geometry. Using a tensor-network-based approach, we quantify how amplitude-damping and dephasing channels degrade the flow of quantum information along the ladder and the fidelity of one- and two-qubit gate operations. We then demonstrate that shaping the global drive compresses the gate sequences by an order of magnitude in time, restoring high gate fidelities. We stress that this mitigation is far from trivial: in a globally driven processor, dissipation acts on every physical qubit---including those outside the logical register that sustain the surrounding ordered phases---so its impact cannot be suppressed by protecting an isolated subsystem, and is instead overcome purely through the temporal shaping of the global drive.
Ashkan Abedi, Roberto Menta, Julien Despres et al.· 0 citations
Preparing smooth real-amplitude quantum states is a key subroutine in quantum solvers for dissipative PDEs, such as LCHS, where discretized positive weights must be encoded into amplitudes. Exact state-preparation decompositions can reach unit fidelity ideally, but their two-qubit depth grows quickly and causes severe fidelity loss on NISQ hardware. We propose a low-depth, hardware-aware variational ansatz tailored to smooth, weakly entangled, near-real target distributions typical of damped PDE dynamics. The circuit uses one layer of local Ry rotations to generate real amplitudes, a nearest-neighbor CZ entangling layer to introduce limited entanglement, and additional Rz rotations implemented virtually as frame updates. Virtual Rz operations add no physical pulses and do not increase circuit duration, providing extra degrees of freedom without enlarging the gate footprint; in simulation they are treated as ideal to isolate their benefit. From a tensor-network viewpoint, the alternating structure restricts the state to a low-bond-dimension MPS, matching the target smoothness (for 3 qubits, bond dimension<= 2). We optimize parameters with COBYLA to minimize infidelity and benchmark against exact state preparation (Qiskit) and a RealAmplitudes (CZ) baseline. Under depolarizing noise representative of NISQ and early fault-tolerant regimes, the proposed single-layer circuit achieves high ideal fidelity with O(n) depth and substantially higher noisy fidelity than deeper exact constructions. In coherent-noise sweeps, virtual Rz parameters absorb systematic phase errors and axis mismatch, maintaining near-unity fidelity over a wide error range. These results indicate that virtual-Rz-enabled, low-depth circuits provide a practical, noise-resilient state-preparation primitive for PDE solvers on NISQ and early FTQC hardware.
Toru Fujii· 0 citations
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