arXiv subjects

Adi Makmal

Publications and source records attributed to Adi Makmal.

16 recordsLinked to original sources

arXiv2026

Measurements Number Scaling in the Quantum Approximate Optimization Algorithm for MaxCut: A Statistical Analysis

We provide a statistical analysis of the measurement (shot) requirements of the quantum approximate optimization algorithm (QAOA) for the MaxCut problem. We derive sufficient conditions on the number of shots per cost operator evaluation to: (a) estimate the expected cost to within a relative error $\delta$ and a confidence $1-\epsilon$, and (b) ensure SGD-based parameter optimization converges to a target relative suboptimality level with high probability. In addition, we provide an explicit bound on the number of SGD iterations required to reach the target accuracy. Our analysis reveals an unexpected scaling phenomenon: for specific graph classes, which we formally characterize, the total shot budget needed to achieve a fixed relative-performance metric decreases as the instance size grows. This result complements earlier cost function concentration arguments regarding parameter optimization redundancy, thereby highlighting the potential for high-performance, low-overhead QAOA implementations for large-scale MaxCut instances. To assist practitioners, we translate our analytical findings into practical rules of thumb for shot-budget allocation and validate these results with numerical simulations, offering new insights into the interplay between graph size, structural complexity, and resource requirements in QAOA.

quant-ph↗

arXiv2024

Molecular groundstate determination via short pulses on superconducting qubits

Quantum computing is currently hindered by hardware noise. We present a freestyle superconducting pulse optimization method, incorporating two-qubit channels, which enhances flexibility, execution speed, and noise resilience. A minimal 0.22 ns pulse is shown to determine the H2 groundstate to within chemical accuracy upon real-hardware, approaching the quantum speed limit. Similarly, a pulse significantly shorter than circuit-based counterparts is found for the LiH molecule, attaining state-of-the-art accuracy. The method is general and can potentially accelerate performance across various quantum computing components and hardware.

quant-ph↗

arXiv2023

A Variational Qubit-Efficient MaxCut Heuristic Algorithm

MaxCut is a key NP-Hard combinatorial optimization graph problem with extensive theoretical and industrial applications, including the Ising model and chip design. While quantum computing offers new solutions for such combinatorial challenges which are potentially better than classical schemes, with the Quantum Approximate Optimization Algorithm (QAOA) being a state-of-the-art example, its performance is currently hindered by hardware noise and limited qubit number. Here, we present a new variational Qubit-Efficient MaxCut (QEMC) algorithm that requires a logarithmic number of qubits with respect to the graph size, an exponential reduction compared to QAOA. We demonstrate cutting-edge performance for graph instances consisting of up to 32 nodes (5 qubits) on real superconducting hardware, and for graphs with up to 2048 nodes (11 qubits) using noiseless simulations, outperforming the established classical algorithm of Goemans and Williamson (GW). The QEMC algorithm's innovative encoding scheme empowers it with great noise-resiliency on the one hand, but also enables its efficient classical simulation on the other, thus obscuring a distinct quantum advantage. Nevertheless, even in the absence of quantum advantage, the QEMC algorithm serves as a potential quantum-inspired algorithm, provides a challenging benchmark for QAOA, and presents a novel encoding paradigm with potential applications extending to other quantum and classical algorithms.

quant-ph↗

arXiv2023

A Qubit-Efficient Variational Selected Configuration-Interaction Method

Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that tackles the molecular groundstate problem and is regarded as one of the flagships of quantum computing. Yet, to date, only very small molecules were computed via VQE, due to high noise levels in current quantum devices. Here we present an alternative variational quantum scheme that requires significantly less qubits. The reduction in qubit number allows for shallower circuits to be sufficient, rendering the method more resistant to noise. The proposed algorithm, termed variational quantum selected-configuration-interaction (VQ-SCI), is based on: (a) representing the target groundstate as a superposition of Slater determinant configurations, encoded directly upon the quantum computational basis states; and (b) selecting a-priory only the most dominant configurations. This is demonstrated through a set of groundstate calculations of the H$_2$, LiH, BeH$_2$, H$_2$O, NH$_3$ and C$_2$H$_4$ molecules in the sto-3g basis set, performed on IBM quantum devices. We show that the VQ-SCI reaches the full-CI (FCI) energy within chemical accuracy using the lowest number of qubits reported to date. Moreover, when the SCI matrix is generated ``on the fly", the VQ-SCI requires exponentially less memory than classical SCI methods. This offers a potential remedy to a severe memory bottleneck problem in classical SCI calculations. Finally, the proposed scheme is general and can be straightforwardly applied for finding the groundstate of any Hermitian matrix, outside the chemical context.

quant-ph↗

arXiv2022

Iterative-Free Quantum Approximate Optimization Algorithm Using Neural Networks

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on finding better initial circuit parameters, which would reduce the number of required iterations and hence the overall execution time. While existing methods for parameter initialization have shown great success, they often offer a single set of parameters for all problem instances. We propose a practical method that uses a simple, fully connected neural network that leverages previous executions of QAOA to find better initialization parameters tailored to a new given problem instance. We benchmark state-of-the-art initialization methods for solving the MaxCut problem of Erdős-Rényi graphs using QAOA and show that our method is consistently the fastest to converge while also yielding the best final result. Furthermore, the parameters predicted by the neural network are shown to match very well with the fully optimized parameters, to the extent that no iterative steps are required, thereby effectively realizing an iterative-free QAOA scheme.

quant-ph↗

arXiv2022

Approximate encoding of quantum states using shallow circuits

A common requirement of quantum simulations and algorithms is the preparation of complex states through sequences of 2-qubit gates. For a generic quantum state, the number of gates grows exponentially with the number of qubits, becoming unfeasible on near-term quantum devices. Here, we aim at creating an approximate encoding of the target state using a limited number of gates. As a first step, we consider a quantum state that is efficiently represented classically, such as a one-dimensional matrix product state. Using tensor network techniques, we develop an optimization algorithm that approaches the optimal implementation for a fixed number of gates. Our algorithm runs efficiently on classical computers and requires a polynomial number of iterations only. We demonstrate the feasibility of our approach by comparing optimal and suboptimal circuits on real devices. We, next, consider the implementation of the proposed optimization algorithm directly on a quantum computer and overcome inherent barren plateaus by employing a local cost function rather than a global one. By simulating realistic shot noise, we verify that the number of required measurements scales polynomially with the number of qubits. Our work offers a universal method to prepare target states using local gates and represents a significant improvement over known strategies.

quant-ph↗

arXiv2018

Benchmarking projective simulation in navigation problems

Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory. The model has been shown to provide a flexible framework for constructing reinforcement-learning agents, and it allows for quantum mechanical generalization, which leads to a speed-up in deliberation time. PS agents have been applied successfully in the context of complex skill learning in robotics, and in the design of state-of-the-art quantum experiments. In this paper, we study the performance of projective simulation in two benchmarking problems in navigation, namely the grid world and the mountain car problem. The performance of PS is compared to standard tabular reinforcement learning approaches, Q-learning and SARSA. Our comparison demonstrates that the performance of PS and standard learning approaches are qualitatively and quantitatively similar, while it is much easier to choose optimal model parameters in case of projective simulation, with a reduced computational effort of one to two orders of magnitude. Our results show that the projective simulation model stands out for its simplicity in terms of the number of model parameters, which makes it simple to set up the learning agent in unknown task environments.

cs.LG↗

arXiv2017

Projective simulation with generalization

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

cs.AI↗

arXiv2016

Quantum walks on embedded hypercubes: Nonsymmetric and nonlocal cases

The expected hitting time of discrete quantum walks on a hypercube (HC) is numerically known to be exponentially shorter than that of their classical analogs in terms of the scaling with the HC dimension. Recent numerical analyses illustrated that this scaling exists not only on the bare HC, but also when the HC graph is symmetrically and locally embedded into larger graphs. The present work investigates the necessity of symmetry and locality for the speed-up by considering embeddings that are nonsymmetric or nonlocal. We provide numerical evidence that the exponential speed-up survives also in these cases. Furthermore, our numerical simulations demonstrate that removing a single edge from the HC also does not destroy the exponential speed-up. In the nonlocal embedding of the HC we encounter dark states, which we analyze. We provide a general and detailed presentation of the mapping that reduces the exponentially large Hilbert space of the quantum walk to an effective subspace of polynomial scaling. This mapping is our essential tool to numerically study quantum walks in such high-dimensional structures.

quant-ph↗

arXiv2016

Meta-learning within Projective Simulation

Learning models of artificial intelligence can nowadays perform very well on a large variety of tasks. However, in practice different task environments are best handled by different learning models, rather than a single, universal, approach. Most non-trivial models thus require the adjustment of several to many learning parameters, which is often done on a case-by-case basis by an external party. Meta-learning refers to the ability of an agent to autonomously and dynamically adjust its own learning parameters, or meta-parameters. In this work we show how projective simulation, a recently developed model of artificial intelligence, can naturally be extended to account for meta-learning in reinforcement learning settings. The projective simulation approach is based on a random walk process over a network of clips. The suggested meta-learning scheme builds upon the same design and employs clip networks to monitor the agent's performance and to adjust its meta-parameters "on the fly". We distinguish between "reflexive adaptation" and "adaptation through learning", and show the utility of both approaches. In addition, a trade-off between flexibility and learning-time is addressed. The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.

cs.AI↗

arXiv2014

Projective simulation for classical learning agents: a comprehensive investigation

We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced [H.J. Briegel and G. De las Cuevas. Sci. Rep. 2, 400, (2012)]. Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its learning times and the way both properties scale with the problems' dimension. In addition, we situate the PS agent in different learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model's flexibility. Furthermore, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classifier systems, two popular models in the field of reinforcement learning. It is shown that PS is a competitive artificial intelligence model of unique properties and strengths.

nlin.AO↗

arXiv2014

Quantum speedup for active learning agents

Can quantum mechanics help us in building intelligent robots and agents? One of the defining characteristics of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in any real-life situation is the size and complexity of the corresponding task environment. Owing to, e.g., a large space of possible strategies, learning is typically slow. Even for a moderate task environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here we show that quantum physics can help and provide a significant speed-up for active learning as a genuine problem of artificial intelligence. We introduce a large class of quantum learning agents for which we show a quadratic boost in their active learning efficiency over their classical analogues. This result will be particularly relevant for applications involving complex task environments.

quant-ph↗

arXiv2014

Entanglement of $π$-LME states and the SAT problem

In this paper we investigate the entanglement properties of the class of $π$-locally maximally entanglable ($π$-LME) states, which are also known as the "real equally weighted states" or the "hypergraph states". The $π$-LME states comprise well-studied classes of quantum states (e.g. graph states) and exhibit a large degree of symmetry. Motivated by the structure of LME states, we show that the capacity to (efficiently) determine if a $π$-LME state is entangled would imply an efficient solution to the boolean satisfiability (SAT) problem. More concretely, we show that this particular problem of entanglement detection, phrased as a decision problem, is $\mathsf{NP}$-complete. The restricted setting we consider yields a technically uninvolved proof, and illustrates that entanglement detection, even when quantum states under consideration are highly restricted, still remains difficult.

quant-ph↗

arXiv2014

A self-interaction-free local hybrid functional: Accurate binding energies vis-à-vis accurate ionization potentials from Kohn-Sham eigenvalues

We present and test a new approximation for the exchange-correlation (xc) energy of Kohn-Sham density functional theory. It combines exact exchange with a compatible non-local correlation functional. The functional is by construction free of one-electron self-interaction, respects constraints derived from uniform coordinate scaling, and has the correct asymptotic behavior of the xc energy density. It contains one parameter that is not determined ab initio. We investigate whether it is possible to construct a functional that yields accurate binding energies and affords other advantages, specifically Kohn-Sham eigenvalues that reliably reflect ionization potentials. Tests for a set of atoms and small molecules show that within our local-hybrid form accurate binding energies can be achieved by proper optimization of the free parameter in our functional, along with an improvement in dissociation energy curves and in Kohn-Sham eigenvalues. However, the correspondence of the latter to experimental ionization potentials is not yet satisfactory, and if we choose to optimize their prediction, a rather different value of the functional's parameter is obtained. We put this finding in a larger context by discussing similar observations for other functionals and possible directions for further functional development that our findings suggest.

physics.chem-ph↗

arXiv2014

Projective simulation applied to the grid-world and the mountain-car problem

We study the model of projective simulation (PS) which is a novel approach to artificial intelligence (AI). Recently it was shown that the PS agent performs well in a number of simple task environments, also when compared to standard models of reinforcement learning (RL). In this paper we study the performance of the PS agent further in more complicated scenarios. To that end we chose two well-studied benchmarking problems, namely the "grid-world" and the "mountain-car" problem, which challenge the model with large and continuous input space. We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.

cs.AI↗

arXiv2013

Quantum walks on embedded hypercubes

It has been proved by Kempe that discrete quantum walks on the hypercube (HC) hit exponentially faster than the classical analog. The same was also observed numerically by Krovi and Brun for a slightly different property, namely, the expected hitting time. Yet, to what extent this striking result survives in more general graphs, is to date an open question. Here we tackle this question by studying the expected hitting time for quantum walks on HCs that are embedded into larger symmetric structures. By performing numerical simulations of the discrete quantum walk and deriving a general expression for the classical hitting time, we observe an exponentially increasing gap between the expected classical and quantum hitting times, not only for walks on the bare HC, but also for a large family of embedded HCs. This suggests that the quantum speedup is stable with respect to such embeddings.

quant-ph↗