Feedback-based quantum algorithms have recently emerged as potential methods for approximating the ground states of Hamiltonians . One such algorithm, the feedback-based algorithm for quantum optimization (FALQON), is specifically designed to solve quadratic unconstrained binary optimization problems. Its extension, the feedback-based algorithm for quantum optimization with constraints (FALQON-C), was introduced to handle constrained optimization problems with equality and inequality constraints . In this work, we extend the feedback-based quantum algorithms framework to address a broader class of constraints known as invalid configuration (IC) constraints, which explicitly prohibit specific configurations of decision variables. We first present a transformation technique that converts the constrained optimization problem with invalid configuration constraints into an equivalent unconstrained problem by incorporating a penalizing term into the cost function. Then, leaning upon control theory, we propose an alternative method tailored for feedback-based quantum algorithms that directly tackles IC constraints without requiring slack variables. Our approach introduces a new operator that encodes the optimal feasible solution of the constrained optimization problem as its ground state. Then, a controlled quantum system based on the Lyapunov control technique is designed to ensure convergence to the ground state of this operator. Two approaches are introduced in the design of this operator to address IC constraints: the folded spectrum approach and the deflation approach. These methods eliminate the need for slack variables, significantly reducing the quantum circuit depth and the number of qubits required. We show the effectiveness of our proposed algorithms through numerical simulations.
Feedback-based quantum algorithms have recently emerged as potential methods for approximating the ground states of Hamiltonians . One such algorithm, the feedback-based algorithm for quantum optimization (FALQON), is specifically designed to solve quadratic unconstrained binary optimization problems. Its extension, the feedback-based algorithm for quantum optimization with constraints (FALQON-C), was introduced to handle constrained optimization problems with equality and inequality constraints . In this work, we extend the feedback-based quantum algorithms framework to address a broader class of constraints known as invalid configuration (IC) constraints, which explicitly prohibit specific configurations of decision variables. We first present a transformation technique that converts the constrained optimization problem with invalid configuration constraints into an equivalent unconstrained problem by incorporating a penalizing term into the cost function. Then, leaning upon control theory, we propose an alternative method tailored for feedback-based quantum algorithms that directly tackles IC constraints without requiring slack variables. Our approach introduces a new operator that encodes the optimal feasible solution of the constrained optimization problem as its ground state. Then, a controlled quantum system based on the Lyapunov control technique is designed to ensure convergence to the ground state of this operator. Two approaches are introduced in the design of this operator to address IC constraints: the folded spectrum approach and the deflation approach. These methods eliminate the need for slack variables, significantly reducing the quantum circuit depth and the number of qubits required. We show the effectiveness of our proposed algorithms through numerical simulations.
This thesis develops a control-theoretic framework for the design and analysis of quantum optimization algorithms.The central idea is to model quantum algorithms as controlled dynamical systems in which quantum gates act as control inputs that steer the system's state toward the ground or excited states of a target Hamiltonian.Within this framework, the thesis advances the family of feedback-based quantum algorithms (FQAs), building on Lyapunov-inspired feedback principles introduced in prior work, and develops new algorithmic extensions for constrained optimization and excited state preparation.This work is motivated by the potential of FQAs to tackle complex optimization problems, such as preparing ground states of Hamiltonians, which underpin a wide range of applications in engineering, chemistry, and combinatorial optimization.This work enhances the performance of FQAs and extends their applicability to a broader class of quantum optimization problems.It consists of three main parts.The first part focuses on enhancing the performance and robustness of FQAs.It introduces an adaptive sampling noise mitigation technique that incorporates a new control law into the algorithm design to improve robustness and convergence with limited measurements.The second part extends the feedback-based framework to excited state preparation, yielding two new algorithms: the feedback-based quantum algorithm for excited states preparation (FQAE) and the weighted feedback-based quantum algorithm for excited states preparation (WFQAE), with applications in quantum chemistry and many-body physics.The third part generalizes the feedback-based framework to tackle more complex combinatorial optimization problems, specifically those with inequality and invalid configuration constraints, by proposing the feedback-based algorithm for quantum optimization with constraints (FALQON-C) and the feedback-based algorithm for quantum optimization with invalid configuration constraints (FALQON-IC).The design of these algorithms leverages control theory to reduce circuit depth while maintaining convergence efficiency within the feedback framework.The thesis demonstrates that control-theoretic tools such as Lyapunov stability analysis and feedback design provide a systematic foundation for developing quantum algorithms with improved robustness, generalization, and scalability.The results offer new insights into quantum optimization and highlight the role of control theory as a guiding principle for the development of efficient feedback-based quantum algorithms.iii ResuméDenne afhandling handler om, hvordan man kan bruge kontrolteori til at udvikle og analysere kvantealgoritmer til optimering.Hovedideen er at betragte kvantealgoritmer som dynamiske systemer, der kan styres, hvor kvanteoperatorer fungerer som styringssignaler, der gradvist bringer systemet hen mod en ønsket tilstand, for eksempel grundtilstanden af en given Hamilton-operator.Inden for denne ramme videreudvikler afhandlingen såkaldte feedback-baserede kvantealgoritmer (FQAs).Disse algoritmer bruger løbende målinger og feedback til at justere sig selv, inspireret af klassiske principper fra kontrolteori.Afhandlingen bygger videre på tidligere arbejde og introducerer nye metoder, som gør det muligt både at løse optimeringsproblemer med begrænsninger og at beregne exciterede tilstande.Motivation for arbejdet er, at denne type algoritmer har potentiale til at løse svære optimeringsproblemer, som f.eks.at finde grundtilstande af Hamiltonoperatorer.Sådanne problemer er centrale i mange anvendelser inden for ingeniørvidenskab, kemi og kombinatorisk optimering.Afhandlingen forbedrer både ydeevnen og anvendeligheden af feedback-baserede kvantealgoritmer og er opdelt i tre hoveddele.Den første del fokuserer på at gøre algoritmerne mere robuste og mindre følsomme over for støj i målinger, blandt andet ved at indføre en adaptiv metode, som fungerer bedre, når antallet af målinger er begrænset.Den anden del udvider metoden til også at kunne beregne exciterede tilstande.Dette fører til to nye algoritmer, som kan anvendes inden for blandt andet kvantekemi og mangelegeme-fysik.Den tredje del viser, hvordan den feedback-baserede tilgang kan bruges til mere komplekse optimeringsproblemer, hvor der er begrænsninger eller ugyldige løsninger.Her introduceres nye algoritmer, der er designet til at håndtere sådanne problemer effektivt, samtidig med at kvantekredsløbene holdes så simple som muligt.Samlet set viser afhandlingen, at værktøjer fra kontrolteori, som stabilitetsanalyse og feedback-design, giver et solidt og systematisk grundlag for at udvikle mere robuste, fleksible og skalerbare kvantealgoritmer.Resultaterne giver ny indsigt i kvanteoptimering og understreger kontrolteoriens vigtige rolle i udviklingen af effektive feedback-baserede kvantealgoritmer.
Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this problem, a usual strategy is to map the assembly to a Quadratic Unconstrained Binary Optimization (QUBO) formulation, which can be solved by different quantum algorithms. In this work, we focus on three versions of the Feedback-based Algorithm, a protocol that eliminates classical optimization loops via measurement feedback. We analyze long-read DNA fragments from SARS-CoV-2 and human mitochondrial DNA using standard FALQON, second-order FALQON (SO-FALQON), and time-rescaled FALQON (TR-FALQON). Numerical results show that both variants improve convergence to the ground state and increase success probabilities at reduced circuit depths. These findings indicate that enhanced feedback-driven dynamics are effective for solving combinatorial problems on near-term quantum hardware.
Quantum optimization, for both classical and quantum functions, is one of the most well-studied applications of quantum computing, but recent trends have relied on hybrid methods that push much of the fine-tuning off onto costly classical algorithms. Feedback-based quantum algorithms, such as FALQON, avoid these fine-tuning problems but at the cost of additional circuit depth and a lack of convergence guarantees. In this work we take the local greedy information collected by Lyapunov feedback control and develop an analytic framework to use it to perturbatively update previous control layers, similar to the global optimal control achievable using Pontryagin optimal control. This perturbative methodology, which we call feedback-based optimally controlled quantum states (FOCQS), can be used to improve the results of feedback-based algorithms, like FALQON, providing better convergence but still no formal guarantees of convergence. Furthermore, this perturbative method can be used to push smooth annealing-like control protocol closer to the control optimum, even providing an iterative approach, albeit with diminishing returns. In numerical testing, we show improvements in convergence and required depth due to these methods over existing quantum feedback control methods.
Quantum optimization, both for classical and quantum functions, is one of the most well-studied applications of quantum computing, but recent trends have relied on hybrid methods that push much of the fine-tuning off onto costly classical algorithms. Feedback-based quantum algorithms, such as FALQON, avoid these fine-tuning problems but at the cost of additional circuit depth and a lack of convergence guarantees. In this work, we take the local greedy information collected by Lyapunov feedback control and develop an analytic framework to use it to perturbatively update previous control layers, similar to the global optimal control achievable using Pontryagin optimal control. This perturbative methodology, which we call Feedback Optimally Controlled Quantum States (FOCQS), can be used to improve the results of feedback-based algorithms, like FALQON. Furthermore, this perturbative method can be used to push smooth annealing-like control protocol closer to the control optimum, even providing and iterative approach, albeit with diminishing returns. In numerical testing, we show improvements in convergence and required depth due to these methods over existing quantum feedback control methods.
In contrast to the classical optimization process required by the quantum approximate optimization algorithm, FALQON, a feedback-based algorithm for quantum optimization [A. B. Magann et al., Phys. Rev. Lett. 129, 250502 (2022)], enables one to obtain approximate solutions to combinatorial optimization problems without any classical optimization effort. In this study, we leverage the specifications of a recent experimental platform for the neutral-atom system [Z. Fu et al., Phys. Rev. A 105, 042430 (2022)] and present a scheme to implement an optimally tuned small-angle controlled-phase gate. By examining the two- to four-qubit FALQON algorithms in the MaxCut problem and considering the spontaneous emission of the neutral atomic system, we observe that the performance of FALQON implemented with small-angle controlled-phase gates exceeds that of FALQON utilizing controlled-Z gates. This approach has the potential to significantly simplify the logic circuit required to simulate FALQON and effectively address the MaxCut problem, which may pave the way for the experimental implementation of near-term noisy intermediate-scale quantum algorithms with neutral-atom systems.
Abstract Solving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have inequality constraints that require a particular cost function encoding. The common approach is the use of slack variables to represent the inequality constraints in the cost function. However, the use of slack variables considerably increases the number of qubits and operations required to solve these problems using quantum devices. In this work, we present an alternative method that does not require extra slack variables and consists of using an unbalanced penalization function to represent the inequality constraints in the QUBO. This function is characterized by larger penalization when the inequality constraint is not achieved than when it is. We evaluate our approach on the TSP, BPP, and KP, successfully encoding the optimal solution of the original optimization problem near the ground state cost Hamiltonian. Additionally, we employ D -Wave Advantage and D -Wave hybrid solvers to solve the BPP, surpassing the performance of the slack variables approach by achieving solutions for up to 29 items, whereas the slack variables approach only handles up to 11 items. This new approach can be used to solve combinatorial problems with inequality constraints with a reduced number of resources compared to the slack variables approach using quantum annealing or variational quantum algorithms.