## Introduction

Quantum adiabatic processes are a powerful strategy to implement quantum state engineering, which aims at manipulating a quantum system to attain a target state at a designed time T. In the adiabatic scenario, the quantum system evolves under a sufficiently slowly-varying Hamiltonian, which prevents changes in the populations of the energy eigenlevels. In particular, if the system is prepared in an eigenstate of the Hamiltonian H at a time t = 0, it will evolve to the corresponding instantaneous eigenstate at later times. This transitionless evolution is ensured by the adiabatic theorem, which is one of the oldest and most explored tools in quantum mechanics1,2,3. The huge amount of applications of the adiabatic behavior has motivated renewed interest in the adiabatic theorem, which has implied in its rigorous formulation4,5,6,7,8,9,10 as well as in new bounds for adiabaticity11,12,13. In quantum information processing, the adiabatic theorem is the basis for the methodology of adiabatic quantum computation (AQC)14, which has been originally proposed as an approach for the solution of hard combinatorial search problems. More generally, AQC has been proved to be universal for quantum computing, being equivalent to the standard circuit model of quantum computation up to polynomial resource-overhead15. Moreover, it is a physically appealing approach, with a number of experimental implementations in distinct architectures, e.g., nuclear magnetic resonance16,17,18, ion traps19 and superconducting flux quantum bits (qubits) through the D-Wave quantum annealer20,21,22.

Recently, the circuit model has been directly connected with AQC via hybrid approaches23,24. Then, an adiabatic circuit can be designed based on the adiabatic realization of quantum gates, which allows for the translation of the quantum circuit to the AQC framework with no further resources required. In particular, it is possible to implement universal sets of quantum gates through controlled adiabatic evolutions (CAE)24. In turn, CAE are used to perform one-qubit and two-qubit gates, allowing for universality through the set of one-qubit rotations joint with an entangling two-qubit gate25,26. However, since these processes are ruled by the adiabatic approximation, it turns out that each gate of the adiabatic circuit will be implemented within some fixed probability (for a finite evolution time). Moreover, the time for performing each individual gate will be bounded from below by the adiabatic time condition4,5,6,7,8,9,10. For a recent analysis on adiabatic control of quantum gates and its corresponding non-adiabatic errors, see ref. 27.

In order to resolve the limitations of adiabaticity in the hybrid model, we propose here a general shortcut to CAE through simple time-independent counter-diabatic assistant Hamiltonians within the framework of the superadiabatic theory28,29,30,31. The physical resources spent by this strategy will be governed by the quantum circuit complexity, but no adiabatic constraint will be required in the individual implementation of the quantum gates. Moreover, the gates will be deterministically implemented with probability one as long as decoherence effects can be avoided. In particular, we discuss the realization of rotation gates and arbitrary n-qubit controlled gates, which can be used to design different sets of universal quantum gates. This analog approach allows for fast implementation of individual gates, whose time consumption is only dictated by the quantum speed limit (QSL) (for closed systems, see refs 32, 33, 34, 35). Indeed, the time demanded for each gate will imply in an energy cost, which increases with the speed of the evolution. In this context, by analyzing the energy-time complementarity, we will show that the QSL provides an energy cost for superadiabatic evolutions that upper bounds the cost of adiabatic implementations.

Let us begin by discussing the design of adiabatic quantum circuits as introduced by Hen24 through the implementation of quantum gates via CAE.

In order to define quantum gates through CAE, we will introduce a discrete bipartite system associated with a Hilbert space . The system is composed by a target subsystem and an auxiliary subsystem , whose individual Hilbert spaces and have dimensions and , respectively. The dynamics of will be governed by a Hamiltonian in the form24

where f(0) = g(1) = 1, g(0) = f(1) = 0 and {Pk} denotes a complete set of orthogonal projectors over , so that they satisfy PkPm = δkmPk and ∑kPk = 1. Alternatively, we can write Eq. (1) as

with denoting a Hamiltonian that acts on . Suppose now that we prepare the system in the initial state , where is an arbitrary state of and is the (non-degenerate) ground state of H(b). Then is the ground state of the initial Hamiltonian H(b). By applying the adiabatic theorem3,36, a sufficiently slowing-varying evolution of H(t) will drive the system (up to a phase) to the final state

where is the ground state of 24.

#### Single-qubit unitaries and controlled two-qubit gates

We can perform a single-qubit unitary transformation through a general rotation of an angle ϕ around a direction on the Bloch sphere. In this direction, we begin by preparing the system , taken here as two qubits, in the initial state , where are the computational states of the auxiliary system . Then, we let the system adiabatically evolve driven by the Hamiltonian24

where H0(s) and Hϕ(s) are adiabatically-evolved Hamiltonians, whose effect will be restricted to the respective subspaces of the projectors , where is a unitary vector on the Bloch sphere associated with and , with {σi} denoting the set of Pauli matrices. The Hamiltonians are taken as , where ξ  {0, ϕ}, ωħ sets the energy scale (ω > 0), θ0 is a constant parameter and s = t/τ denotes a dimensionless (parametrized) time, with τ the total time of evolution. Note then that

By writing the initial state of as , where is an arbitrary (not necessarily known) qubit state, the final state follows from Eq. (3), i.e. . Note that is the ground state of Hξ(s), reading , with ξ  {0, ϕ}. An equivalent form of writing is given by

Hence, we have a probabilistic implementation of the rotated target state for an arbitrary angle ϕ around an arbitrarily chosen axis , with probability . In particular, this probability approximates to one by taking θ0 ≈ π.

In order to perform controlled rotations of a qubit by an angle ϕ around a direction , the starting point will be to take the subsystem as a two-qubit system and keeping as a single auxiliary qubit. The Hamiltonian is now chosen to be

which will govern the evolution of the initial composite state , with . From Eq. (3), the final state of the subsystem in the limit θ0 → π is now the controlled rotated vector . By combining controlled rotations with the single-qubit unitaries discussed above, it is possible to design universal sets of quantum gates through an adiabatic implementation.

## Results

In this Section we present the main results of this work. We start by generalizing the adiabatic implementation of quantum gates proposed in ref. 24 for n-qubit controlled gates. Even though n-qubit controlled gates can be decomposable into one and two-qubit gates (see, e.g. refs 25,37), this implementation implies in an extended class of adiabatic universal gates, e.g. the set {Toffoli, Hadamard}38,39. Then, we will derive the main result of this work, which is a shortcut for general adiabatic circuits through constant counter-diabatic Hamiltonians, which implies in the possibility of fast analog implementations of quantum circuits. Moreover, we will present an analysis of the quantum speed limit in the context of the energetic cost of the superadiabatic circuit.

In order to implement n-controlled gates, we define the subsystem as an (n + 1)-qubit system, with the first n qubits used as the control register and the last qubit taken as the target register. For the auxiliary system , we keep it as a single qubit. Then, we take the initial state as , with the subsystem described by

where are complex amplitudes, kl {0, 1},  = {±} and is an arbitrary axis in the Bloch sphere. Here we have written the target qubit in the basis , leaving the remaining qubits of in the computational basis. For simplicity, we will write the states in its decimal representation, i.e.

where N = 2n. Then, we let the system evolve driven by the Hamiltonian

We note that the rotation of the target qubit is expected to be applied if the state of the control system is . Then, if the Hamiltonian is sufficiently slowly-varying so that we can apply the adiabatic theorem, the system will achieve the final state

where is defined as (ξ  {0, ϕ}). An equivalent form of writing Eq. (10) is

with

Thus, by performing a measurement over the auxiliary qubit, we find the rotated state with probability . As in the previous case of a rotation controlled by one qubit, this probability can be enhanced to one in the limit θ0 → π. Indeed, this state implies in a rotation of the target qubit in conditioned by the state of the control register in . An application of this scheme is the adiabatic implementation of the Toffoli gate, which constitutes an unitary operation implementing an X gate over the target qubit if all control qubits are in the state 1, with no effect if any qubit of the control register is in the state 0. This can be easily achieved here by a rotation of an angle π around of the direction x, therefore choosing ϕ = π and , with denoting the eigenstates of σx.

### Shortcut to adiabaticity via counter-diabatic driving

Let us introduce now a shortcut to general CAE through the superadiabatic approach. This will allow for fast piecewise implementation of quantum gates, whose evolution time will not be constrained by the adiabatic theorem. We begin by defining the evolution operator30

which leads an initial state into an evolved state given by

where are the eigenvectors of the adiabatic Hamiltonian. Note that this evolution mimics the adiabatic behavior. The Hamiltonian that guides the evolution of the system is the superadiabatic Hamiltonian, which reads

where the additional term HCD(t) is the counter-diabatic Hamiltonian

Therefore, a superadiabatic implementation of a dynamical evolution involves the knowledge of the eigenstates of the adiabatic Hamiltonian H(t), which limits the direct application of the superadiabatic approach in quantum computation. For instance, by adopting the original AQC approach14, superadiabatic implementations seem prohibitive, since the whole set of eigenlevels of a many-body Hamiltonian is required. In a similar context, counter-diabatic driving protocols based on realizable settings have been investigated for assisted evolutions in quantum critical phenomena40,41,42. Here, as we shall see, the superadiabatic implementation of universal quantum circuits in the hybrid approach can be promptly achieved, since we deal with the eigenspectrum of piecewise Hamiltonians, which act over a few qubits. It is then appealing to formulate a superadiabatic theory to CAE and then to specify it to the implementation of universal sets of quantum gates. Let us begin by establishing the complete set of eigenstates of the Hamiltonian H(t) provided by Eq. (2). To this end, consider the eigenvalue equation to each Hamiltonian Hk(t) given by

with . By defining the projectors Pk in Eq. (2) as , with and , we can write the complete set of eigenstates of H(t) as

such that . Indeed, from Eq. (2), we have . Note that each projector Pk is associated with a Hamiltonian Hk. For instance, for the adiabatic implementation of n-controlled gates, we have defined the Hamiltonian H in Eq. (9) by linking the set with H0 and by linking the remaining projector with Hϕ. The next step is to obtain the counter-diabatic Hamiltonian HCD(t) that implements the shortcut to the adiabatic evolution of H(t). In this direction, we use the eigenstates of H(t) as given by Eq. (18). Then, we get

with . Therefore

where and is the counter-diabatic Hamiltonian to be associated with the piecewise adiabatic contribution Hl(t) acting over subsystem , which reads

Hence, from Eq. (15), we can implement the shortcut dynamics through the superadiabatic Hamiltonian

where is the piecewise superadiabatic Hamiltonian. Note that the cost of performing superadiabatic evolutions requires the knowledge of the eigenvalues and eigenstates of Hl(t). For the implementation of general n-controlled gates, this is a Hamiltonian acting over a single qubit, which is independent of the circuit complexity. Moreover, we can show that, for an arbitrary n-controlled quantum gate, the counter-diabatic Hamiltonians (ξ  {0, ϕ}) associated with shortcuts to adiabatic evolutions driven by , with s = t/τ, are time-independent operators given by

Eq. (23) shows that the implementation of the shortcut can be achieved with a very simple assistant Hamiltonian in the quantum dynamics. Its proof is provided in Section Methods.

### Quantum speed limit

It is expected that the shortcut via a counter-diabatic Hamiltonian is faster than the evolution via an adiabatic Hamiltonian, but how much faster can it be? To answer this question, we shall take a lower bound to the time evolution in quantum dynamics as provided by the quantum speed limit (QSL). We will consider a closed quantum system evolving between arbitrary pure states and . Since the evolution is driven by a time-dependent superadiabatic Hamiltonian HSA(t), we will take the generalized Margolus-Levitin bound33 derived by Deffner and Lutz35, which reads

where is the Bures metric for pure states26 and

For superadiabatic evolutions, the initial state evolves to , where denotes the instantaneous ground state of the adiabatic Hamiltonian H(t). By using the parametrized time s = t/τ, we can show from Eqs. (24) and (25) that the total time τ that mimics the adiabatic evolution within the superadiabatic approach can be reduced to an arbitrary small value. More specifically, the addition of a counter-diabatic Hamiltonian implies into the QSL bound

with η > 0 and , as shown in Section Methods. Therefore, the QSL bound reduces to

with τ and ω defined by the superadiabatic Hamiltonian HSA(t). This means that the superadiabatic implementation is compatible with an arbitrary reduction of the total time τ, which holds independently of the boundary states and . Naturally, a higher energetic cost is expected to be involved for a smaller evolution time τ. In particular, saturation of Eq. (27) is achieved for either τ → 0 or ω → 0, with both cases implying in τω → 0. Note that this limit is forbidden in the adiabatic regime for finite ω, since the energy gap is proportional to ħω, which implies in an adiabatic time of the order τad 1/ωn, with 3,4,7,36. Hence, Eq. (27) leads to a flexible running time in a superadiabatic implementation, only limited by the energy-time complementarity.

### The Energetic Cost

Let us show now that time and energy are complementary resources in superadiabatic implementations of quantum evolutions. We shall define the energetic cost associated with a superadiabatic Hamiltonian through

with HSA(t) given by Eq. (22) and the norm provided by the Hilbert-Schmidt norm . Since HSA(t) is Hermitian, we can write

To derive Eq. (29), we have used that Tr({H(t), HCD(t)}) = 0. This can be obtained by computing the trace in the eigenbasis of H(t) and noticing that the expectation value of HCD(t) taken in an eigenstate of H(t) vanishes, i.e. . In particular, let us define the energetic cost to the adiabatic Hamiltonian as

Then, it follows that the energetic cost Σ(τ) in superadiabatic evolutions supersedes the energetic cost Σ0(τ) for a corresponding adiabatic physical process. In order to evaluate Σ(τ) we adopt the basis of eigenstates of the adiabatic Hamiltonian H(t). By using Eq. (18), this yields

where are the energies of the adiabatic Hamiltonian Hl(t) and

In order to analyze the energetic cost as provided by Eq. (30) for superadiabatic qubit rotation gates, we set and (l). Moreover, by using Eq. (18), we obtain , which leads to [See Eqs. (34) and (35) in Section Methods]. Hence

We illustrate the behavior of Σ(τ) in Fig. 1, where it is apparent that the energetic cost increases inversely proportional to the total time of evolution. In particular, note also that, for a fixed energetic cost, the optimal choice θ0 → π requires a longer evolution. This is because of the fact that, in this case, the final state associated with the auxiliary qubit is orthogonal to its initial state, so it is farther in the Bloch sphere. In the more general case of controlled gates, the analysis is similar as in the case of single-qubit gates. However, we must take into account the number of projectors composing the set {Pk}. More specifically, the sum over l in Eq. (30) shall run over 1 to 4, which is the number of projectors over the subsystem . Thus we can show that energetic cost ΣCG to implement controlled gates is .

## Methods

### Time-independent counter-diabatic Hamiltonians for n-controlled gates

Let us explicitly design here the superadiabatic implementation of controlled evolutions for piecewise Hamiltonians Hξ(s) as provided by Eqs. (5). To this end, consider the eigenvalue equation

with ξ = {0, ϕ}, where

From Eq. (18), it follows that the eigenstates for the adiabatic Hamiltonian H(s) governing the composite system are given by the sets and associated with the set of eigenvalues and , respectively. By evaluating the eigenvalues of H0(s) and Hϕ(s), we obtain that their spectra are equal, being provided by . Thus, H(s) exhibits doubly degenerate levels, with and associated with levels E+ = ωħ and E = −ωħ, respectively. By using now Eqs. (34) and (35), we obtain , for any i = {±} and ξ = {0,  ϕ}. Then, from Eq. (20), we obtain that the counter-diabatic hamiltonian is , which leads to the time-independent counter-diabatic Hamiltonian given by Eq. (23). The extension to the case of n-controlled gates can be achieved as follows. From Eq. (18), the eigenstates of H(s) read

where , ε, k = {±} and ξ = {0, ϕ}. By computing the eigenvalues of H(s), we obtain that the spectrum of H(s) is (2N)-degenerate, with and associated with the levels E+ = ωħ and E = −ωħ, respectively. By using these results into Eq. (20), we obtain that the counter-diabatic piecewise Hamiltonian is given by Eq. (23). Hence, the implementation any n-controlled gate is achieved through a time-independent counter-diabatic Hamiltonian.

### Quantum speed limit for superadiabatic evolutions

Let us apply here the QSL bound to superadiabatic evolutions. By using the fact than the evolves in the ground state of H(t) and that HSA(t) is given by Eq. (15), we have

where E0(t) is the instantaneous ground state energy of H(t). Now we use Eq. (16) and the inequality , which yields

By using the parametrized time s = t/τ, we obtain

where and . Since the ground state energy for the adiabatic Hamiltonian H(s) in the case of n-controlled gates is E0(s) = −ωħ [see Eqs. (5) and (9)], we write η1(s) = ωη(s), with . Moreover, we define χ(s) ≡ η2(s) + η3(s). Then

Let us now analise the term χ(s). First, note that . Then, we use that (see proof in ref. 35), which yields

where we have used the inequality . From the definition of the Bures metric, we have . Hence, , which implies into Eq. (27).