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Jernej Rudi Finžgar

Publications and source records attributed to Jernej Rudi Finžgar.

11 recordsLinked to original sources

Fault-tolerant cost of shallow QAOA on near-symmetric optimization problems

Montanaro and Zhou proved that depth-one Quantum Approximate Optimization Algorithm (QAOA) finds the planted solution of certain near-symmetric constraint satisfaction problems with probability $Ω(1)$, and observed exponential runtimes for general-purpose classical solvers on explicit realizations, providing strong evidence of an empirical exponential speedup of low-depth QAOA. We ask what such a circuit costs fault-tolerantly. Although the cost Hamiltonian carries $Θ(n^\ell)$ clauses, the rotation angle at which QAOA succeeds shrinks as $n^{1-\ell}$, and we show that small-angle Clifford$+T$ rotation synthesis reduces the non-Clifford cost per circuit to $\widetilde O(n^2)$ for every clause locality $\ell$. Compiling the circuit, however, requires the explicit clause list. We find that the mechanism underlying constant QAOA success fixes the degree-one Fourier coefficients of the cost, whose signs reveal the planted solution. However, this leakage need not reveal the solution: we design families in which the quadratic non-Clifford scaling and hard exact optimization coexist.

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Quantum-informed surrogate sampling for combinatorial optimization

We introduce Quantum-Informed Surrogate Sampling (QISS), a post-processing framework that generates candidate solutions to combinatorial optimization problems from low-weight correlations of shallow quantum circuits. The quantum device estimates local observables, which are directly accessible by repeated measurements and for which a wide range of error-mitigation tools are available, while candidate solutions are generated classically without explicit dependence on the combinatorial optimization problem itself. We evaluate QISS on Maximum Cut and Maximum Independent Set problems on $N$ variables and show that only $O(N)$ low-order correlators from shallow circuits suffice to produce competitive solutions that surpass vanilla QAOA. For MaxCut on 3-regular graphs, QISS from $p=3$ QAOA correlators outperforms vanilla QAOA at $p=17$ on average, with further improvements possible by warm-starting QAOA. We validate the procedure on the 54-qubit IQM Emerald quantum device and demonstrate its noise resilience. Our results support a regime for near-term optimization in which shallow circuits serve not as direct samplers but as generators of informative statistics for scalable classical sampling.

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A scalable quantum-enhanced greedy algorithm for maximum independent set problems

We investigate a hybrid quantum-classical algorithm for solving the Maximum Independent Set (MIS) problem on regular graphs, combining the Quantum Approximate Optimization Algorithm (QAOA) with a minimal degree classical greedy algorithm. The method leverages pre-computed QAOA angles, derived from depth-$p$ QAOA circuits on regular trees, to compute local expectation values and inform sequential greedy decisions that progressively build an independent set. This hybrid approach maintains shallow quantum circuit and avoids instance-specific parameter training, making it well-suited for implementation on current quantum hardware: we have implemented the algorithm on a 20 qubit IQM superconducting device to find independent sets in graphs with thousands of nodes. We perform tensor network simulations to evaluate the performance of the algorithm beyond the reach of current quantum hardware and compare to established classical heuristics. Our results show that even at low depth ($p=4$), the quantum-enhanced greedy method significantly outperforms purely classical greedy baselines as well as more sophisticated approximation algorithms. The modular structure of the algorithm and relatively low quantum resource requirements make it a compelling candidate for scalable, hybrid optimization in the NISQ era and beyond.

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Counterdiabatic Driving with Performance Guarantees

Counterdiabatic (CD) driving has the potential to speed up adiabatic quantum state preparation by suppressing unwanted excitations. However, existing approaches either require intractable classical computations or are based on approximations which do not have performance guarantees. We propose and analyze a non-variational, system-agnostic CD expansion method and analytically show that it converges exponentially quickly in the expansion order. In finite systems, the required resources scale inversely with the spectral gap, which we argue is asymptotically optimal. To extend our method to the thermodynamic limit and suppress errors stemming from high-frequency transitions, we leverage finite-time adiabatic protocols. In particular, we show that a time determined by the quantum speed limit is sufficient to prepare the desired ground state, without the need to optimize the adiabatic trajectory. Numerical tests of our method on the quantum Ising chain show that our method can outperform state-of-the-art variational CD approaches.

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Quantum-Guided Cluster Algorithms for Combinatorial Optimization

Finding the ground state of Ising spin glasses is notoriously difficult due to disorder and frustration. Often, this challenge is framed as a combinatorial optimization problem, for which a common strategy employs simulated annealing, a Monte Carlo (MC)-based algorithm that updates spins one at a time. Yet, these localized updates can cause the system to become trapped in local minima. Cluster algorithms (CAs) were developed to address this limitation and have demonstrated considerable success in studying ferromagnetic systems; however, they tend to encounter percolation issues when applied to generic spin glasses. In this work, we introduce a novel CA designed to tackle these challenges by leveraging precomputed two-point correlations, aiming solve combinatorial optimization problems in the form of Max-Cut more efficiently. In our approach, clusters are formed probabilistically based on these correlations. Various classical and quantum algorithms can be employed to generate correlations that embody information about the energy landscape of the problem. By utilizing this information, the algorithm aims to identify groups of spins whose simultaneous flipping induces large transitions in configuration space with high acceptance probability -- even at low energy levels -- thereby escaping local minima more effectively. Notably, clusters generated using correlations from the Quantum Approximate Optimization Algorithm exhibit high acceptance rates at low temperatures. These acceptance rates often increase with circuit depth, accelerating the algorithm and enabling more efficient exploration of the solution space.

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Quantum Computing for Automotive Applications

Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We explore recent advances in quantum optimization, machine learning, and numerical and chemistry simulations, highlighting their potential and limitations. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications. While quantum algorithms show potential in many application domains, current noisy intermediate-scale quantum hardware limits scale and, thus, business benefits. In the long term, fault-tolerant systems promise theoretical speedups; however, they also require further progress in hardware and software (e.g., related to error correction and data loading). We expect that with this progress, significant practical benefits will emerge eventually.

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Quantum Dissipative Search via Lindbladians

Closed quantum systems follow a unitary time evolution that can be simulated on quantum computers. By incorporating non-unitary effects via, e.g., measurements on ancilla qubits, these algorithms can be extended to open-system dynamics, such as Markovian processes described by the Lindblad master equation. In this paper, we analyze the convergence criteria and speed of a Markovian, purely dissipative quantum random walk on an unstructured classical search space. Notably, we show that certain jump operators make the quantum process replicate a classical one, while others yield differences between open quantum (OQRW) and classical random walks. We also clarify a previously observed quadratic speedup, demonstrating that OQRWs are no more efficient than classical search. Finally, we analyze a dissipative discrete-time ground-state preparation algorithm with a lower implementation cost. This allows us to interpolate between the dissipative and the unitary domain and thereby illustrate the important role of coherence for the quadratic speedup.

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Quantum and classical correlations in shrinking algorithms for optimization

Understanding the benefits of quantum computing for solving combinatorial optimization problems (COPs) remains an open research question. In this work, we extend and analyze algorithms that solve COPs by recursively shrinking them. The algorithms leverage correlations between variables extracted from quantum or classical subroutines to recursively simplify the problem. We compare the performance of the algorithms equipped with correlations from the quantum approximate optimization algorithm (QAOA) as well as the classical linear programming (LP) and semi-definite programming (SDP) relaxations. This allows us to benchmark the utility of QAOA correlations against established classical relaxation algorithms. We apply the recursive algorithm to MaxCut problem instances with up to a hundred vertices at different graph densities. Our results indicate that LP outperforms all other approaches for low-density instances, while SDP excels for high-density problems. Moreover, the shrinking algorithm proves to be a viable alternative to established methods of rounding LP and SDP relaxations. In addition, the recursive shrinking algorithm outperforms its bare counterparts for all three types of correlations, i.e., LP with spanning tree rounding, the Goemans-Williamson algorithm, and conventional QAOA. While the lowest depth QAOA consistently yields worse results than the SDP, our tensor network experiments show that the performance increases significantly for deeper QAOA circuits.

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Quantum-Informed Recursive Optimization Algorithms

We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps that recursively simplify the problem. These reduction steps address the limitations of the quantum component and ensure solution feasibility in constrained optimization problems. Additionally, we use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware. We demonstrate the capabilities of our approach by informing QIRO with correlations from classical simulations of shallow (depth $p=1$) circuits of the quantum approximate optimization algorithm (QAOA), solving instances of maximum independent set and maximum satisfiability problems with hundreds of variables. We also demonstrate how QIRO can be deployed on a neutral atom quantum processor available online on Amazon Braket to find large independent sets of graphs. In summary, our scheme achieves results comparable to classical heuristics, such as simulated annealing and greedy algorithms, even with relatively weak quantum resources. Furthermore, enhancing the quality of these quantum resources improves the performance of the algorithms, highlighting the potential of QIRO. Notably, the modular nature of QIRO offers various avenues for modifications, positioning our work as a blueprint for designing a broader class of hybrid quantum-classical algorithms for combinatorial optimization.

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Designing Quantum Annealing Schedules using Bayesian Optimization

We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with results for two paradigmatic spin models. We find that Bayesian optimization is able to identify schedules resulting in fidelities several orders of magnitude better than standard protocols for both quantum and reverse annealing, as applied to the $p$-spin model. We also show that our scheme can help improve the design of hybrid quantum algorithms for hard combinatorial optimization problems, such as the maximum independent set problem, and illustrate these results via experiments on a neutral atom quantum processor available on Amazon Braket.

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QUARK: A Framework for Quantum Computing Application Benchmarking

Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions increases. While different low-level benchmarks for QC exist, these benchmarks do not provide sufficient insights into real-world application-level performance. We propose an application-centric benchmark method and the QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. This paper establishes three significant contributions: (1) it makes a case for application-level benchmarks and provides an in-depth "pen and paper" benchmark formulation of two reference problems: robot path and vehicle option optimization from the industrial domain; (2) it proposes the open-source QUARK framework for designing, implementing, executing, and analyzing benchmarks; (3) it provides multiple reference implementations for these two reference problems based on different known, and where needed, extended, classical and quantum algorithmic approaches and analyzes their performance on different types of infrastructures.

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