arXiv subjects

Diogo Gomes

Publications and source records attributed to Diogo Gomes.

At least 19 recordsLinked to original sources

arXiv2026

Error Mitigation in Bosonic Systems via Virtual Distillation

Virtual distillation is a promising error-mitigation technique that exploits multiple copies of a noisy quantum state to estimate observables as if measured on a purified state. Although originally introduced in the context of bosonic many-body systems under the name of virtual cooling, its development and applications have largely focused on qubit-based quantum computation. Here, we establish a framework for virtual distillation in bosonic quantum information processing and continuous-variable quantum computing. Building on a diagonalization of cyclic shift operators implemented with passive linear-optical interferometers, we derive experimentally accessible protocols for estimating virtually distilled expectation values of observables relevant to bosonic architectures. In particular, we show how to recover noise-mitigated expectation values of number operators, phase-shift operators, and arbitrary quadratures from multi-copy measurements. For number operators, we further demonstrate the estimation of virtually distilled correlators of arbitrary order through the characteristic function of the photon-number distribution. We apply the framework to states affected by photon loss and dephasing, two of the dominant noise mechanisms in bosonic quantum computation, and quantify the resulting suppression of noise contributions. Our results extend virtual distillation beyond its original setting and provide a practical route toward error-mitigated measurements in bosonic quantum processors using experimentally available linear-optical resources.

quant-ph↗

arXiv2026

Existence and Structure for First-Order Time-Dependent Mean-Field Games with Local Couplings

We develop a Banach-space framework for first-order time-dependent mean-field games with local couplings, using monotone operator theory and low-order $p$-Laplacian regularization to avoid high-order elliptic smoothing. Under monotonicity and power-growth assumptions, together with either a Lagrangian lower bound or strict positivity of the initial density, we prove existence of weak variational-inequality solutions by Minty's method. The constructed solutions satisfy uniform $L^\beta$ estimates on the density, $L^\alpha$ estimates on the spatial gradient of the value function, and space-time shift estimates sufficient to identify the limiting PDE system. We prove that any variational-inequality solution satisfying these bounds, regardless of how it is obtained, is a MFG solution satisfying the Hamilton--Jacobi and transport equations in the $BV$ sense. This separates the construction of VI-solutions from the verification of the PDE system, a feature not directly available in the existing stationary Banach-space framework. Finally, for each fixed density $m$, we establish a maximal value function among Hamilton--Jacobi subsolutions; every MFG value function coincides with this maximal representative on $\{m>0\}$ and initially on $\{m_0>0\}$. Under semi-strict monotonicity, the density $m$ itself is unique. Our results apply to non-separable Hamiltonians with power growth and impose no dimension restrictions.

math.AP↗

arXiv2026

Bregman-projected mirror methods for regularized stationary mean-field games

We develop and analyze a Bregman-projected mirror iteration for low-order regularizations of stationary mean-field game (MFG) systems in their natural Banach space setting. For separable Hamiltonians of the form $H(x,p,m)=H_0(x,p)-g(m)$, with quadratic or super-quadratic Hamiltonian growth and linear or super-linear density couplings, we formulate a low-order $\bar\gamma$-Laplacian regularization of the stationary MFG system as a variational inequality on $L^{\bar\beta}(\mathbb T^d)\times W^{1,\bar\gamma}(\mathbb T^d)$. To approximate solutions of this regularized variational inequality, we introduce a Bregman geometry matched to the mixed Lebesgue--Sobolev exponents of the problem and analyze a constrained two-step mirror method with frozen operator evaluation. For the exact constrained iteration and each fixed regularization parameter $\epsi>0$, we derive a one-step Bregman inequality and use it to prove that the constrained iteration converges strongly to the unique solution of the regularized variational inequality under natural summability conditions on the step sizes. Numerical experiments on one- and two-dimensional models, validated against exact test solutions, illustrate residual decay under mesh refinement and suggest improved practical performance of the two-step implementation in the tested discretizations.

math.NA↗

arXiv2026

Weak-Strong Uniqueness for Second-Order Mean-Field Games

We extend the weak-strong uniqueness principle for mean-field game (MFG) systems to a broad class of second-order stationary and time-dependent problems. Under standard monotonicity, growth, and coercivity assumptions on the Hamiltonian, and relying strictly on the integrability exponents guaranteed by the existing theory for monotone MFG systems, we show that any weak solution must coincide with a given strong solution. Our analysis covers models with spatially dependent scalar diffusion coefficients, using monotonicity arguments and a coefficient-adapted mollification strategy to manage the variable diffusion terms. We extend this strategy to establish weak-strong uniqueness in the corresponding second-order, initial-terminal, time-dependent setting. Finally, to address the critical quadratic growth regime, we derive a new a priori second-order estimate for a stationary MFG system with logarithmic coupling. This estimate provides quantitative bounds on the solution in terms of the data, and yields weak-strong uniqueness in the range where the improved integrability yields $L^2$ control of the density. Since numerical and approximation methods for MFGs naturally yield weak solutions in the monotonicity sense, whereas strong solutions are known to exist in many settings, our results identify any weak limit produced by such methods with the strong solution whenever one exists.

math.AP↗

arXiv2026

Ranking Mean-Field Planning Games

This paper studies a one-dimensional Mean-Field Planning (MFP) system with a non-local, rank-based coupling. Using a potential formulation, we rewrite the system as an associated scalar partial differential equation. We prove an equivalence between classical solutions to the ranking MFP system with positive density and classical solutions to the associated potential problem, and we derive explicit reconstruction formulas. We then identify a monotonicity structure in the associated operator, which, under strict convexity assumptions, yields uniqueness of classical solutions to the associated problem and, hence, uniqueness of the ranking MFP system up to an additive constant in the value function. Finally, under superlinear growth assumptions, we exploit monotonicity to address existence in a low-regularity setting. By formulating a variational inequality for a q-Laplacian regularized operator, we apply Minty's method to establish the existence of weak solutions in the space of functions of bounded variation for a relaxed potential formulation.

math.AP↗

arXiv2026

A Monotone Operator Approach to Separable Mean-Field Games with Mixed Boundary Conditions

We study a class of local, first-order, stationary mean-field games (MFGs) on bounded domains with nonstandard mixed boundary conditions: prescribed inflow on $\Gamma_N$ and a relaxed Signorini-type exit condition on $\Gamma_D$ (complementarity between exit flux and boundary value). For separable Hamiltonians, we overcome the lack of coercivity and the boundary complementarity constraints by introducing a monotone operator on a convex domain, augmented with an auxiliary nonnegative boundary variable $h$ encoding exit flux. To address a constant-shift degeneracy in the value function $u$ (the transport equation depends only on $Du$), we employ a quotient-space formulation that restores coercivity. Using the Browder--Minty theorem, we prove existence for a penalized operator $A_\epsilon$ on a convex domain and pass to the limit as $ \epsilon \to 0^+$. We obtain weak solutions $(m,u,h)$ solving the associated variational inequality, with $m \in L^{\beta+1}(\Omega)$, $u \in W^{1,\gamma}(\Omega)$, and $h$ in the dual trace space on $\Gamma_D$.

math.AP↗

arXiv2025

Solving Mean-Field Games with Monotonicity Methods in Banach Spaces

This paper develops a unified framework for proving the existence of solutions to stationary first-order mean-field games (MFGs) based on the theory of monotone operators in Banach spaces. We cast the coupled MFG system as a variational inequality, overcoming the limitations of prior Hilbert-space approaches that relied on high-order regularization and typically yielded only weak solutions in the monotone operator sense. In contrast, with our low-order regularization, we obtain strong solutions. Our approach addresses the non-coercivity of the underlying MFG operator through two key regularization strategies. First, by adding a low-order $p$-Laplacian term, we restore coercivity, derive uniform a priori estimates, and pass to the limit via Minty's method. This establishes, for the first time via monotonicity methods, the existence of strong solutions for models with both standard power-growth and singular congestion, with the latter requiring a careful restriction of the operator's domain. Second, for Hamiltonians with only minimal growth hypotheses, we regularize the Hamiltonian itself via infimal convolution to prove the existence of weak solutions. Our Banach-space framework unifies and extends earlier existence results. By avoiding high-order smoothing, it not only provides a more direct theoretical path but is also ideally suited for modern numerical algorithms.

math.AP↗

arXiv2025

Regularity for Weak Solutions to First-Order Local Mean Field Games

We establish interior regularity results for first-order, stationary, local mean-field game (MFG) systems. Specifically, we study solutions of the coupled system consisting of a Hamilton-Jacobi-Bellman equation $H(x, Du, m) = 0$ and a transport equation $-\operatorname{div}(m D_pH(x, Du, m)) = 0$ in a domain $Ω\subset \mathbb{R}^d$. Under suitable structural assumptions on the Hamiltonian $H$, without requiring monotonicity of the system, convexity of the Hamiltonian, separability in variables, or smoothness beyond basic continuity in $(p,m)$, we introduce a notion of weak solutions that allows the application of techniques from elliptic regularity theory. Our main contribution is to prove that the value function $u$ is locally Hölder continuous in $Ω$. The proof leverages the connection between first-order MFG systems and quasilinear equations in divergence form, adapting classical techniques to handle the specific structure of MFG systems.

math.AP↗

arXiv2025

Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium

In this paper, we address the problem of optimizing flows on generalized graphs that feature multiple entry points and multiple populations, each with varying cost structures. We tackle this problem by considering the multi-population Wardrop equilibrium, defined through variational inequalities. We rigorously analyze the existence and uniqueness of the Wardrop equilibrium. Furthermore, we introduce an efficient numerical method to find the solution. In particular, we reformulate the equilibrium problem as a distributed optimization problem over subgraphs and introduce a novel Hessian Riemannian flow method, a Riemannian-manifold-projected Hessian flow, to efficiently compute a solution. Finally, we demonstrate the effectiveness of our approach through examples in urban traffic management, including routing for diverse vehicle types and strategies for minimizing emissions in congested environments.

eess.SY↗

arXiv2025

Weak-strong uniqueness for solutions to mean-field games

This paper addresses the crucial question of solution uniqueness in stationary first-order Mean-Field Games (MFGs). Despite well-established existence results, establishing uniqueness, particularly for weaker solutions in the sense of monotone operators, remains an open challenge. Building upon the framework of monotonicity methods, we introduce a linearization method that enables us to prove a weak-strong uniqueness result for stationary MFG systems on the d-dimensional torus. In particular, we give explicit conditions under which this uniqueness holds.

math.AP↗

arXiv2025

An introduction to monotonicity methods in mean-field games

This chapter examines monotonicity techniques in the theory of mean-field games(MFGs). Originally, monotonicity ideas were used to establish the uniqueness of solutions for MFGs. Later, monotonicity methods and monotone operators were further exploited to build numerical methods and to construct weak solutions under mild assumptions. Here, after a brief discussion on the mean-field game formulation, we introduce the Minty method and regularization strategies for PDEs. These are then used to address typical stationary and time-dependent monotone MFGs and to establish the existence of weak solutions for such MFGs.

math.AP↗

arXiv2025

A Fully-discrete Semi-Lagrangian scheme for a price formation MFG model

Here, we examine a fully-discrete Semi-Lagrangian scheme for a mean-field game price formation model. We show the existence of the solution of the discretized problem and that it is monotone as a multivalued operator. Moreover, we show that the limit of the discretization converges to the weak solution of the continuous price formation mean-field game using monotonicity methods. Numerical simulations demonstrate that this scheme can provide results efficiently, comparing favorably with other methods in the examples we tested.

math.NA↗

arXiv2024

Inference of Causal Networks using a Topological Threshold

We propose a constraint-based algorithm, which automatically determines causal relevance thresholds, to infer causal networks from data. We call these topological thresholds. We present two methods for determining the threshold: the first seeks a set of edges that leaves no disconnected nodes in the network; the second seeks a causal large connected component in the data. We tested these methods both for discrete synthetic and real data, and compared the results with those obtained for the PC algorithm, which we took as the benchmark. We show that this novel algorithm is generally faster and more accurate than the PC algorithm. The algorithm for determining the thresholds requires choosing a measure of causality. We tested our methods for Fisher Correlations, commonly used in PC algorithm (for instance in \cite{kalisch2005}), and further proposed a discrete and asymmetric measure of causality, that we called Net Influence, which provided very good results when inferring causal networks from discrete data. This metric allows for inferring directionality of the edges in the process of applying the thresholds, speeding up the inference of causal DAGs.

stat.ML↗

arXiv2023

Machine Learning architectures for price formation models with common noise

We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational agents subject to a market clearing condition imposed by random supply, which presents additional challenges compared to the deterministic counterpart. Our approach uses a dual recurrent neural network architecture encoding noise dependence and a particle approximation of the mean-field model with a single loss function optimized by adversarial training. We provide a posteriori estimates for convergence and illustrate our method through numerical experiments.

math.OC↗

arXiv2023

A First-Order Mean-Field Game on a Bounded Domain with Mixed Boundary Conditions

Entry-exit dynamics are crucial in modeling crowd movement. Here, we present a novel first-order, stationary mean-field game model on a bounded domain that accurately captures these dynamics. The interior dynamics of the system are governed by a standard first-order stationary MFG system consisting of a Hamilton-Jacobi equation coupled with a transport equation. The model incorporates nonstandard mixed boundary conditions corresponding to an entry region $\Gamma_N$, where a Neumann condition prescribes agent inflow, and an exit region $\Gamma_D$, where a no-entry condition prevents inward flow. Additionally, we impose an upper bound on the exit cost through $\Gamma_D$, combined with a complementary contact-set condition. The contact-set condition distinguishes boundary contact points, where the exit cost is attained and exit is permitted, from non-contact points, where a strict no-penetration condition is enforced. This mixed approach overcomes the limitations of classical Dirichlet conditions, which can artificially force boundary points to serve as both entry and exit locations. We analyze the system through a variational formulation, applying the direct method of the calculus of variations to establish the existence of solutions under minimal regularity assumptions. Furthermore, we prove a partial uniqueness result for the gradient of the value function (particularly in regions with positive agent density) and establish the uniqueness of the density function. Several examples, including one- and two-dimensional cases, illustrate the proper assignment of entry and exit roles and demonstrate that contact does not necessarily enforce exit. Additionally, they showcase first-order MFG phenomena, such as the formation of empty regions, where agent density vanishes. These results provide a rigorous mathematical foundation for modeling realistic entry-exit scenarios.

math.AP↗

arXiv2023

Machine Learning architectures for price formation models

Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.

math.OC↗

arXiv2022

A random-supply Mean Field Game price model

We consider a market where a finite number of players trade an asset whose supply is a stochastic process. The price formation problem consists of finding a price process that ensures that when agents act optimally to minimize their trading costs, the market clears, and supply meets demand. This problem arises in market economies, including electricity generation from renewable sources in smart grids. Our model includes noise on the supply side, which is counterbalanced on the consumption side by storing energy or reducing the demand according to a dynamic price process. By solving a constrained minimization problem, we prove that the Lagrange multiplier corresponding to the market-clearing condition defines the solution of the price formation problem. For the linear-quadratic structure, we characterize the price process of a continuum population using optimal control techniques. We include numerical schemes for the price computation in the finite and infinite games, and we illustrate the model using real data.

math.AP↗

arXiv2022

A Variational Approach For Price Formation Models In One Dimension

In this paper, we study a class of first-order mean-field games (MFGs) that model price formation. Using Poincar{é} Lemma, we eliminate one of the equations and obtain a variational problem for a single function. This variational problem offers an alternative approach for the numerical solution of the original MFGs system. We show a correspondence between solutions of the MFGs system and the variational problem. Moreover, we address the existence of solutions for the variational problem using the direct method in the calculus of variations. We end the paper with numerical results for a linear-quadratic model.

math.AP↗