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An open-loop control is computed from a dynamical model and the initial preparation, then applied without changing it in response to measurements during that run. Controllability determines which transformations are possible in principle; field design must also account for duration, available control generators, amplitude and bandwidth limits, and calibration errors.

Three useful model-based strategies are the following. Resonant quantum control chooses carrier frequencies and phases to select transitions, and uses quantum pulse area or elementary rotation gates to compile the desired transformation. Adiabatic quantum control chooses a slowly varying path of Quantum Hamiltonians whose relevant eigenstate connects the initial and target states, maintaining a sufficiently large spectral gap. Quantum optimal control parametrizes or varies the fields and maximizes a fidelity or observable objective, while penalizing resources and enforcing the dynamical equations. These can all produce predetermined laboratory waveforms; experimental feedback is a separate design choice.

For a detailed variational construction, letChoose a positive target observable $O$, for example $O=|{\psi }_d⟩⟨{\psi }_d|$ for pure-state transfer. MaximizeThe terminal term is the target probability or observable expectation, and the quadratic integral penalizes field energy. A costate $\chi (t)$ enforces the Schrödinger equation through the real augmented functionalVariation in $\chi$ recovers the state equation. Integrating the variation in $\dot{\psi }$ by parts gives the terminal boundary term $2\mathrm{Re}⟨O\psi (T)-\chi (T)|\delta \psi (T)⟩$ and the interior adjoint equation. Thus the Euler-Lagrange conditions areThe costate obeys the same Hamiltonian equation but is propagated backward from its terminal value. The field variation isHence the variational costate gradient for Hamiltonian quantum control isAt an unconstrained stationary field,These equations couple forward and backward boundary data, so this is not a single initial-value integration, nor a direct feedback law for an unknown experimental state.

A practical direct-adjoint looping algorithm starts from an admissible trial field. Propagate $\psi$ forward and store its trajectory; set $\chi (T)=O\psi (T)$ and propagate $\chi$ backward; compute $g_k$ along the two trajectories; then update $u_k^{\mathrm{new}}=u_k+\eta g_k$ for a positive step size $\eta$. A line search evaluates the actual objective and decreases $\eta$ until the ascent condition is met. Project into the admissible amplitude range or optimize a finite Fourier or pulse basis to enforce bandwidth constraints. Repeat until the projected gradient and change in objective are small. Repeated initializations help explore different local optima; the stationarity equations alone do not guarantee a global optimum.

For a piecewise constant discretization, define $H_j=H_0+{\sum }_k{u}_{k,j}{H}_k$ and $U_j=e^{-i\Delta t{H}_j/\hslash }$. Forward multiplication gives the state trajectory; backward multiplication gives the costate. The exact matrix-exponential derivative isThis supplies accurate discrete gradients even when $H_j$ and $H_k$ do not commute. The small-step expression $\partial U_j/\partial u_{k,j}\simeq -i\Delta t{H}_k{U}_j/\hslash$ is only an approximation. Forward/backward factorization is the basis of gradient ascent pulse engineering. For a full-gate objective, propagate all basis columns or $U(t)$ itself and use a terminal objective such as the phase-insensitive unitary gate error; optimizing one state alone does not implement a prescribed operation on every input.

To implement the result, convert the optimized envelope and quadratures into the calibrated electric or magnetic drive fields. An arbitrary-waveform source can set time-domain quadratures; optical spectral pulse shaping applies a complex mask to the available pulse spectrum,using, for example, a 4f pulse shaper and spatial light modulator. Finite spectral support, mask resolution and actuator limits must be included in the optimization: smoothing an unconstrained optimum afterward may spoil it. Simulating the calibrated final pulse, optionally over an ensemble of detunings or calibration errors, checks its predicted fidelity and robustness. The computed fields are then executed as open-loop control, with all measurements used for later calibration rather than an assumed instantaneous state readout.

Write $\cos {\omega }_{1}t=(e^{i{\omega }_{1}t}+e^{-i{\omega }_{1}t})/2$ and $\Delta \omega ={\omega }_2-{\omega }_1$. Multiplication of the matrix from part (e) givesThese are exactly the constant, difference-frequency, and sum-frequency terms in the PDF, with each Hermitian pair visible. For a weak, slowly varying envelope, the terms at $2{\omega }_1$ and ${\omega }_1+{\omega }_2$ average out by the rotating-wave approximation. To discard the other transition as well, its detuning must be nonzero and large compared with the drive coupling and envelope bandwidth. Sufficient scale conditions arewhere $B$ is a characteristic envelope bandwidth; turn-on transients must satisfy the same spectral restrictions. ThenSmall coupling compared with the optical frequencies alone is not enough for this selective approximation: if $|\Delta \omega |$ is comparable with the pulse bandwidth or Rabi scale, the spectator transition remains driven. A long selective gate also accumulates a small off-resonant Stark phase of order $(A_1{d}_2{)}^2/\Delta \omega$, so that error must be acceptable or compensated. These assumptions distinguish resonant quantum control from an exact decoupling.

Hamiltonian engineering chooses accessible interactions so that their actual or effective Quantum Hamiltonian produces the desired evolution. A typical model is $H_f(t)=H_0+{\sum }_k{f}_k(t)H_k$; the controls may change frequencies, amplitudes, phases or coupling strengths. Three standard model-based open-loop control approaches illustrate different mechanisms.

In resonant quantum control, select a transition with a near-resonant field. In a rotating frame, after neglecting rapidly oscillating terms in a justified regime, an effective two-level drive can be $H_{\mathrm{eff}}(t)=\Omega (t){\sigma }_x/2$. When its axis is fixed,The pulse area sets the rotation angle and the drive phase selects the transverse axis. A resonant pulse of area $\pi$ swaps the two level populations. This requires knowledge of transition frequencies, couplings and the validity of the selective-drive approximation.

In adiabatic quantum control, design a slowly varying Quantum Hamiltonian whose desired instantaneous eigenstate connects the initial and final states. For a nondegenerate eigenstate separated by a gap, the quantum adiabatic theorem explains why sufficiently slow evolution follows that state up to a phase. The relative size of matrix elements of $\dot{H}$ and squared spectral gaps controls the approximation; degeneracies or rapid changes invalidate simple following. This engineers a state path rather than a prescribed resonant rotation.

In average Hamiltonian engineering, apply a predetermined sequence of fast pulses $P_j$ and let the system evolve for intervals ${\tau }_j$ in the corresponding frames. For cycle length $T_c={\sum }_j{\tau }_j$, the leading effective generator isChoose the sequence to cancel unwanted terms or retain desired couplings. For instance, equal intervals under $H_0=\omega {\sigma }_z/2$ and its ${\sigma }_x$ conjugate cancel the leading generator because ${\sigma }_x{\sigma }_z{\sigma }_x=-{\sigma }_z$. Noncommuting terms produce higher-order commutator corrections, so fast cycling, finite pulse errors and the accuracy of the model matter. All three methods determine a waveform or sequence before its execution; none automatically corrects an unknown mismatch during that run.

After expressing a driven Quantum Hamiltonian in a suitable interaction or rotating frame, discard terms oscillating rapidly compared with the retained dynamics. Near a selected transition, this leaves resonant terms that accumulate coherently while counter-rotating terms average out to leading order. The drive strength and detuning must be small relative to the discarded oscillation frequencies over the intended regime; strong driving can require the neglected frequency shifts and corrections. The approximation supports resonant quantum control, but it is not exact merely because the carrier is tuned to resonance.

A drift Quantum Hamiltonian is strongly regular when its energy levels are nondegenerate and distinct unordered transitions have distinct absolute nonzero Bohr frequencies. Sometimes regularity is required only on the graph of allowed control transitions. This lets polynomials in the squared drift adjoint action separate different coupling quadratures and permits frequency-selective resonant quantum control. This notion is unrelated to a strongly regular graph.