Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State

arXiv:2608.24493 · quant-ph, cs.CC, cs.DS · Submitted 2026-08-25 · Read on arXiv

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Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Kai: Today's paper: "Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State".

Mira: This paper establishes an optimal lower bound for estimating the ground-state energy of a Hamiltonian when access to a guiding state with sufficient overlap is provided.

Kai: First, who's behind it and why it matters.

Title and authors: Kai: So, let's look at the title and the authors of this paper, "Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State." It immediately tells us the central focus is on finding the best possible minimum number of unitary applications needed for this specific problem.

Mira: The authors are Rolando D. Somma and Ronald de Wolf, and their work sits right at the intersection of quantum complexity theory and practical simulation problems in quantum chemistry.

Lev: It's interesting to see a paper focused purely on the lower bound aspect; from an error correction perspective, knowing what is fundamentally impossible without more queries is as important as knowing what is possible with a certain number of queries.

Kai: And this paper seems to be really pushing the boundaries by establishing a matching lower bound against existing upper bounds, which suggests they've done a thorough job of characterizing the complexity landscape for this guided Hamiltonian problem.

Mira: The implications here are that we now have a tight theoretical target; any algorithm we design for ground-state energy estimation needs to meet or exceed these requirements to be considered optimal in terms of query complexity.

Lev: Knowing these necessary conditions helps us prioritize which algorithmic approaches are actually worth exploring on real hardware, because we can immediately filter out the ones that are guaranteed to be inefficient.

Kai: It’s about defining the minimum cost for a certain level of accuracy, which is a very concrete metric for experimentalists trying to optimize their measurement sequences.

Mira: And the authors are setting up this framework by clearly defining what we mean by additive error delta, success probability epsilon, and the overlap parameter gamma.

Lev: That explicit definition is crucial because it allows researchers like us to translate abstract complexity bounds into tangible constraints on the noise and fidelity of a physical quantum computer.

Kai: So, in short, this paper is about setting the fundamental theoretical requirement for running any guided ground-state estimation algorithm.

Mira: It sets up a strong foundation because it connects these complex parameters—error probability, overlap, and spectral gap—to the necessary number of unitary operations needed.

The paper's summary: Kai: So, to summarize what the paper actually does in "Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State," it establishes a concrete lower bound on the number of U and U minus one applications required to estimate the ground-state energy.

Mira: Essentially, they tackle the guided Hamiltonian problem by defining exactly how many times we need to apply the unitary operator to get an additive error delta while maintaining a success probability of at least one-epsilon.

Lev: The paper uses analytic tools like Coppersmith-Rivlin and extremal properties of Chebyshev polynomials to prove this lower bound by analyzing hard families of instances.

Kai: They do this by looking at polynomial methods applied to these instances, which is a powerful mathematical technique for showing that solving the problem with T queries leads to an approximation in terms of T delta queries.

Mira: The key result they prove is the joint lower bound ((one/epsilon)/gamma delta) applications, provided the dimension of H is at least (one/epsilon)/gamma squared.

Lev: That condition on the dimension being large enough, N at least (one/epsilon)/gamma squared, is a major detail because it defines the regime where this specific lower bound holds true for their proof.

Kai: They also showed that this same lower bound applies when the ground state is unique and H has a gap of delta between its first and second eigenvalue, or in cases involving ground-state preparation where delta is defined as the spectral gap.

Mira: This breadth means the result isn't just for one specific type of Hamiltonian but applies across different structures, which makes it much more generally useful in quantum chemistry simulations.

Lev: When we think about running this on hardware, this generalized applicability suggests that we can use these theoretical bounds to plan our experiments across a wider variety of molecular systems.

Kai: So the overall summary is that they've proven the required number of queries is proportional to (one/epsilon)/gamma delta under sufficient dimensional conditions.

Mira: It really boils down to quantifying the trade-off: you can reduce error probability epsilon by increasing queries logarithmically, but this cost scales inversely with the overlap and spectral gap.

The paper's improvements: Kai: Now, regarding the improvements discussed in "Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State," they highlight that they have improved previous upper bounds to O((one/epsilon)/gamma delta) very recently.

Mira: That improvement is significant because it means the upper bound is now matching the lower bound, confirming that the established complexity ((one/epsilon)/gamma delta) is indeed achievable.

Lev: Achieving a matching lower and upper bound simultaneously for these parameters provides strong evidence that this specific scaling of queries is asymptotically optimal under the conditions they set.

Kai: The paper also extends this result to other models, such as block-encoding models, showing the lower bound applies to the number of uses of V and V minus one in those settings.

Mira: Furthermore, there's a discussion about improved upper bounds when H is non-negative and presented as a sum of squares, which implies a tighter lower bound might exist for those specific mathematical representations.

Lev: That finding about the sum-of-squares formulation leading to ((one/epsilon)/gamma sqrt delta) is very exciting because it suggests we could achieve better precision if our Hamiltonian problems can be mapped into that form.

Kai: So, the main improvement they highlight isn't just the scaling itself, but showing that this lower bound holds across different structural representations of H and providing tighter bounds for specific mathematical forms.

Mira: It points toward a future where we might not only need to focus on simple overlap gamma but also on exploiting other mathematical properties of the Hamiltonian for better estimation efficiency.

Lev: If we can exploit those sum-of-squares representations effectively in a real quantum simulation, it could translate directly into faster convergence or higher precision without needing an exponentially larger number of queries.

Conclusion: Kai: So, wrapping up the discussion on "Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State," the authors have confirmed that ((one/epsilon)/gamma delta) applications of U and its inverse U minus one are necessary and sufficient under the specified conditions.

Mira: The implication is that this gives us a definitive measure of the resource cost for ground-state estimation, showing exactly how error tolerance, overlap, and spectral gap influence the required unitary queries.

Lev: For our work in quantum error correction, it provides a clear roadmap: when designing an algorithm for a system with these constraints, we know precisely the minimum number of steps that will be needed to achieve reliable results.

Kai: It’s encouraging because this theoretical framework helps bridge the gap between abstract complexity theory and what we can actually build and measure in our quantum hardware experiments.

Mira: We should keep paying attention to their notes about the guiding states being effective even when overlap is just a minimum, which suggests that practical implementation might need to be more flexible than just relying on a high gamma.

Lev: If we can develop better methods for generating those guides, the entire landscape of accessible guided simulations will shift in a very positive direction.

Kai: It's been really illuminating to see how this paper solidifies the theoretical requirements for estimating ground-state energies within these constraints.

Mira: Indeed, it sets a firm benchmark that we can use when comparing different proposed algorithms for quantum chemistry problems with similar error tolerances.

quant-ph, cs.CC, cs.DS

Submitted: 2026-08-25

Updated: 2026-09-30

Comments: v2: some small changes and a few extra references in the introduction

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 89/100

The gist: This paper establishes an optimal lower bound for estimating the ground-state energy of a Hamiltonian when access to a guiding state with sufficient overlap is provided.

Key concepts

Guided Hamiltonian Problem
This is the task of estimating a quantum system's lowest energy (ground state) when you have access to a special 'guiding state.' This guiding state must overlap significantly with the true ground space of the system, allowing for an efficient estimation strategy.
Time Evolution Operator ($U$ and $U^{-1}$)
The time evolution operator represents how a quantum system changes over time. The paper analyzes how many times you need to apply this operator and its inverse to gather enough information about the ground-state energy within specified error limits.
Lower Bound Proof Strategy
The proof uses hard families of problems and polynomial methods to show that any algorithm must perform at least a certain number of operations. It involves analyzing different scenarios, like when the ground state is unique versus when it is not.

Terminology

Summary

This paper establishes an optimal lower bound for estimating the ground-state energy of a Hamiltonian when access to a guiding state with sufficient overlap is provided. This result is significant because it determines the minimum number of applications of the time evolution operator and its inverse necessary to estimate this energy within additive error, success probability, and spectral gap constraints. The derived lower bound matches recent upper bounds, suggesting optimality in all three key parameters: approximation error, overlap parameter, and spectral gap.

Problem Formulation

The guided Hamiltonian problem seeks to estimate the ground-state energy of a Hamiltonian H given access to the unitary U = e iH and a guiding state prepared by a unitary A that has an overlap of at least γ with the ground space of H. The goal is to determine How many applications of U and its inverse U−1 are necessary and sufficient to estimate the ground-state energy within additive error δ, with success probability at least 1−ε. This problem is central to quantum chemistry, where estimating the smallest eigenvalue λmin of H (which corresponds to the ground-state energy) is a core task.

Known Upper Bounds and Matching Lower Bounds

The paper reviews existing bounds on the number of applications of U and U−1. An upper bound O(log(1/ε) log(1/γ)/γδ) was known, which was improved to O(log(1/ε)/γδ). The authors prove the joint lower bound omega(log(1/ε)/γδ) with the tight ε-dependence when the dimension of H is at least log(1/ε)/γ2. They also show that this same lower bound holds for special cases, such as when the ground state is guaranteed to be unique and H has a gap of δ between its first and second eigenvalue, or for ground-state preparation where δ denotes the spectral gap.

Lower Bound Proof Strategy

The proof involves analyzing hard families of instances. The authors use the polynomial method to analyze these instances, showing that solving the problem with T queries yields a solution to an O(1)-approximate case with T δ queries, which can then be lower bounded using analytic tools like Coppersmith-Rivlin and extremal properties of Chebyshev polynomials. For a specific hard family where H is diagonal, the proof involves distinguishing between two cases: Case 1, where the guiding state overlap is high but the ground state is not unique (leading to T = omega(1/δ√N)), and Case 2, where a unique ground state exists (leading to T = omega(log(1/ε)/γδ)). The overall lower bound is determined by choosing N = log(1/ε)2/γ2, which makes the two bounds equal.

Key Results and Extensions

The main result proves the joint lower bound: omega(log(1/ε)/γδ) applications of U and U−1 are necessary (and, thanks to [JW26], also sufficient). This bound applies when the dimension is sufficiently large, specifically N ≥ log(1/ε)/γ2. Furthermore, the results extend to other models:

  1. The lower bounds apply when H can be accessed via its block-encoding model. In this setting, the lower bound extends to the number of uses of V and V−1 in the block-encoding model.

  2. Improved upper bounds are known when H is nonnegative and presented as a sum of squares; in this case, the authors imply a lower bound omega(log(1/ε)/γ√δ).

Discussion on Guiding State Requirements

The paper notes a surprising finding: the hard families for both lower bounds involve guiding states that are effectively independent of the ground state (and of its energy), despite having the required ≥ γ overlap with the ground state or ground space. This suggests that a simple overlap requirement may not be optimal, hinting that future work should explore alternative requirements for a more useful guiding state. The derived bound includes all relevant asymptotic parameters: γ, δ, and ε.

References

[BBC+01] Robert Beals, Harry Buhrman, Richard Cleve, Michele Mosca, and Ronald de Wolf. Quantum lower bounds by polynomials. Journal of the ACM, 48(4):778–797, 2001.

[BCWZ99] Harry Buhrman, Richard Cleve, Ronald de Wolf, and Christof Zalka. Bounds for small error and zero-error quantum algorithms. In Proceedings of 40th IEEE FOCS, pages 358–368, 1999.

[JW26] Stacey Jeffery and Freek Witteveen. Optimal quantum algorithm for ground-state energy estimation with a guiding state, 2026. To appear. arXiv:??

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed the provided scientific paper, Optimal Lower Bound for Ground-State Energy Estimation with a Guiding State. This paper establishes a fundamental lower bound on the quantum query complexity required to estimate the ground-state energy of a Hamiltonian when aided by an approximate guiding state.

Based on this research, here are specific improvements that can be made to AI systems, categorized by the capability they would gain:

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  1. Improving Quantum Chemistry and Materials Science Simulations (Ground-State Energy Estimation):

The primary improvement is the ability to perform more efficient quantum chemistry simulations, specifically for finding ground-state energies of complex molecular systems described by Hamiltonians.

  • Improvement: Implement quantum algorithms that leverage guided local Hamiltonian problems (as discussed in Section 1.2 and Theorem 3.1).

  • Specific Capability: The AI system can estimate the ground-state energy of a molecule described by a complex Hamiltonian (e.g., electronic structure Hamiltonian) within an additive error of at most ±δ, with a high success probability (related to ε), using the minimum necessary number of unitary applications.

  • Benefit: This allows for the rapid and accurate calculation of molecular properties that are currently computationally intractable for classical computers, such as reaction barriers or binding energies, by designing algorithms that minimize the required quantum resources (unitary queries).

  1. Enhancing Quantum Machine Learning and Optimization Subroutines:

The paper shows that ground-state estimation is a core subroutine in larger quantum algorithms.

  • Improvement: Develop Quantum Subroutines for optimization tasks where the objective function involves finding a minimum eigenvalue of a Hamiltonian.

  • Specific Capability: The AI system can efficiently find the minimum eigenvalue (ground state energy) of an operator, provided it has access to a guiding state with non-negligible overlap (guiding state preparation).

  • Benefit: This speeds up quantum optimization algorithms, such as Variational Quantum Eigensolver (VQE) iterations or quantum phase estimation routines used in machine learning models, by providing a tighter lower bound on the required query complexity for the energy subproblem.

  1. Optimizing Quantum State Preparation and Characterization:

The paper addresses the difficulty of preparing high-fidelity ground states.

  • Improvement: Create algorithms that efficiently prepare or approximate ground states using available resources (Unitary applications U and A).

  • Specific Capability: The AI system can generate a quantum state (guiding state) that is guaranteed to have a minimum overlap γ with the true ground space, even if the exact ground state preparation is hard.

  • Benefit: This makes complex quantum simulations feasible in regimes where perfect ground state preparation is impossible, enabling the use of ansatz states as effective starting points for deeper quantum computations.

  1. Developing Robust Quantum Algorithm Design (Query Complexity Analysis):

The paper provides rigorous lower bounds, which are crucial for algorithm design.

  • Improvement: Integrate rigorous complexity analysis into the design phase of quantum algorithms.

  • Specific Capability: When designing a new quantum algorithm for ground-state estimation, the system can use the derived lower bound, specifically the tight dependency on parameters—the trade-off between error probability ε, spectral gap δ, and guiding state overlap γ—to determine if a proposed algorithm is asymptotically optimal or if it requires an excessive number of unitary applications (U and U−1).

  • Benefit: This prevents wasted computational effort by ensuring that the chosen algorithmic strategy adheres to the theoretical minimum required query complexity.

  1. Leveraging Spectral Amplification Techniques:

The paper discusses using Sum-of-Squares Spectral Amplification (SOSSA) to improve precision.

  • Improvement: Implement hybrid algorithms that combine black-box spectral amplification with guided phase estimation.

  • Specific Capability: The AI system can treat the Hamiltonian as a sum of squares (H = G†G) and use the access to the square root operator G (via block encoding) to perform phase estimation on an amplified version of the energy problem, leading to a lower bound improvement from O(log(1/ε)/γδ) to O(log(1/ε)/γ√δ).

  • Benefit: This allows for achieving higher precision in ground-state energy estimation for a given query budget by utilizing advanced mathematical transformations (SOSSA), which is vital for pushing the limits of current quantum simulation capabilities.

Abstract

Suppose we can apply the unitary U=e i H for some Hamiltonian H, and are given access to a unitary that prepares a guiding state promised to have overlap at least γ>0 with the ground space of H. Our goal is to estimate the ground-state energy of H within additive error δ> 0 and success probability at least 1-epsilon, epsilon>0. How many applications of U and its inverse U-1 are necessary and sufficient? This quantity corresponds to the total Hamiltonian-simulation time needed. An upper bound O((1/epsilon) (1/γ)/γδ) was known, and was improved to O((1/epsilon)/γδ) very recently [JW26]. A matching lower bound was known whenever one of the three parameters δ,γ, epsilon was held constant [MdW26]. In this paper we prove the joint lower bound Ω((1/epsilon)/γδ) with the tight epsilon-dependence provided the dimension of H is at least (1/epsilon)/γ squared. Furthermore, we show that this same lower bound (with slightly larger dimension) holds for both the special case in which the ground state is guaranteed to be unique and H has a gap of δ between its first and second eigenvalue; and for ground-state preparation, where δ denotes the spectral gap and epsilon now is the approximation error. The lower bounds also apply when the Hamiltonian can be accessed via its block-encoding, and when fractional powers of U are allowed, as in continuous-time Hamiltonian simulation. Lastly, improved upper bounds are known when H is nonnegative and presented as a sum of squares; and our results imply the lower bound Ω((1/epsilon)/γ sqrtδ) for this case.

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